# Performance Evaluation Framework

This document provides comprehensive methodologies for evaluating chunking strategy performance and effectiveness.

## Evaluation Metrics

### Core Retrieval Metrics

#### Retrieval Precision
Measures the fraction of retrieved chunks that are relevant to the query.

```python
def calculate_precision(retrieved_chunks: List[Dict], relevant_chunks: List[Dict]) -> float:
    """
    Calculate retrieval precision
    Precision = |Relevant ∩ Retrieved| / |Retrieved|
    """
    retrieved_ids = {chunk.get('id') for chunk in retrieved_chunks}
    relevant_ids = {chunk.get('id') for chunk in relevant_chunks}

    intersection = retrieved_ids & relevant_ids

    if not retrieved_ids:
        return 0.0

    return len(intersection) / len(retrieved_ids)
```

#### Retrieval Recall
Measures the fraction of relevant chunks that are successfully retrieved.

```python
def calculate_recall(retrieved_chunks: List[Dict], relevant_chunks: List[Dict]) -> float:
    """
    Calculate retrieval recall
    Recall = |Relevant ∩ Retrieved| / |Relevant|
    """
    retrieved_ids = {chunk.get('id') for chunk in retrieved_chunks}
    relevant_ids = {chunk.get('id') for chunk in relevant_chunks}

    intersection = retrieved_ids & relevant_ids

    if not relevant_ids:
        return 0.0

    return len(intersection) / len(relevant_ids)
```

#### F1-Score
Harmonic mean of precision and recall.

```python
def calculate_f1_score(precision: float, recall: float) -> float:
    """
    Calculate F1-score
    F1 = 2 * (Precision * Recall) / (Precision + Recall)
    """
    if precision + recall == 0:
        return 0.0

    return 2 * (precision * recall) / (precision + recall)
```

### Mean Reciprocal Rank (MRR)
Measures the rank of the first relevant result.

```python
def calculate_mrr(queries: List[Dict], results: List[List[Dict]]) -> float:
    """
    Calculate Mean Reciprocal Rank
    """
    reciprocal_ranks = []

    for query, query_results in zip(queries, results):
        relevant_found = False

        for rank, result in enumerate(query_results, 1):
            if result.get('is_relevant', False):
                reciprocal_ranks.append(1.0 / rank)
                relevant_found = True
                break

        if not relevant_found:
            reciprocal_ranks.append(0.0)

    return sum(reciprocal_ranks) / len(reciprocal_ranks)
```

### Mean Average Precision (MAP)
Considers both precision and the ranking of relevant documents.

```python
def calculate_average_precision(retrieved_chunks: List[Dict], relevant_chunks: List[Dict]) -> float:
    """
    Calculate Average Precision for a single query
    """
    retrieved_ids = {chunk.get('id') for chunk in retrieved_chunks}
    relevant_ids = {chunk.get('id') for chunk in relevant_chunks}

    if not relevant_ids:
        return 0.0

    precisions = []
    relevant_count = 0

    for rank, chunk in enumerate(retrieved_chunks, 1):
        if chunk.get('id') in relevant_ids:
            relevant_count += 1
            precision_at_rank = relevant_count / rank
            precisions.append(precision_at_rank)

    return sum(precisions) / len(relevant_ids) if relevant_ids else 0.0

def calculate_map(queries: List[Dict], results: List[List[Dict]]) -> float:
    """
    Calculate Mean Average Precision across multiple queries
    """
    average_precisions = []

    for query, query_results in zip(queries, results):
        ap = calculate_average_precision(query_results, query.get('relevant_chunks', []))
        average_precisions.append(ap)

    return sum(average_precisions) / len(average_precisions) if average_precisions else 0.0
```

