Performance Optimization

import noveum_trace
import time
import numpy as np
from typing import Dict, List
 
class PerformanceAnalyzer:
    """Analyze performance patterns from tracing data."""
 
    def __init__(self):
        self.performance_metrics = {}
        self.bottleneck_thresholds = {
            "llm_latency": 5.0,      # 5 seconds
            "embedding_latency": 2.0,  # 2 seconds
            "retrieval_latency": 1.0,  # 1 second
            "memory_usage": 1000,      # 1GB
            "token_efficiency": 0.5    # 50% efficiency
        }
 
    @noveum_trace.trace_analysis(analysis_type="performance_bottleneck")
    def analyze_operation_performance(self, operation_data: Dict) -> Dict:
        """Analyze performance bottlenecks in AI operations."""
 
        with noveum_trace.trace("performance_analysis") as span:
            span.set_attribute("analysis.operation_type", operation_data.get("type"))
            span.set_attribute("analysis.operation_id", operation_data.get("id"))
 
            # Analyze latency patterns
            latency_analysis = self.analyze_latency_patterns(operation_data)
            span.set_attribute("analysis.avg_latency", latency_analysis["avg_latency"])
            span.set_attribute("analysis.p95_latency", latency_analysis["p95_latency"])
            span.set_attribute("analysis.latency_variance", latency_analysis["variance"])
 
            # Analyze resource usage
            resource_analysis = self.analyze_resource_usage(operation_data)
            span.set_attribute("analysis.peak_memory", resource_analysis["peak_memory"])
            span.set_attribute("analysis.avg_cpu", resource_analysis["avg_cpu"])
 
            # Analyze token efficiency
            token_analysis = self.analyze_token_efficiency(operation_data)
            span.set_attribute("analysis.token_efficiency", token_analysis["efficiency"])
            span.set_attribute("analysis.token_waste", token_analysis["waste_percentage"])
 
            # Identify bottlenecks
            bottlenecks = self.identify_bottlenecks(
                latency_analysis,
                resource_analysis,
                token_analysis
            )
 
            span.set_attribute("analysis.bottleneck_count", len(bottlenecks))
            span.set_attribute("analysis.critical_bottlenecks",
                             len([b for b in bottlenecks if b["severity"] == "critical"]))
 
            # Generate optimization recommendations
            recommendations = self.generate_optimization_recommendations(bottlenecks)
            span.set_attribute("analysis.recommendations_count", len(recommendations))
 
            return {
                "bottlenecks": bottlenecks,
                "recommendations": recommendations,
                "performance_score": self.calculate_performance_score(
                    latency_analysis, resource_analysis, token_analysis
                )
            }
 
    def analyze_latency_patterns(self, operation_data: Dict) -> Dict:
        """Analyze latency patterns and identify slow operations."""
 
        latencies = operation_data.get("latencies", [])
        if not latencies:
            return {"avg_latency": 0, "p95_latency": 0, "variance": 0}
 
        avg_latency = np.mean(latencies)
        p95_latency = np.percentile(latencies, 95)
        p99_latency = np.percentile(latencies, 99)
        variance = np.var(latencies)
 
        # Identify anomalous latencies
        z_scores = np.abs((np.array(latencies) - avg_latency) / np.std(latencies))
        anomalous_count = np.sum(z_scores > 2)  # More than 2 standard deviations
 
        return {
            "avg_latency": avg_latency,
            "p95_latency": p95_latency,
            "p99_latency": p99_latency,
            "variance": variance,
            "anomalous_requests": anomalous_count,
            "anomaly_percentage": (anomalous_count / len(latencies)) * 100
        }
 
    def analyze_resource_usage(self, operation_data: Dict) -> Dict:
        """Analyze memory and CPU usage patterns."""
 
