Advanced RAG Implementation: 10x Performance with Multi-Stage Retrieval

class TwoStageRetrieval:
    def __init__(self, vector_db, embedding_model, reranker):
        self.vector_db = vector_db
        self.embedding_model = embedding_model
        self.reranker = reranker
    
    async def retrieve(self, query, top_k=20, final_k=5):
        """Two-stage retrieval with reranking"""
        # Stage 1: Vector similarity search
        query_embedding = await self.embedding_model.embed(query)
        candidates = await self.vector_db.similarity_search(
            query_embedding, top_k=top_k
        )
        
        # Stage 2: Rerank candidates
        reranked = await self.reranker.rerank(query, candidates)
        
        # Return top final_k results
        return reranked[:final_k]
    
    async def hybrid_retrieval(self, query, top_k=20, final_k=5):
        """Hybrid retrieval combining multiple strategies"""
        # Vector search
        vector_results = await self.vector_search(query, top_k)
        
        # Keyword search
        keyword_results = await self.keyword_search(query, top_k)
        
        # Combine results
        combined_results = await self.combine_results(
            vector_results, keyword_results
        )
        
        # Rerank combined results
        reranked = await self.reranker.rerank(query, combined_results)
        
        return reranked[:final_k]

class ThreeStageRetrieval:
    def __init__(self, vector_db, embedding_model, reranker, final_ranker):
        self.vector_db = vector_db
        self.embedding_model = embedding_model
        self.reranker = reranker
        self.final_ranker = final_ranker
    
    async def retrieve(self, query, top_k=50, mid_k=20, final_k=5):
        """Three-stage retrieval with multiple ranking steps"""
        # Stage 1: Broad retrieval
        broad_results = await self.broad_retrieval(query, top_k)
        
        # Stage 2: Rerank and filter
        reranked_results = await self.reranker.rerank(query, broad_results)
        filtered_results = await self.filter_results(reranked_results, mid_k)
        
        # Stage 3: Final ranking
        final_results = await self.final_ranker.rank(query, filtered_results)
        
        return final_results[:final_k]
    
    async def broad_retrieval(self, query, top_k):
        """Broad retrieval using multiple strategies"""
        # Vector search
        vector_results = await self.vector_search(query, top_k // 2)
        
        # Keyword search
        keyword_results = await self.keyword_search(query, top_k // 2)
        
        # Semantic search
        semantic_results = await self.semantic_search(query, top_k // 2)
        
        # Combine all results
        all_results = vector_results + keyword_results + semantic_results
        
        # Remove duplicates
        unique_results = await self.remove_duplicates(all_results)
        
        return unique_results[:top_k]
    
    async def filter_results(self, results, max_results):
        """Filter results based on relevance and quality"""
        filtered = []
        
        for result in results:
            # Check relevance score
            if result.get('relevance_score', 0) > 0.3:
                # Check quality metrics
                if await self.check_quality(result):
                    filtered.append(result)
            
            if len(filtered) >= max_results:
                break
        
        return filtered
    
    async def check_quality(self, result):
        """Check result quality metrics"""
        # Check text length
        if len(result.get('text', '')) < 50:
            return False
        
        # Check metadata completeness
        if not result.get('metadata', {}).get('source'):
            return False
        
        # Check for spam or low-quality content
        if await self.is_low_quality(result):
            return False
        
        return True

class AdaptiveRetrieval:
    def __init__(self, retrieval_strategies, strategy_selector):
        self.strategies = retrieval_strategies
        self.selector = strategy_selector
        self.performance_tracker = PerformanceTracker()
    
    async def retrieve(self, query, context=None):
        """Adaptive retrieval based on query characteristics"""
        # Analyze query characteristics
        query_analysis = await self.analyze_query(query)
        
        # Select appropriate strategy
        strategy = await self.selector.select_strategy(query_analysis)
        
        # Execute retrieval with selected strategy
        results = await self.strategies[strategy].retrieve(query, context)
        
        # Track performance
        await self.performance_tracker.track(strategy, query_analysis, results)
        
        return results
    
    async def analyze_query(self, query):
        """Analyze query characteristics"""
        analysis = {
            'length': len(query.split()),
            'complexity': await self.calculate_complexity(query),
            'intent': await self.detect_intent(query),
            'domain': await self.detect_domain(query),
            'temporal': await self.detect_temporal(query)
        }
        
        return analysis
    
    async def calculate_complexity(self, query):
        """Calculate query complexity"""
        # Simple heuristic for complexity
        complexity_score = 0
        