### Normalized Discounted Cumulative Gain (NDCG)
Measures ranking quality with emphasis on highly relevant results.

```python
def calculate_dcg(retrieved_chunks: List[Dict]) -> float:
    """
    Calculate Discounted Cumulative Gain
    """
    dcg = 0.0

    for rank, chunk in enumerate(retrieved_chunks, 1):
        relevance = chunk.get('relevance_score', 0)
        dcg += relevance / np.log2(rank + 1)

    return dcg

def calculate_ndcg(retrieved_chunks: List[Dict], ideal_chunks: List[Dict]) -> float:
    """
    Calculate Normalized Discounted Cumulative Gain
    """
    dcg = calculate_dcg(retrieved_chunks)
    idcg = calculate_dcg(ideal_chunks)

    if idcg == 0:
        return 0.0

    return dcg / idcg
```

## End-to-End RAG Evaluation

### Answer Quality Metrics

#### Factual Consistency
Measures how well the generated answer aligns with retrieved chunks.

```python
import spacy
from transformers import pipeline

class FactualConsistencyEvaluator:
    def __init__(self):
        self.nlp = spacy.load("en_core_web_sm")
        self.nli_pipeline = pipeline("text-classification",
                                   model="roberta-large-mnli")

    def evaluate_consistency(self, answer: str, retrieved_chunks: List[str]) -> float:
        """
        Evaluate factual consistency between answer and retrieved context
        """
        if not retrieved_chunks:
            return 0.0

        # Combine retrieved chunks as context
        context = " ".join(retrieved_chunks[:3])  # Use top 3 chunks

        # Use Natural Language Inference to check consistency
        result = self.nli_pipeline(f"premise: {context} hypothesis: {answer}")

        # Extract consistency score (entailment probability)
        for item in result:
            if item['label'] == 'ENTAILMENT':
                return item['score']
            elif item['label'] == 'CONTRADICTION':
                return 1.0 - item['score']

        return 0.5  # Neutral if NLI is inconclusive
```

#### Answer Completeness
Measures how completely the answer addresses the user's query.

```python
def evaluate_completeness(answer: str, query: str, reference_answer: str = None) -> float:
    """
    Evaluate answer completeness
    """
    # Extract key entities from query
    query_entities = extract_entities(query)
    answer_entities = extract_entities(answer)

    # Calculate entity coverage
    if not query_entities:
        return 0.5  # Neutral if no entities in query

    covered_entities = query_entities & answer_entities
    entity_coverage = len(covered_entities) / len(query_entities)

    # If reference answer is available, compare against it
    if reference_answer:
        reference_entities = extract_entities(reference_answer)
        answer_reference_overlap = len(answer_entities & reference_entities) / max(len(reference_entities), 1)
        return (entity_coverage + answer_reference_overlap) / 2

    return entity_coverage

def extract_entities(text: str) -> set:
    """
    Extract named entities from text (simplified)
    """
    # This would use a proper NER model in practice
    import re

    # Simple noun phrase extraction as placeholder
    noun_phrases = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', text)
    return set(noun_phrases)
```

#### Response Relevance
Measures how relevant the answer is to the original query.

```python
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

class RelevanceEvaluator:
    def __init__(self, model_name="all-MiniLM-L6-v2"):
        self.model = SentenceTransformer(model_name)

    def evaluate_relevance(self, query: str, answer: str) -> float:
        """
        Evaluate semantic relevance between query and answer
        """
        # Generate embeddings
        query_embedding = self.model.encode([query])
        answer_embedding = self.model.encode([answer])

        # Calculate cosine similarity
        similarity = cosine_similarity(query_embedding, answer_embedding)[0][0]

        return float(similarity)
```