        memory_usage = operation_data.get("memory_usage", [])
        cpu_usage = operation_data.get("cpu_usage", [])
 
        return {
            "peak_memory": max(memory_usage) if memory_usage else 0,
            "avg_memory": np.mean(memory_usage) if memory_usage else 0,
            "memory_growth": self.calculate_memory_growth(memory_usage),
            "avg_cpu": np.mean(cpu_usage) if cpu_usage else 0,
            "peak_cpu": max(cpu_usage) if cpu_usage else 0,
            "cpu_variance": np.var(cpu_usage) if cpu_usage else 0
        }
 
    def analyze_token_efficiency(self, operation_data: Dict) -> Dict:
        """Analyze token usage efficiency."""
 
        token_data = operation_data.get("token_usage", {})
        input_tokens = token_data.get("input_tokens", [])
        output_tokens = token_data.get("output_tokens", [])
        context_tokens = token_data.get("context_tokens", [])
 
        if not input_tokens:
            return {"efficiency": 0, "waste_percentage": 0}
 
        # Calculate efficiency metrics
        avg_input = np.mean(input_tokens)
        avg_output = np.mean(output_tokens)
        avg_context = np.mean(context_tokens) if context_tokens else 0
 
        # Token efficiency = useful tokens / total tokens
        useful_tokens = avg_output  # Assuming output tokens are "useful"
        total_tokens = avg_input + avg_output
        efficiency = useful_tokens / total_tokens if total_tokens > 0 else 0
 
        # Calculate waste from oversized context
        optimal_context = self.calculate_optimal_context_size(input_tokens, output_tokens)
        context_waste = max(0, avg_context - optimal_context) if avg_context > 0 else 0
        waste_percentage = (context_waste / total_tokens) * 100 if total_tokens > 0 else 0
 
        return {
            "efficiency": efficiency,
            "waste_percentage": waste_percentage,
            "avg_input_tokens": avg_input,
            "avg_output_tokens": avg_output,
            "avg_context_tokens": avg_context,
            "optimal_context_size": optimal_context
        }
 
    def identify_bottlenecks(self, latency_data: Dict, resource_data: Dict, token_data: Dict) -> List[Dict]:
        """Identify performance bottlenecks based on analysis."""
 
        bottlenecks = []
 
        # Latency bottlenecks
        if latency_data["avg_latency"] > self.bottleneck_thresholds["llm_latency"]:
            bottlenecks.append({
                "type": "latency",
                "component": "llm_operations",
                "severity": "critical" if latency_data["avg_latency"] > 10 else "warning",
                "value": latency_data["avg_latency"],
                "threshold": self.bottleneck_thresholds["llm_latency"],
                "description": f"Average LLM latency ({latency_data['avg_latency']:.2f}s) exceeds threshold"
            })
 
        # Memory bottlenecks
        if resource_data["peak_memory"] > self.bottleneck_thresholds["memory_usage"]:
            bottlenecks.append({
                "type": "memory",
                "component": "resource_usage",
                "severity": "critical" if resource_data["peak_memory"] > 2000 else "warning",
                "value": resource_data["peak_memory"],
                "threshold": self.bottleneck_thresholds["memory_usage"],
                "description": f"Peak memory usage ({resource_data['peak_memory']:.0f}MB) exceeds threshold"
            })
 
        # Token efficiency bottlenecks
        if token_data["efficiency"] < self.bottleneck_thresholds["token_efficiency"]:
            bottlenecks.append({
                "type": "token_efficiency",
                "component": "llm_usage",
                "severity": "warning",
                "value": token_data["efficiency"],
                "threshold": self.bottleneck_thresholds["token_efficiency"],
                "description": f"Token efficiency ({token_data['efficiency']:.2f}) below optimal threshold"
            })
 
        # High variance indicates inconsistent performance
        if latency_data["variance"] > 5.0:
            bottlenecks.append({
                "type": "consistency",
                "component": "system_stability",
                "severity": "warning",
                "value": latency_data["variance"],
                "threshold": 5.0,
                "description": f"High latency variance ({latency_data['variance']:.2f}) indicates inconsistent performance"
            })
 
        return bottlenecks
 
    def generate_optimization_recommendations(self, bottlenecks: List[Dict]) -> List[Dict]:
        """Generate specific optimization recommendations based on bottlenecks."""
 