        # Length factor
        complexity_score += len(query.split()) * 0.1
        
        # Question words
        question_words = ['what', 'how', 'why', 'when', 'where', 'which']
        complexity_score += sum(1 for word in question_words if word in query.lower()) * 0.2
        
        # Technical terms
        technical_terms = ['algorithm', 'implementation', 'architecture', 'optimization']
        complexity_score += sum(1 for term in technical_terms if term in query.lower()) * 0.3
        
        return min(complexity_score, 1.0)

class CrossEncoderReranker:
    def __init__(self, model_name="cross-encoder/ms-marco-MiniLM-L-6-v2"):
        self.model = CrossEncoder(model_name)
        self.batch_size = 32
    
    async def rerank(self, query, candidates):
        """Rerank candidates using cross-encoder"""
        if not candidates:
            return []
        
        # Prepare query-candidate pairs
        pairs = [(query, candidate['text']) for candidate in candidates]
        
        # Get relevance scores
        scores = self.model.predict(pairs)
        
        # Update candidates with scores
        for i, candidate in enumerate(candidates):
            candidate['rerank_score'] = float(scores[i])
        
        # Sort by rerank score
        reranked = sorted(candidates, key=lambda x: x['rerank_score'], reverse=True)
        
        return reranked
    
    async def batch_rerank(self, query, candidates, batch_size=None):
        """Batch reranking for efficiency"""
        if batch_size is None:
            batch_size = self.batch_size
        
        reranked_candidates = []
        
        # Process in batches
        for i in range(0, len(candidates), batch_size):
            batch = candidates[i:i + batch_size]
            batch_reranked = await self.rerank(query, batch)
            reranked_candidates.extend(batch_reranked)
        
        return reranked_candidates

class MultiFactorReranker:
    def __init__(self, cross_encoder, keyword_matcher, freshness_scorer):
        self.cross_encoder = cross_encoder
        self.keyword_matcher = keyword_matcher
        self.freshness_scorer = freshness_scorer
        self.weights = {
            'relevance': 0.4,
            'keyword_match': 0.2,
            'freshness': 0.2,
            'authority': 0.2
        }
    
    async def rerank(self, query, candidates):
        """Rerank using multiple factors"""
        if not candidates:
            return []
        
        # Calculate scores for each factor
        for candidate in candidates:
            # Relevance score
            relevance_score = await self.calculate_relevance_score(query, candidate)
            
            # Keyword match score
            keyword_score = await self.keyword_matcher.score(query, candidate['text'])
            
            # Freshness score
            freshness_score = await self.freshness_scorer.score(candidate)
            
            # Authority score
            authority_score = await self.calculate_authority_score(candidate)
            
            # Calculate weighted score
            total_score = (
                relevance_score * self.weights['relevance'] +
                keyword_score * self.weights['keyword_match'] +
                freshness_score * self.weights['freshness'] +
                authority_score * self.weights['authority']
            )
            
            candidate['total_score'] = total_score
            candidate['factor_scores'] = {
                'relevance': relevance_score,
                'keyword_match': keyword_score,
                'freshness': freshness_score,
                'authority': authority_score
            }
        
        # Sort by total score
        reranked = sorted(candidates, key=lambda x: x['total_score'], reverse=True)
        
        return reranked
    
    async def calculate_relevance_score(self, query, candidate):
        """Calculate relevance score using cross-encoder"""
        score = self.cross_encoder.predict([(query, candidate['text'])])
        return float(score[0])
    
    async def calculate_authority_score(self, candidate):
        """Calculate authority score based on source"""
        source = candidate.get('metadata', {}).get('source', '')
        
        # Authority scoring based on source
        authority_scores = {
            'academic': 0.9,
            'government': 0.8,
            'news': 0.7,
            'blog': 0.5,
            'forum': 0.3
        }
        
        return authority_scores.get(source, 0.5)

class LearningToRankReranker:
    def __init__(self, model_path=None):
        self.model = None
        self.feature_extractor = FeatureExtractor()
        self.training_data = []
        
        if model_path:
            self.load_model(model_path)
    
    async def rerank(self, query, candidates):
        """Rerank using learned model"""
        if not candidates:
            return []
        