## Performance Metrics

### Processing Time

```python
import time
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class PerformanceMetrics:
    total_time: float
    chunking_time: float
    embedding_time: float
    search_time: float
    generation_time: float
    throughput: float  # documents per second

class PerformanceProfiler:
    def __init__(self):
        self.timings = {}
        self.start_times = {}

    def start_timer(self, operation: str):
        self.start_times[operation] = time.time()

    def end_timer(self, operation: str):
        if operation in self.start_times:
            duration = time.time() - self.start_times[operation]
            if operation not in self.timings:
                self.timings[operation] = []
            self.timings[operation].append(duration)
            return duration
        return 0.0

    def get_performance_metrics(self, document_count: int) -> PerformanceMetrics:
        total_time = sum(sum(times) for times in self.timings.values())

        return PerformanceMetrics(
            total_time=total_time,
            chunking_time=sum(self.timings.get('chunking', [0])),
            embedding_time=sum(self.timings.get('embedding', [0])),
            search_time=sum(self.timings.get('search', [0])),
            generation_time=sum(self.timings.get('generation', [0])),
            throughput=document_count / total_time if total_time > 0 else 0
        )
```

### Memory Usage

```python
import psutil
import os
from typing import Dict, List

class MemoryProfiler:
    def __init__(self):
        self.process = psutil.Process(os.getpid())
        self.memory_snapshots = []

    def take_memory_snapshot(self, label: str):
        """Take a snapshot of current memory usage"""
        memory_info = self.process.memory_info()
        memory_mb = memory_info.rss / 1024 / 1024  # Convert to MB

        self.memory_snapshots.append({
            'label': label,
            'memory_mb': memory_mb,
            'timestamp': time.time()
        })

    def get_peak_memory_usage(self) -> float:
        """Get peak memory usage in MB"""
        if not self.memory_snapshots:
            return 0.0
        return max(snapshot['memory_mb'] for snapshot in self.memory_snapshots)

    def get_memory_usage_by_operation(self) -> Dict[str, float]:
        """Get memory usage breakdown by operation"""
        if not self.memory_snapshots:
            return {}

        memory_by_op = {}
        for i in range(1, len(self.memory_snapshots)):
            prev_snapshot = self.memory_snapshots[i-1]
            curr_snapshot = self.memory_snapshots[i]

            operation = curr_snapshot['label']
            memory_delta = curr_snapshot['memory_mb'] - prev_snapshot['memory_mb']

            if operation not in memory_by_op:
                memory_by_op[operation] = []
            memory_by_op[operation].append(memory_delta)

        return {op: sum(deltas) for op, deltas in memory_by_op.items()}
```

## Evaluation Datasets

### Standardized Test Sets

#### Question-Answer Pairs

```python
from dataclasses import dataclass
from typing import List, Optional
import json

@dataclass
class EvaluationQuery:
    id: str
    question: str
    reference_answer: Optional[str]
    relevant_chunk_ids: List[str]
    query_type: str  # factoid, analytical, comparative
    difficulty: str  # easy, medium, hard
    domain: str  # finance, medical, legal, technical

class EvaluationDataset:
    def __init__(self, name: str):
        self.name = name
        self.queries: List[EvaluationQuery] = []
        self.documents: Dict[str, str] = {}
        self.chunks: Dict[str, Dict] = {}

    def add_query(self, query: EvaluationQuery):
        self.queries.append(query)

    def add_document(self, doc_id: str, content: str):
        self.documents[doc_id] = content

    def add_chunk(self, chunk_id: str, content: str, doc_id: str, metadata: Dict):
        self.chunks[chunk_id] = {
            'id': chunk_id,
            'content': content,
            'doc_id': doc_id,
            'metadata': metadata
        }

    def save_to_file(self, filepath: str):
        data = {
            'name': self.name,
            'queries': [
                {
                    'id': q.id,
                    'question': q.question,
                    'reference_answer': q.reference_answer,
                    'relevant_chunk_ids': q.relevant_chunk_ids,
                    'query_type': q.query_type,
                    'difficulty': q.difficulty,
                    'domain': q.domain
                }
                for q in self.queries
            ],
            'documents': self.documents,
            'chunks': self.chunks
        }

        with open(filepath, 'w') as f:
            json.dump(data, f, indent=2)

    @classmethod
    def load_from_file(cls, filepath: str):
        with open(filepath, 'r') as f:
            data = json.load(f)

        dataset = cls(data['name'])
        dataset.documents = data['documents']
        dataset.chunks = data['chunks']

        for q_data in data['queries']:
            query = EvaluationQuery(
                id=q_data['id'],
                question=q_data['question'],
                reference_answer=q_data.get('reference_answer'),
                relevant_chunk_ids=q_data['relevant_chunk_ids'],
                query_type=q_data['query_type'],
                difficulty=q_data['difficulty'],
                domain=q_data['domain']
            )
            dataset.add_query(query)

        return dataset
```