        recommendations = []
 
        for bottleneck in bottlenecks:
            if bottleneck["type"] == "latency":
                recommendations.extend([
                    {
                        "category": "model_optimization",
                        "action": "Consider using a faster model variant",
                        "impact": "high",
                        "implementation_effort": "low",
                        "description": "Switch to a smaller, faster model for non-critical operations"
                    },
                    {
                        "category": "caching",
                        "action": "Implement response caching",
                        "impact": "high",
                        "implementation_effort": "medium",
                        "description": "Cache similar requests to avoid redundant LLM calls"
                    },
                    {
                        "category": "batching",
                        "action": "Implement request batching",
                        "impact": "medium",
                        "implementation_effort": "medium",
                        "description": "Batch multiple requests to improve throughput"
                    }
                ])
 
            elif bottleneck["type"] == "memory":
                recommendations.extend([
                    {
                        "category": "context_optimization",
                        "action": "Optimize context window usage",
                        "impact": "high",
                        "implementation_effort": "medium",
                        "description": "Reduce context size through summarization or filtering"
                    },
                    {
                        "category": "memory_management",
                        "action": "Implement memory pooling",
                        "impact": "medium",
                        "implementation_effort": "high",
                        "description": "Use memory pools to reduce allocation overhead"
                    }
                ])
 
            elif bottleneck["type"] == "token_efficiency":
                recommendations.extend([
                    {
                        "category": "prompt_optimization",
                        "action": "Optimize prompt design",
                        "impact": "high",
                        "implementation_effort": "low",
                        "description": "Reduce prompt length while maintaining effectiveness"
                    },
                    {
                        "category": "context_filtering",
                        "action": "Implement smart context filtering",
                        "impact": "medium",
                        "implementation_effort": "medium",
                        "description": "Only include relevant context for each query"
                    }
                ])
 
        return recommendations

class OptimizationImplementor:
    """Implement performance optimizations based on tracing insights."""
 
    def __init__(self):
        self.optimization_cache = {}
        self.performance_baseline = {}
 
    @noveum_trace.trace_optimization(optimization_type="caching")
    def implement_intelligent_caching(self, cache_strategy: str = "semantic") -> Dict:
        """Implement intelligent caching based on semantic similarity."""
 
        with noveum_trace.trace("optimization.caching") as span:
            span.set_attribute("optimization.strategy", cache_strategy)
            span.set_attribute("optimization.cache_size", len(self.optimization_cache))
 
            if cache_strategy == "semantic":
                return self.implement_semantic_caching()
            elif cache_strategy == "exact_match":
                return self.implement_exact_match_caching()
            elif cache_strategy == "time_based":
                return self.implement_time_based_caching()
 
    def implement_semantic_caching(self) -> Dict:
        """Implement semantic similarity-based caching."""
 
        with noveum_trace.trace("semantic_caching_setup") as span:
            # Set up semantic similarity threshold
            similarity_threshold = 0.85
            span.set_attribute("caching.similarity_threshold", similarity_threshold)
 
            # Initialize embedding model for cache key generation
            embedding_model = initialize_embedding_model()
            span.set_attribute("caching.embedding_model", embedding_model.model_name)
 
            cache_manager = SemanticCacheManager(
                similarity_threshold=similarity_threshold,
                embedding_model=embedding_model
            )
 
            # Test cache performance
            cache_performance = self.test_cache_performance(cache_manager)
 
            span.set_attribute("caching.hit_rate", cache_performance["hit_rate"])
            span.set_attribute("caching.avg_lookup_time", cache_performance["avg_lookup_time"])
            span.set_attribute("caching.memory_usage", cache_performance["memory_usage"])
 
            return {
                "cache_manager": cache_manager,
                "performance_metrics": cache_performance,
                "optimization_impact": self.calculate_optimization_impact(cache_performance)
            }
 