        # Extract features
        features = []
        for candidate in candidates:
            feature_vector = await self.feature_extractor.extract(
                query, candidate
            )
            features.append(feature_vector)
        
        # Predict scores
        if self.model:
            scores = self.model.predict(features)
        else:
            # Fallback to simple scoring
            scores = await self.fallback_scoring(query, candidates)
        
        # Update candidates with scores
        for i, candidate in enumerate(candidates):
            candidate['learned_score'] = float(scores[i])
        
        # Sort by learned score
        reranked = sorted(candidates, key=lambda x: x['learned_score'], reverse=True)
        
        return reranked
    
    async def train(self, training_data):
        """Train the reranking model"""
        # Extract features and labels
        X, y = [], []
        
        for example in training_data:
            query = example['query']
            candidates = example['candidates']
            labels = example['labels']
            
            for candidate, label in zip(candidates, labels):
                features = await self.feature_extractor.extract(query, candidate)
                X.append(features)
                y.append(label)
        
        # Train model
        self.model = self.create_model()
        self.model.fit(X, y)
        
        return self.model
    
    def create_model(self):
        """Create learning-to-rank model"""
        from sklearn.ensemble import RandomForestRegressor
        return RandomForestRegressor(n_estimators=100, random_state=42)

class HybridSearch:
    def __init__(self, vector_db, keyword_index, embedding_model):
        self.vector_db = vector_db
        self.keyword_index = keyword_index
        self.embedding_model = embedding_model
        self.fusion_strategies = {
            'reciprocal_rank': self.reciprocal_rank_fusion,
            'weighted_combination': self.weighted_combination,
            'learning_to_rank': self.learning_to_rank_fusion
        }
    
    async def search(self, query, top_k=10, fusion_method='reciprocal_rank'):
        """Hybrid search combining vector and keyword search"""
        # Vector search
        vector_results = await self.vector_search(query, top_k * 2)
        
        # Keyword search
        keyword_results = await self.keyword_search(query, top_k * 2)
        
        # Fuse results
        if fusion_method in self.fusion_strategies:
            fused_results = await self.fusion_strategies[fusion_method](
                vector_results, keyword_results, top_k
            )
        else:
            # Default to reciprocal rank fusion
            fused_results = await self.reciprocal_rank_fusion(
                vector_results, keyword_results, top_k
            )
        
        return fused_results
    
    async def vector_search(self, query, top_k):
        """Vector similarity search"""
        query_embedding = await self.embedding_model.embed(query)
        results = await self.vector_db.similarity_search(query_embedding, top_k)
        
        # Add search type
        for result in results:
            result['search_type'] = 'vector'
        
        return results
    
    async def keyword_search(self, query, top_k):
        """Keyword-based search"""
        # Extract keywords
        keywords = await self.extract_keywords(query)
        
        # Search keyword index
        results = await self.keyword_index.search(keywords, top_k)
        
        # Add search type
        for result in results:
            result['search_type'] = 'keyword'
        
        return results
    
    async def reciprocal_rank_fusion(self, vector_results, keyword_results, top_k):
        """Reciprocal rank fusion for combining results"""
        # Create result maps
        vector_map = {result['id']: result for result in vector_results}
        keyword_map = {result['id']: result for result in keyword_results}
        
        # Calculate reciprocal rank scores
        scores = {}
        
        for i, result in enumerate(vector_results):
            doc_id = result['id']
            rr_score = 1.0 / (i + 1)  # Reciprocal rank
            scores[doc_id] = scores.get(doc_id, 0) + rr_score
        
        for i, result in enumerate(keyword_results):
            doc_id = result['id']
            rr_score = 1.0 / (i + 1)  # Reciprocal rank
            scores[doc_id] = scores.get(doc_id, 0) + rr_score
        
        # Combine results
        combined_results = []
        for doc_id, score in scores.items():
            if doc_id in vector_map:
                result = vector_map[doc_id].copy()
                result['fusion_score'] = score
                combined_results.append(result)
            elif doc_id in keyword_map:
                result = keyword_map[doc_id].copy()
                result['fusion_score'] = score
                combined_results.append(result)
        
        # Sort by fusion score
        combined_results.sort(key=lambda x: x['fusion_score'], reverse=True)
        
        return combined_results[:top_k]
    
    async def weighted_combination(self, vector_results, keyword_results, top_k):
        """Weighted combination of search results"""
        # Set weights
        vector_weight = 0.7
        keyword_weight = 0.3
        