### Dataset Generation

#### Synthetic Query Generation

```python
import random
from typing import List, Dict

class SyntheticQueryGenerator:
    def __init__(self):
        self.query_templates = {
            'factoid': [
                "What is {concept}?",
                "When did {event} occur?",
                "Who developed {technology}?",
                "How many {items} are mentioned?",
                "What is the value of {metric}?"
            ],
            'analytical': [
                "Compare and contrast {concept1} and {concept2}.",
                "Analyze the impact of {concept} on {domain}.",
                "What are the advantages and disadvantages of {technology}?",
                "Explain the relationship between {concept1} and {concept2}.",
                "Evaluate the effectiveness of {approach} for {problem}."
            ],
            'comparative': [
                "Which is better: {option1} or {option2}?",
                "How does {method1} differ from {method2}?",
                "Compare the performance of {system1} and {system2}.",
                "What are the key differences between {approach1} and {approach2}?"
            ]
        }

    def generate_queries_from_chunks(self, chunks: List[Dict], num_queries: int = 100) -> List[EvaluationQuery]:
        """Generate synthetic queries from document chunks"""
        queries = []

        # Extract entities and concepts from chunks
        entities = self._extract_entities_from_chunks(chunks)

        for i in range(num_queries):
            query_type = random.choice(['factoid', 'analytical', 'comparative'])
            template = random.choice(self.query_templates[query_type])

            # Fill template with extracted entities
            query_text = self._fill_template(template, entities)

            # Find relevant chunks for this query
            relevant_chunks = self._find_relevant_chunks(query_text, chunks)

            query = EvaluationQuery(
                id=f"synthetic_{i}",
                question=query_text,
                reference_answer=None,  # Would need generation model
                relevant_chunk_ids=[chunk['id'] for chunk in relevant_chunks],
                query_type=query_type,
                difficulty=random.choice(['easy', 'medium', 'hard']),
                domain='synthetic'
            )

            queries.append(query)

        return queries

    def _extract_entities_from_chunks(self, chunks: List[Dict]) -> Dict[str, List[str]]:
        """Extract entities, concepts, and relationships from chunks"""
        # This would use proper NER in practice
        entities = {
            'concepts': [],
            'technologies': [],
            'methods': [],
            'metrics': [],
            'events': []
        }

        for chunk in chunks:
            content = chunk['content']
            # Simplified entity extraction
            words = content.split()
            entities['concepts'].extend([word for word in words if len(word) > 6])
            entities['technologies'].extend([word for word in words if 'technology' in word.lower()])
            entities['methods'].extend([word for word in words if 'method' in word.lower()])
            entities['metrics'].extend([word for word in words if '%' in word or '$' in word])

        # Remove duplicates and limit
        for key in entities:
            entities[key] = list(set(entities[key]))[:50]

        return entities

    def _fill_template(self, template: str, entities: Dict[str, List[str]]) -> str:
        """Fill query template with random entities"""
        import re

        def replace_placeholder(match):
            placeholder = match.group(1)

            # Map placeholders to entity types
            entity_mapping = {
                'concept': 'concepts',
                'concept1': 'concepts',
                'concept2': 'concepts',
                'technology': 'technologies',
                'method': 'methods',
                'method1': 'methods',
                'method2': 'methods',
                'metric': 'metrics',
                'event': 'events',
                'items': 'concepts',
                'option1': 'concepts',
                'option2': 'concepts',
                'approach': 'methods',
                'problem': 'concepts',
                'domain': 'concepts',
                'system1': 'concepts',
                'system2': 'concepts'
            }

            entity_type = entity_mapping.get(placeholder, 'concepts')
            available_entities = entities.get(entity_type, ['something'])

            if available_entities:
                return random.choice(available_entities)
            else:
                return 'something'

        return re.sub(r'\{(\w+)\}', replace_placeholder, template)

    def _find_relevant_chunks(self, query: str, chunks: List[Dict], k: int = 3) -> List[Dict]:
        """Find chunks most relevant to the query"""
        # Simple keyword matching for synthetic generation
        query_words = set(query.lower().split())

        chunk_scores = []
        for chunk in chunks:
            chunk_words = set(chunk['content'].lower().split())
            overlap = len(query_words & chunk_words)
            chunk_scores.append((overlap, chunk))

        # Sort by overlap and return top k
        chunk_scores.sort(key=lambda x: x[0], reverse=True)
        return [chunk for _, chunk in chunk_scores[:k]]
```