    @noveum_trace.trace_optimization(optimization_type="batching")
    def implement_request_batching(self, batch_size: int = 5, timeout_ms: int = 100) -> Dict:
        """Implement request batching for improved throughput."""
 
        with noveum_trace.trace("optimization.batching") as span:
            span.set_attribute("batching.batch_size", batch_size)
            span.set_attribute("batching.timeout_ms", timeout_ms)
 
            # Set up batching system
            batch_processor = RequestBatchProcessor(
                batch_size=batch_size,
                timeout_ms=timeout_ms
            )
 
            # Measure batching efficiency
            baseline_performance = self.measure_baseline_performance()
            span.set_attribute("batching.baseline_throughput", baseline_performance["throughput"])
 
            # Test batched performance
            batched_performance = self.test_batched_performance(batch_processor)
            span.set_attribute("batching.batched_throughput", batched_performance["throughput"])
 
            # Calculate improvement
            throughput_improvement = (
                (batched_performance["throughput"] - baseline_performance["throughput"]) /
                baseline_performance["throughput"]
            ) * 100
 
            span.set_attribute("batching.throughput_improvement_percent", throughput_improvement)
 
            return {
                "batch_processor": batch_processor,
                "performance_improvement": throughput_improvement,
                "cost_savings": self.calculate_batching_cost_savings(
                    baseline_performance, batched_performance
                )
            }
 
    @noveum_trace.trace_optimization(optimization_type="context_optimization")
    def optimize_context_usage(self, optimization_strategy: str = "smart_truncation") -> Dict:
        """Optimize context window usage for better performance."""
 
        with noveum_trace.trace("optimization.context") as span:
            span.set_attribute("context_optimization.strategy", optimization_strategy)
 
            if optimization_strategy == "smart_truncation":
                return self.implement_smart_truncation()
            elif optimization_strategy == "hierarchical_summarization":
                return self.implement_hierarchical_summarization()
            elif optimization_strategy == "relevance_filtering":
                return self.implement_relevance_filtering()
 
    def implement_smart_truncation(self) -> Dict:
        """Implement smart context truncation based on relevance."""
 
        with noveum_trace.trace("smart_truncation") as span:
            # Analyze current context usage patterns
            context_analysis = self.analyze_context_patterns()
            span.set_attribute("truncation.avg_context_length", context_analysis["avg_length"])
            span.set_attribute("truncation.utilization_rate", context_analysis["utilization"])
 
            # Implement relevance-based truncation
            truncation_strategy = RelevanceTruncationStrategy(
                max_tokens=4000,
                relevance_threshold=0.7,
                preserve_structure=True
            )
 
            # Test truncation effectiveness
            truncation_results = self.test_truncation_strategy(truncation_strategy)
 
            span.set_attribute("truncation.token_savings", truncation_results["token_savings"])
            span.set_attribute("truncation.quality_retention", truncation_results["quality_retention"])
            span.set_attribute("truncation.latency_improvement", truncation_results["latency_improvement"])
 
            return {
                "truncation_strategy": truncation_strategy,
                "token_savings_percent": truncation_results["token_savings_percent"],
                "quality_impact": truncation_results["quality_impact"],
                "cost_savings": truncation_results["cost_savings"]
            }
 
class RelevanceTruncationStrategy:
    """Smart truncation based on content relevance."""
 
    def __init__(self, max_tokens: int, relevance_threshold: float, preserve_structure: bool = True):
        self.max_tokens = max_tokens
        self.relevance_threshold = relevance_threshold
        self.preserve_structure = preserve_structure
 
    @noveum_trace.trace_component(component_type="context_processor")
    def truncate_context(self, context: str, query: str) -> Dict:
        """Truncate context while preserving relevance."""
 