        # Normalize scores
        vector_results = await self.normalize_scores(vector_results)
        keyword_results = await self.normalize_scores(keyword_results)
        
        # Create result maps
        vector_map = {result['id']: result for result in vector_results}
        keyword_map = {result['id']: result for result in keyword_results}
        
        # Calculate weighted scores
        scores = {}
        for result in vector_results:
            doc_id = result['id']
            scores[doc_id] = result['normalized_score'] * vector_weight
        
        for result in keyword_results:
            doc_id = result['id']
            if doc_id in scores:
                scores[doc_id] += result['normalized_score'] * keyword_weight
            else:
                scores[doc_id] = result['normalized_score'] * keyword_weight
        
        # Combine results
        combined_results = []
        for doc_id, score in scores.items():
            if doc_id in vector_map:
                result = vector_map[doc_id].copy()
            else:
                result = keyword_map[doc_id].copy()
            
            result['weighted_score'] = score
            combined_results.append(result)
        
        # Sort by weighted score
        combined_results.sort(key=lambda x: x['weighted_score'], reverse=True)
        
        return combined_results[:top_k]

class MultiModalSearch:
    def __init__(self, text_embedder, image_embedder, audio_embedder, vector_db):
        self.text_embedder = text_embedder
        self.image_embedder = image_embedder
        self.audio_embedder = audio_embedder
        self.vector_db = vector_db
    
    async def search(self, query, query_type='text', top_k=10):
        """Multi-modal search based on query type"""
        if query_type == 'text':
            return await self.text_search(query, top_k)
        elif query_type == 'image':
            return await self.image_search(query, top_k)
        elif query_type == 'audio':
            return await self.audio_search(query, top_k)
        elif query_type == 'multimodal':
            return await self.multimodal_search(query, top_k)
        else:
            raise ValueError(f"Unknown query type: {query_type}")
    
    async def text_search(self, query, top_k):
        """Text-based search"""
        query_embedding = await self.text_embedder.embed(query)
        results = await self.vector_db.similarity_search(
            query_embedding, top_k, filter={'type': 'text'}
        )
        return results
    
    async def image_search(self, query, top_k):
        """Image-based search"""
        query_embedding = await self.image_embedder.embed(query)
        results = await self.vector_db.similarity_search(
            query_embedding, top_k, filter={'type': 'image'}
        )
        return results
    
    async def audio_search(self, query, top_k):
        """Audio-based search"""
        query_embedding = await self.audio_embedder.embed(query)
        results = await self.vector_db.similarity_search(
            query_embedding, top_k, filter={'type': 'audio'}
        )
        return results
    
    async def multimodal_search(self, query, top_k):
        """Multi-modal search combining all modalities"""
        # Search each modality
        text_results = await self.text_search(query, top_k // 3)
        image_results = await self.image_search(query, top_k // 3)
        audio_results = await self.audio_search(query, top_k // 3)
        
        # Combine results
        all_results = text_results + image_results + audio_results
        
        # Rerank combined results
        reranked = await self.rerank_multimodal(query, all_results)
        
        return reranked[:top_k]
    
    async def rerank_multimodal(self, query, results):
        """Rerank multi-modal results"""
        # Calculate cross-modal similarity
        for result in results:
            if result['type'] == 'text':
                similarity = await self.calculate_text_similarity(query, result)
            elif result['type'] == 'image':
                similarity = await self.calculate_image_similarity(query, result)
            elif result['type'] == 'audio':
                similarity = await self.calculate_audio_similarity(query, result)
            else:
                similarity = 0.0
            
            result['cross_modal_similarity'] = similarity
        
        # Sort by cross-modal similarity
        reranked = sorted(results, key=lambda x: x['cross_modal_similarity'], reverse=True)
        
        return reranked

class EmbeddingFineTuner:
    def __init__(self, base_model, domain_data, learning_rate=1e-5):
        self.base_model = base_model
        self.domain_data = domain_data
        self.learning_rate = learning_rate
        self.fine_tuned_model = None
    
    async def fine_tune(self, epochs=5, batch_size=32):
        """Fine-tune embedding model for domain-specific performance"""
        # Prepare training data
        training_data = await self.prepare_training_data()
        