## A/B Testing Framework

### Statistical Significance Testing

```python
import numpy as np
from scipy import stats
from typing import List, Dict, Tuple

class ABTestAnalyzer:
    def __init__(self):
        self.significance_level = 0.05

    def compare_metrics(self, control_metrics: List[float],
                       treatment_metrics: List[float],
                       metric_name: str) -> Dict:
        """
        Compare metrics between control and treatment groups
        """
        control_mean = np.mean(control_metrics)
        treatment_mean = np.mean(treatment_metrics)

        control_std = np.std(control_metrics)
        treatment_std = np.std(treatment_metrics)

        # Perform t-test
        t_statistic, p_value = stats.ttest_ind(control_metrics, treatment_metrics)

        # Calculate effect size (Cohen's d)
        pooled_std = np.sqrt(((len(control_metrics) - 1) * control_std**2 +
                             (len(treatment_metrics) - 1) * treatment_std**2) /
                            (len(control_metrics) + len(treatment_metrics) - 2))

        cohens_d = (treatment_mean - control_mean) / pooled_std if pooled_std > 0 else 0

        # Determine significance
        is_significant = p_value < self.significance_level

        return {
            'metric_name': metric_name,
            'control_mean': control_mean,
            'treatment_mean': treatment_mean,
            'absolute_difference': treatment_mean - control_mean,
            'relative_difference': ((treatment_mean - control_mean) / control_mean * 100) if control_mean != 0 else 0,
            'control_std': control_std,
            'treatment_std': treatment_std,
            't_statistic': t_statistic,
            'p_value': p_value,
            'is_significant': is_significant,
            'effect_size': cohens_d,
            'significance_level': self.significance_level
        }

    def analyze_ab_test_results(self,
                               control_results: Dict[str, List[float]],
                               treatment_results: Dict[str, List[float]]) -> Dict:
        """
        Analyze A/B test results across multiple metrics
        """
        analysis_results = {}

        # Ensure both dictionaries have the same keys
        all_metrics = set(control_results.keys()) & set(treatment_results.keys())

        for metric in all_metrics:
            if metric in control_results and metric in treatment_results:
                analysis_results[metric] = self.compare_metrics(
                    control_results[metric],
                    treatment_results[metric],
                    metric
                )

        # Calculate overall summary
        significant_improvements = sum(1 for result in analysis_results.values()
                                     if result['is_significant'] and result['relative_difference'] > 0)
        significant_degradations = sum(1 for result in analysis_results.values()
                                      if result['is_significant'] and result['relative_difference'] < 0)

        analysis_results['summary'] = {
            'total_metrics_compared': len(analysis_results),
            'significant_improvements': significant_improvements,
            'significant_degradations': significant_degradations,
            'no_significant_change': len(analysis_results) - significant_improvements - significant_degradations
        }

        return analysis_results
```