        with noveum_trace.trace("context_truncation") as span:
            span.set_attribute("truncation.original_length", len(context))
            span.set_attribute("truncation.max_tokens", self.max_tokens)
            span.set_attribute("truncation.query", query[:100])  # First 100 chars
 
            # Split context into segments
            segments = self.split_into_segments(context)
            span.set_attribute("truncation.segment_count", len(segments))
 
            # Calculate relevance scores for each segment
            relevance_scores = self.calculate_relevance_scores(segments, query)
            span.set_attribute("truncation.avg_relevance", np.mean(relevance_scores))
 
            # Select most relevant segments within token limit
            selected_segments = self.select_relevant_segments(
                segments, relevance_scores, self.max_tokens
            )
 
            span.set_attribute("truncation.segments_selected", len(selected_segments))
            span.set_attribute("truncation.selection_ratio", len(selected_segments) / len(segments))
 
            # Reconstruct context
            truncated_context = self.reconstruct_context(selected_segments)
 
            span.set_attribute("truncation.final_length", len(truncated_context))
            span.set_attribute("truncation.compression_ratio",
                             len(truncated_context) / len(context))
 
            return {
                "truncated_context": truncated_context,
                "original_segments": len(segments),
                "selected_segments": len(selected_segments),
                "relevance_scores": relevance_scores,
                "compression_ratio": len(truncated_context) / len(context)
            }
 
    def split_into_segments(self, context: str) -> List[str]:
        """Split context into meaningful segments."""
        # Implementation would split by paragraphs, sentences, or semantic chunks
        # This is a simplified version
        sentences = context.split('. ')
        return [sentence.strip() + '.' for sentence in sentences if sentence.strip()]
 
    def calculate_relevance_scores(self, segments: List[str], query: str) -> List[float]:
        """Calculate relevance score for each segment relative to query."""
        # In a real implementation, this would use semantic similarity
        # This is a simplified version using keyword overlap
        query_words = set(query.lower().split())
 
        scores = []
        for segment in segments:
            segment_words = set(segment.lower().split())
            overlap = len(query_words.intersection(segment_words))
            score = overlap / max(len(query_words), 1)
            scores.append(score)
 
        return scores
 
    def select_relevant_segments(self, segments: List[str], scores: List[float], max_tokens: int) -> List[str]:
        """Select most relevant segments within token limit."""
        # Sort segments by relevance score
        segment_scores = list(zip(segments, scores))
        segment_scores.sort(key=lambda x: x[1], reverse=True)
 
        selected = []
        total_tokens = 0
 
        for segment, score in segment_scores:
            segment_tokens = len(segment.split()) * 1.3  # Rough token estimation
 
            if score >= self.relevance_threshold and total_tokens + segment_tokens <= max_tokens:
                selected.append(segment)
                total_tokens += segment_tokens
 
            if total_tokens >= max_tokens:
                break
 
        return selected
 
    def reconstruct_context(self, selected_segments: List[str]) -> str:
        """Reconstruct context from selected segments."""
        if self.preserve_structure:
            # Maintain original order where possible
            return ' '.join(selected_segments)
        else:
            # Order by relevance
            return ' '.join(selected_segments)

class ModelOptimizer:
    """Optimize model selection based on performance requirements."""
 
    def __init__(self):
        self.model_performance_data = {}
        self.cost_performance_matrix = {}
 
    @noveum_trace.trace_optimization(optimization_type="model_selection")
    def optimize_model_selection(self, requirements: Dict) -> Dict:
        """Select optimal model based on performance and cost requirements."""
 
        with noveum_trace.trace("model_optimization") as span:
            span.set_attribute("optimization.latency_requirement", requirements.get("max_latency"))
            span.set_attribute("optimization.quality_requirement", requirements.get("min_quality"))
            span.set_attribute("optimization.cost_constraint", requirements.get("max_cost"))
 