        # Create fine-tuning dataset
        dataset = self.create_dataset(training_data)
        
        # Fine-tune model
        self.fine_tuned_model = await self.train_model(dataset, epochs, batch_size)
        
        return self.fine_tuned_model
    
    async def prepare_training_data(self):
        """Prepare training data for fine-tuning"""
        training_data = []
        
        for example in self.domain_data:
            # Create positive pairs (similar documents)
            positive_pairs = await self.create_positive_pairs(example)
            training_data.extend(positive_pairs)
            
            # Create negative pairs (dissimilar documents)
            negative_pairs = await self.create_negative_pairs(example)
            training_data.extend(negative_pairs)
        
        return training_data
    
    async def create_positive_pairs(self, example):
        """Create positive training pairs"""
        positive_pairs = []
        
        # Use query-document pairs as positive examples
        query = example['query']
        relevant_docs = example['relevant_documents']
        
        for doc in relevant_docs:
            positive_pairs.append({
                'text1': query,
                'text2': doc['text'],
                'label': 1.0
            })
        
        return positive_pairs
    
    async def create_negative_pairs(self, example):
        """Create negative training pairs"""
        negative_pairs = []
        
        # Use query-irrelevant document pairs as negative examples
        query = example['query']
        irrelevant_docs = example['irrelevant_documents']
        
        for doc in irrelevant_docs:
            negative_pairs.append({
                'text1': query,
                'text2': doc['text'],
                'label': 0.0
            })
        
        return negative_pairs
    
    def create_dataset(self, training_data):
        """Create training dataset"""
        # This would depend on your specific implementation
        # For example, using Hugging Face datasets
        from datasets import Dataset
        
        dataset = Dataset.from_list(training_data)
        return dataset
    
    async def train_model(self, dataset, epochs, batch_size):
        """Train the fine-tuned model"""
        # This would depend on your specific implementation
        # For example, using Hugging Face transformers
        from transformers import Trainer, TrainingArguments
        
        training_args = TrainingArguments(
            output_dir='./fine_tuned_model',
            num_train_epochs=epochs,
            per_device_train_batch_size=batch_size,
            learning_rate=self.learning_rate,
            save_steps=1000,
            eval_steps=1000,
        )
        
        trainer = Trainer(
            model=self.base_model,
            args=training_args,
            train_dataset=dataset,
        )
        
        trainer.train()
        
        return trainer.model

class RerankerFineTuner:
    def __init__(self, base_reranker, training_data):
        self.base_reranker = base_reranker
        self.training_data = training_data
        self.fine_tuned_reranker = None
    
    async def fine_tune(self, epochs=10, learning_rate=1e-4):
        """Fine-tune reranker for domain-specific performance"""
        # Prepare training data
        training_examples = await self.prepare_training_examples()
        
        # Create training dataset
        dataset = self.create_dataset(training_examples)
        
        # Fine-tune reranker
        self.fine_tuned_reranker = await self.train_reranker(
            dataset, epochs, learning_rate
        )
        
        return self.fine_tuned_reranker
    
    async def prepare_training_examples(self):
        """Prepare training examples for reranker"""
        training_examples = []
        
        for example in self.training_data:
            query = example['query']
            candidates = example['candidates']
            labels = example['labels']
            
            # Create training example
            training_example = {
                'query': query,
                'candidates': candidates,
                'labels': labels
            }
            
            training_examples.append(training_example)
        
        return training_examples
    
    async def train_reranker(self, dataset, epochs, learning_rate):
        """Train the fine-tuned reranker"""
        # This would depend on your specific implementation
        # For example, using a custom training loop
        model = self.base_reranker.model
        
        # Set up training
        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
        criterion = torch.nn.MSELoss()
        
        # Training loop
        for epoch in range(epochs):
            for batch in dataset:
                # Forward pass
                predictions = model(batch['features'])
                loss = criterion(predictions, batch['labels'])
                
                # Backward pass
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
        
        return model

class ContextSelector:
    def __init__(self, max_context_tokens=4000):
        self.max_context_tokens = max_context_tokens
        self.selection_strategies = {
            'relevance': self.relevance_selection,
            'diversity': self.diversity_selection,
            'coverage': self.coverage_selection,
            'hybrid': self.hybrid_selection
        }
    
    async def select_context(self, query, retrieved_docs, strategy='hybrid'):
        """Select context using specified strategy"""
        if strategy not in self.selection_strategies:
            raise ValueError(f"Unknown strategy: {strategy}")
        
        return await self.selection_strategies[strategy](query, retrieved_docs)
    
    async def relevance_selection(self, query, retrieved_docs):
        """Select context based on relevance scores"""
        # Sort by relevance score
        sorted_docs = sorted(
            retrieved_docs, 
            key=lambda x: x.get('relevance_score', 0), 
            reverse=True
        )
        