## Automated Evaluation Pipeline

### End-to-End Evaluation

```python
class ChunkingEvaluationPipeline:
    def __init__(self, strategies: Dict[str, Any], dataset: EvaluationDataset):
        self.strategies = strategies
        self.dataset = dataset
        self.results = {}
        self.profiler = PerformanceProfiler()
        self.memory_profiler = MemoryProfiler()

    def run_evaluation(self) -> Dict:
        """Run comprehensive evaluation of all strategies"""
        evaluation_results = {}

        for strategy_name, strategy in self.strategies.items():
            print(f"Evaluating strategy: {strategy_name}")

            # Reset profilers for each strategy
            self.profiler = PerformanceProfiler()
            self.memory_profiler = MemoryProfiler()

            # Evaluate strategy
            strategy_results = self._evaluate_strategy(strategy, strategy_name)
            evaluation_results[strategy_name] = strategy_results

        # Compare strategies
        comparison_results = self._compare_strategies(evaluation_results)

        return {
            'individual_results': evaluation_results,
            'comparison': comparison_results,
            'recommendations': self._generate_recommendations(comparison_results)
        }

    def _evaluate_strategy(self, strategy: Any, strategy_name: str) -> Dict:
        """Evaluate a single chunking strategy"""
        results = {
            'strategy_name': strategy_name,
            'retrieval_metrics': {},
            'quality_metrics': {},
            'performance_metrics': {}
        }

        # Track memory usage
        self.memory_profiler.take_memory_snapshot(f"{strategy_name}_start")

        # Process all documents
        self.profiler.start_timer('total_processing')

        all_chunks = {}
        for doc_id, content in self.dataset.documents.items():
            self.profiler.start_timer('chunking')
            chunks = strategy.chunk(content)
            self.profiler.end_timer('chunking')

            all_chunks[doc_id] = chunks

        self.memory_profiler.take_memory_snapshot(f"{strategy_name}_after_chunking")

        # Generate embeddings for chunks
        self.profiler.start_timer('embedding')
        chunk_embeddings = self._generate_embeddings(all_chunks)
        self.profiler.end_timer('embedding')

        self.memory_profiler.take_memory_snapshot(f"{strategy_name}_after_embedding")

        # Evaluate retrieval performance
        retrieval_results = self._evaluate_retrieval(all_chunks, chunk_embeddings)
        results['retrieval_metrics'] = retrieval_results

        # Evaluate chunk quality
        quality_results = self._evaluate_chunk_quality(all_chunks)
        results['quality_metrics'] = quality_results

        # Get performance metrics
        self.profiler.end_timer('total_processing')
        performance_metrics = self.profiler.get_performance_metrics(len(self.dataset.documents))
        results['performance_metrics'] = performance_metrics.__dict__

        # Get memory metrics
        self.memory_profiler.take_memory_snapshot(f"{strategy_name}_end")
        results['memory_metrics'] = {
            'peak_memory_mb': self.memory_profiler.get_peak_memory_usage(),
            'memory_by_operation': self.memory_profiler.get_memory_usage_by_operation()
        }

        return results

    def _evaluate_retrieval(self, all_chunks: Dict, chunk_embeddings: Dict) -> Dict:
        """Evaluate retrieval performance"""
        retrieval_metrics = {
            'precision': [],
            'recall': [],
            'f1_score': [],
            'mrr': [],
            'map': []
        }

        for query in self.dataset.queries:
            # Perform retrieval
            self.profiler.start_timer('search')
            retrieved_chunks = self._retrieve_chunks(query.question, chunk_embeddings, k=10)
            self.profiler.end_timer('search')

            # Get relevant chunks for this query
            relevant_chunk_ids = set(query.relevant_chunk_ids)
            relevant_chunks = [chunk for chunk in retrieved_chunks
                             if chunk.get('id') in relevant_chunk_ids]

            # Calculate metrics
            precision = calculate_precision(retrieved_chunks, relevant_chunks)
            recall = calculate_recall(retrieved_chunks, relevant_chunks)
            f1 = calculate_f1_score(precision, recall)

            retrieval_metrics['precision'].append(precision)