            # Analyze current model performance
            current_performance = self.analyze_current_model_performance()
            span.set_attribute("optimization.current_latency", current_performance["avg_latency"])
            span.set_attribute("optimization.current_quality", current_performance["quality_score"])
            span.set_attribute("optimization.current_cost", current_performance["cost_per_request"])
 
            # Find optimal models
            candidate_models = self.find_candidate_models(requirements)
            span.set_attribute("optimization.candidate_count", len(candidate_models))
 
            # Test candidate models
            optimization_results = {}
            for model in candidate_models:
                test_results = self.test_model_performance(model, requirements)
                optimization_results[model] = test_results
 
            # Select best model
            optimal_model = self.select_optimal_model(optimization_results, requirements)
            span.set_attribute("optimization.selected_model", optimal_model)
 
            # Calculate optimization impact
            optimization_impact = self.calculate_model_optimization_impact(
                current_performance,
                optimization_results[optimal_model]
            )
 
            for metric, improvement in optimization_impact.items():
                span.set_attribute(f"optimization.improvement.{metric}", improvement)
 
            return {
                "optimal_model": optimal_model,
                "performance_improvement": optimization_impact,
                "candidate_results": optimization_results,
                "recommendation_confidence": self.calculate_confidence(optimization_results)
            }
 
    def analyze_current_model_performance(self) -> Dict:
        """Analyze current model performance metrics."""
 
        with noveum_trace.trace("current_model_analysis") as span:
            # Collect performance data from recent traces
            recent_traces = self.get_recent_trace_data(hours=24)
 
            if not recent_traces:
                return {"avg_latency": 0, "quality_score": 0, "cost_per_request": 0}
 
            latencies = [trace["latency"] for trace in recent_traces]
            quality_scores = [trace["quality_score"] for trace in recent_traces if "quality_score" in trace]
            token_costs = [trace["token_cost"] for trace in recent_traces if "token_cost" in trace]
 
            performance = {
                "avg_latency": np.mean(latencies),
                "p95_latency": np.percentile(latencies, 95),
                "quality_score": np.mean(quality_scores) if quality_scores else 0,
                "cost_per_request": np.mean(token_costs) if token_costs else 0,
                "throughput": len(recent_traces) / 24  # requests per hour
            }
 
            span.set_attribute("analysis.trace_count", len(recent_traces))
            span.set_attribute("analysis.avg_latency", performance["avg_latency"])
            span.set_attribute("analysis.quality_score", performance["quality_score"])
 
            return performance
 
    def find_candidate_models(self, requirements: Dict) -> List[str]:
        """Find candidate models that meet basic requirements."""
 
        candidate_models = []
        model_catalog = self.get_model_catalog()
 
        for model_name, model_specs in model_catalog.items():
            # Check if model meets basic requirements
            if (model_specs["max_latency"] <= requirements.get("max_latency", float('inf')) and
                model_specs["min_quality"] >= requirements.get("min_quality", 0) and
                model_specs["cost_per_token"] <= requirements.get("max_cost_per_token", float('inf'))):
                candidate_models.append(model_name)
 
        return candidate_models
 
    def test_model_performance(self, model_name: str, requirements: Dict) -> Dict:
        """Test model performance with sample workload."""
 
        with noveum_trace.trace(f"model_test.{model_name}") as span:
            span.set_attribute("test.model_name", model_name)
 
            # Run test queries
            test_queries = self.get_test_queries()
            results = []
 
            for query in test_queries:
                start_time = time.time()
 
                try:
                    response = self.execute_test_query(model_name, query)
                    latency = time.time() - start_time
 
                    # Calculate quality score
                    quality_score = self.calculate_quality_score(query, response)
 
                    # Calculate cost
                    cost = self.calculate_query_cost(model_name, query, response)
 
                    results.append({
                        "latency": latency,
                        "quality_score": quality_score,
                        "cost": cost,
                        "success": True
                    })
 
                except Exception as e:
                    results.append({
                        "latency": time.time() - start_time,
                        "quality_score": 0,
                        "cost": 0,
                        "success": False,
                        "error": str(e)
                    })
 