        # Select top documents that fit in context
        selected_docs = []
        current_tokens = 0
        
        for doc in sorted_docs:
            doc_tokens = self.count_tokens(doc['text'])
            if current_tokens + doc_tokens <= self.max_context_tokens:
                selected_docs.append(doc)
                current_tokens += doc_tokens
            else:
                break
        
        return selected_docs
    
    async def diversity_selection(self, query, retrieved_docs):
        """Select context based on diversity"""
        # Group documents by similarity
        doc_groups = await self.group_similar_documents(retrieved_docs)
        
        # Select diverse documents
        selected_docs = []
        current_tokens = 0
        
        for group in doc_groups:
            # Select best document from each group
            best_doc = max(group, key=lambda x: x.get('relevance_score', 0))
            
            doc_tokens = self.count_tokens(best_doc['text'])
            if current_tokens + doc_tokens <= self.max_context_tokens:
                selected_docs.append(best_doc)
                current_tokens += doc_tokens
            else:
                break
        
        return selected_docs
    
    async def coverage_selection(self, query, retrieved_docs):
        """Select context based on query coverage"""
        # Analyze query aspects
        query_aspects = await self.analyze_query_aspects(query)
        
        # Select documents that cover different aspects
        selected_docs = []
        covered_aspects = set()
        current_tokens = 0
        
        for doc in retrieved_docs:
            doc_aspects = await self.analyze_document_aspects(doc)
            
            # Check if document covers new aspects
            new_aspects = doc_aspects - covered_aspects
            if new_aspects or not covered_aspects:
                doc_tokens = self.count_tokens(doc['text'])
                if current_tokens + doc_tokens <= self.max_context_tokens:
                    selected_docs.append(doc)
                    covered_aspects.update(doc_aspects)
                    current_tokens += doc_tokens
                else:
                    break
        
        return selected_docs
    
    async def hybrid_selection(self, query, retrieved_docs):
        """Hybrid selection combining multiple strategies"""
        # Get selections from different strategies
        relevance_docs = await self.relevance_selection(query, retrieved_docs)
        diversity_docs = await self.diversity_selection(query, retrieved_docs)
        coverage_docs = await self.coverage_selection(query, retrieved_docs)
        
        # Combine selections
        combined_docs = []
        doc_scores = {}
        
        # Score documents based on multiple strategies
        for doc in retrieved_docs:
            doc_id = doc['id']
            scores = []
            
            # Relevance score
            if doc in relevance_docs:
                scores.append(1.0)
            else:
                scores.append(0.0)
            
            # Diversity score
            if doc in diversity_docs:
                scores.append(1.0)
            else:
                scores.append(0.0)
            
            # Coverage score
            if doc in coverage_docs:
                scores.append(1.0)
            else:
                scores.append(0.0)
            
            # Calculate hybrid score
            doc_scores[doc_id] = sum(scores) / len(scores)
        
        # Select documents based on hybrid scores
        sorted_docs = sorted(
            retrieved_docs,
            key=lambda x: doc_scores.get(x['id'], 0),
            reverse=True
        )
        
        current_tokens = 0
        for doc in sorted_docs:
            doc_tokens = self.count_tokens(doc['text'])
            if current_tokens + doc_tokens <= self.max_context_tokens:
                combined_docs.append(doc)
                current_tokens += doc_tokens
            else:
                break
        
        return combined_docs

class ContextCompressor:
    def __init__(self, compression_model, max_tokens=2000):
        self.compression_model = compression_model
        self.max_tokens = max_tokens
        self.compression_strategies = {
            'summarization': self.summarize_context,
            'extraction': self.extract_key_information,
            'pruning': self.prune_context,
            'hybrid': self.hybrid_compression
        }
    
    async def compress_context(self, context, strategy='hybrid'):
        """Compress context using specified strategy"""
        if strategy not in self.compression_strategies:
            raise ValueError(f"Unknown strategy: {strategy}")
        
        return await self.compression_strategies[strategy](context)
    
    async def summarize_context(self, context):
        """Summarize context to reduce length"""
        # Create summarization prompt
        prompt = f"""
        Summarize the following context while preserving key information:
        