            retrieval_metrics['recall'].append(recall)
            retrieval_metrics['f1_score'].append(f1)

        # Calculate averages
        return {metric: np.mean(values) for metric, values in retrieval_metrics.items()}

    def _evaluate_chunk_quality(self, all_chunks: Dict) -> Dict:
        """Evaluate quality of generated chunks"""
        quality_assessor = ChunkQualityAssessor()
        quality_scores = []

        for doc_id, chunks in all_chunks.items():
            # Analyze document
            content = self.dataset.documents[doc_id]
            analyzer = DocumentAnalyzer()
            analysis = analyzer.analyze(content)

            # Assess chunk quality
            scores = quality_assessor.assess_chunks(chunks, analysis)
            quality_scores.append(scores)

        # Aggregate quality scores
        if quality_scores:
            avg_scores = {}
            for metric in quality_scores[0].keys():
                avg_scores[metric] = np.mean([scores[metric] for scores in quality_scores])
            return avg_scores

        return {}

    def _compare_strategies(self, evaluation_results: Dict) -> Dict:
        """Compare performance across strategies"""
        ab_analyzer = ABTestAnalyzer()

        comparison = {}

        # Compare each metric across strategies
        strategy_names = list(evaluation_results.keys())

        for i in range(len(strategy_names)):
            for j in range(i + 1, len(strategy_names)):
                strategy1 = strategy_names[i]
                strategy2 = strategy_names[j]

                comparison_key = f"{strategy1}_vs_{strategy2}"
                comparison[comparison_key] = {}

                # Compare retrieval metrics
                for metric in ['precision', 'recall', 'f1_score']:
                    if (metric in evaluation_results[strategy1]['retrieval_metrics'] and
                        metric in evaluation_results[strategy2]['retrieval_metrics']):

                        comparison[comparison_key][f"retrieval_{metric}"] = ab_analyzer.compare_metrics(
                            [evaluation_results[strategy1]['retrieval_metrics'][metric]],
                            [evaluation_results[strategy2]['retrieval_metrics'][metric]],
                            f"retrieval_{metric}"
                        )

        return comparison

    def _generate_recommendations(self, comparison_results: Dict) -> Dict:
        """Generate recommendations based on evaluation results"""
        recommendations = {
            'best_overall': None,
            'best_for_precision': None,
            'best_for_recall': None,
            'best_for_performance': None,
            'trade_offs': []
        }

        # This would analyze the comparison results and generate specific recommendations
        # Implementation depends on specific use case requirements

        return recommendations

    def _generate_embeddings(self, all_chunks: Dict) -> Dict:
        """Generate embeddings for all chunks"""
        # This would use the actual embedding model
        # Placeholder implementation
        embeddings = {}

        for doc_id, chunks in all_chunks.items():
            embeddings[doc_id] = []
            for chunk in chunks:
                # Generate embedding for chunk content
                embedding = np.random.rand(384)  # Placeholder
                embeddings[doc_id].append({
                    'chunk': chunk,
                    'embedding': embedding
                })

        return embeddings

    def _retrieve_chunks(self, query: str, chunk_embeddings: Dict, k: int = 10) -> List[Dict]:
        """Retrieve most relevant chunks for a query"""
        # This would use actual similarity search
        # Placeholder implementation
        all_chunks = []

        for doc_embeddings in chunk_embeddings.values():
            for chunk_data in doc_embeddings:
                all_chunks.append(chunk_data['chunk'])

        # Simple random selection as placeholder
        selected = random.sample(all_chunks, min(k, len(all_chunks)))

        return selected
```

This comprehensive evaluation framework provides the tools needed to thoroughly assess chunking strategies across multiple dimensions: retrieval effectiveness, answer quality, system performance, and statistical significance. The modular design allows for easy extension and customization based on specific requirements and use cases.