        {context}
        
        Provide a concise summary that maintains important details.
        """
        
        # Generate summary
        summary = await self.compression_model.generate(prompt)
        
        return summary
    
    async def extract_key_information(self, context):
        """Extract key information from context"""
        # Extract key sentences
        key_sentences = await self.extract_key_sentences(context)
        
        # Extract key entities
        key_entities = await self.extract_key_entities(context)
        
        # Combine key information
        compressed = {
            'key_sentences': key_sentences,
            'key_entities': key_entities,
            'compressed_text': ' '.join(key_sentences)
        }
        
        return compressed
    
    async def prune_context(self, context):
        """Prune context by removing less important parts"""
        # Split context into sentences
        sentences = context.split('. ')
        
        # Score sentences by importance
        sentence_scores = []
        for sentence in sentences:
            score = await self.calculate_sentence_importance(sentence)
            sentence_scores.append((sentence, score))
        
        # Sort by importance
        sentence_scores.sort(key=lambda x: x[1], reverse=True)
        
        # Select top sentences that fit in token limit
        selected_sentences = []
        current_tokens = 0
        
        for sentence, score in sentence_scores:
            sentence_tokens = self.count_tokens(sentence)
            if current_tokens + sentence_tokens <= self.max_tokens:
                selected_sentences.append(sentence)
                current_tokens += sentence_tokens
            else:
                break
        
        return '. '.join(selected_sentences)
    
    async def hybrid_compression(self, context):
        """Hybrid compression combining multiple strategies"""
        # First, try summarization
        summary = await self.summarize_context(context)
        
        # If summary is still too long, use extraction
        if self.count_tokens(summary) > self.max_tokens:
            compressed = await self.extract_key_information(context)
            return compressed['compressed_text']
        
        return summary

class RAGPerformanceMonitor:
    def __init__(self, metrics_backend, alerting_system):
        self.metrics = metrics_backend
        self.alerting = alerting_system
        self.performance_metrics = {}
        self.quality_metrics = {}
    
    async def monitor_rag_performance(self, query, response, sources, processing_time):
        """Monitor comprehensive RAG performance"""
        # Record basic metrics
        await self.record_basic_metrics(query, response, sources, processing_time)
        
        # Record quality metrics
        await self.record_quality_metrics(query, response, sources)
        
        # Record retrieval metrics
        await self.record_retrieval_metrics(sources)
        
        # Check for performance issues
        await self.check_performance_issues(processing_time, sources, response)
    
    async def record_basic_metrics(self, query, response, sources, processing_time):
        """Record basic performance metrics"""
        # Response time
        await self.metrics.record_histogram(
            'rag_response_time',
            processing_time,
            tags={'query_length': len(query.split())}
        )
        
        # Source count
        await self.metrics.record_gauge(
            'rag_source_count',
            len(sources)
        )
        
        # Response length
        await self.metrics.record_gauge(
            'rag_response_length',
            len(response.split())
        )
    
    async def record_quality_metrics(self, query, response, sources):
        """Record quality metrics"""
        # Relevance score
        relevance_score = await self.calculate_relevance_score(query, response)
        await self.metrics.record_gauge('rag_relevance_score', relevance_score)
        
        # Source quality
        source_quality = await self.calculate_source_quality(sources)
        await self.metrics.record_gauge('rag_source_quality', source_quality)
        
        # Response completeness
        completeness_score = await self.calculate_completeness_score(query, response)
        await self.metrics.record_gauge('rag_completeness_score', completeness_score)
    
    async def record_retrieval_metrics(self, sources):
        """Record retrieval-specific metrics"""
        if not sources:
            return
        
        # Average similarity score
        avg_similarity = sum(s.get('similarity', 0) for s in sources) / len(sources)
        await self.metrics.record_gauge('rag_avg_similarity', avg_similarity)
        
        # Source diversity
        diversity_score = await self.calculate_source_diversity(sources)
        await self.metrics.record_gauge('rag_source_diversity', diversity_score)
        
        # Source freshness
        freshness_score = await self.calculate_source_freshness(sources)
        await self.metrics.record_gauge('rag_source_freshness', freshness_score)
    
    async def check_performance_issues(self, processing_time, sources, response):
        """Check for performance issues and alert"""
        issues = []
        
        # Check response time
        if processing_time > 5.0:  # 5 seconds
            issues.append({
                'type': 'PERFORMANCE',
                'severity': 'HIGH',
                'message': f"RAG response time {processing_time}s exceeds threshold"
            })
        
        # Check source quality
        if sources and len(sources) > 0:
            avg_similarity = sum(s.get('similarity', 0) for s in sources) / len(sources)
            if avg_similarity < 0.6:  # 60% similarity threshold
                issues.append({
                    'type': 'QUALITY',
                    'severity': 'MEDIUM',
                    'message': f"RAG source similarity {avg_similarity:.2f} below threshold"
                })
        
        # Check response quality
        if len(response.split()) < 10:  # Very short response
            issues.append({
                'type': 'QUALITY',
                'severity': 'MEDIUM',
                'message': "RAG response too short, possible quality issue"
            })
        
        # Send alerts for critical issues
        for issue in issues:
            if issue['severity'] in ['HIGH', 'CRITICAL']:
                await self.alerting.send_alert(issue)

class RAGOptimizer:
    def __init__(self, rag_system, performance_monitor):
        self.rag_system = rag_system
        self.monitor = performance_monitor
        self.optimization_strategies = {
            'retrieval': self.optimize_retrieval,
            'reranking': self.optimize_reranking,
            'context': self.optimize_context,
            'generation': self.optimize_generation
        }
    
    async def optimize_rag_system(self, performance_data):
        """Optimize RAG system based on performance data"""
        optimizations = []
        
        # Analyze performance bottlenecks
        bottlenecks = await self.identify_bottlenecks(performance_data)
        
        # Apply optimizations
        for bottleneck in bottlenecks:
            if bottleneck in self.optimization_strategies:
                optimization = await self.optimization_strategies[bottleneck]()
                optimizations.append(optimization)
        
        return optimizations
    
    async def identify_bottlenecks(self, performance_data):
        """Identify performance bottlenecks"""
        bottlenecks = []
        
        # Check retrieval performance
        if performance_data.get('avg_retrieval_time', 0) > 1.0:  # 1 second
            bottlenecks.append('retrieval')
        
        # Check reranking performance
        if performance_data.get('avg_reranking_time', 0) > 0.5:  # 0.5 seconds
            bottlenecks.append('reranking')
        
        # Check context assembly performance
        if performance_data.get('avg_context_time', 0) > 0.3:  # 0.3 seconds
            bottlenecks.append('context')
        
        # Check generation performance
        if performance_data.get('avg_generation_time', 0) > 2.0:  # 2 seconds
            bottlenecks.append('generation')
        
        return bottlenecks
    
    async def optimize_retrieval(self):
        """Optimize retrieval performance"""
        # Implement retrieval optimizations
        optimizations = []
        
        # Optimize vector database
        await self.rag_system.vector_db.optimize_index()
        optimizations.append("Vector database index optimized")
        
        # Optimize embedding model
        await self.rag_system.embedding_model.optimize()
        optimizations.append("Embedding model optimized")
        
        # Optimize retrieval parameters
        await self.rag_system.optimize_retrieval_parameters()
        optimizations.append("Retrieval parameters optimized")
        
        return optimizations
    
    async def optimize_reranking(self):
        """Optimize reranking performance"""
        # Implement reranking optimizations
        optimizations = []
        
        # Optimize reranker model
        await self.rag_system.reranker.optimize()
        optimizations.append("Reranker model optimized")
        
        # Optimize reranking parameters
        await self.rag_system.optimize_reranking_parameters()
        optimizations.append("Reranking parameters optimized")
        
        return optimizations
    
    async def optimize_context(self):
        """Optimize context assembly performance"""
        # Implement context optimizations
        optimizations = []
        
        # Optimize context selection
        await self.rag_system.context_selector.optimize()
        optimizations.append("Context selection optimized")
        
        # Optimize context compression
        await self.rag_system.context_compressor.optimize()
        optimizations.append("Context compression optimized")
        
        return optimizations
    
    async def optimize_generation(self):
        """Optimize generation performance"""
        # Implement generation optimizations
        optimizations = []
        
        # Optimize generation model
        await self.rag_system.generation_model.optimize()
        optimizations.append("Generation model optimized")
        
        # Optimize generation parameters
        await self.rag_system.optimize_generation_parameters()
        optimizations.append("Generation parameters optimized")
        
        return optimizations