            # Aggregate results
            successful_results = [r for r in results if r["success"]]
 
            if successful_results:
                performance = {
                    "avg_latency": np.mean([r["latency"] for r in successful_results]),
                    "p95_latency": np.percentile([r["latency"] for r in successful_results], 95),
                    "avg_quality": np.mean([r["quality_score"] for r in successful_results]),
                    "avg_cost": np.mean([r["cost"] for r in successful_results]),
                    "success_rate": len(successful_results) / len(results),
                    "total_tests": len(results)
                }
            else:
                performance = {
                    "avg_latency": float('inf'),
                    "p95_latency": float('inf'),
                    "avg_quality": 0,
                    "avg_cost": float('inf'),
                    "success_rate": 0,
                    "total_tests": len(results)
                }
 
            # Log performance metrics
            for metric, value in performance.items():
                span.set_attribute(f"test.{metric}", value)
 
            return performance

class ResourceOptimizer:
    """Optimize resource usage based on tracing insights."""
 
    @noveum_trace.trace_optimization(optimization_type="resource_usage")
    def optimize_resource_allocation(self, workload_profile: Dict) -> Dict:
        """Optimize resource allocation based on workload characteristics."""
 
        with noveum_trace.trace("resource_optimization") as span:
            span.set_attribute("optimization.workload_type", workload_profile.get("type"))
            span.set_attribute("optimization.peak_qps", workload_profile.get("peak_qps"))
            span.set_attribute("optimization.avg_request_size", workload_profile.get("avg_request_size"))
 
            # Analyze memory usage patterns
            memory_optimization = self.optimize_memory_usage(workload_profile)
 
            # Analyze CPU usage patterns
            cpu_optimization = self.optimize_cpu_usage(workload_profile)
 
            # Optimize connection pooling
            connection_optimization = self.optimize_connection_pooling(workload_profile)
 
            # Calculate overall resource savings
            total_savings = self.calculate_total_resource_savings(
                memory_optimization,
                cpu_optimization,
                connection_optimization
            )
 
            span.set_attribute("optimization.memory_savings_percent", memory_optimization["savings_percent"])
            span.set_attribute("optimization.cpu_savings_percent", cpu_optimization["savings_percent"])
            span.set_attribute("optimization.total_cost_savings", total_savings["cost_savings"])
 
            return {
                "memory_optimization": memory_optimization,
                "cpu_optimization": cpu_optimization,
                "connection_optimization": connection_optimization,
                "total_savings": total_savings,
                "implementation_plan": self.generate_implementation_plan(
                    memory_optimization, cpu_optimization, connection_optimization
                )
            }
 
    def optimize_memory_usage(self, workload_profile: Dict) -> Dict:
        """Optimize memory usage patterns."""
 
        with noveum_trace.trace("memory_optimization") as span:
            # Analyze current memory patterns
            memory_analysis = self.analyze_memory_patterns(workload_profile)
 
            # Identify memory waste
            memory_waste = self.identify_memory_waste(memory_analysis)
            span.set_attribute("memory.waste_sources", len(memory_waste))
 
            # Generate memory optimization strategies
            optimization_strategies = []
 
            if memory_waste.get("context_bloat", 0) > 0.2:  # 20% waste
                optimization_strategies.append({
                    "strategy": "context_compression",
                    "potential_savings": memory_waste["context_bloat"] * 100,
                    "implementation_effort": "medium",
                    "description": "Implement context compression to reduce memory footprint"
                })
 
            if memory_waste.get("cache_inefficiency", 0) > 0.15:  # 15% waste
                optimization_strategies.append({
                    "strategy": "cache_optimization",
                    "potential_savings": memory_waste["cache_inefficiency"] * 100,
                    "implementation_effort": "low",
                    "description": "Optimize cache eviction policies and size limits"
                })
 
            # Calculate total potential savings
            total_savings = sum(strategy["potential_savings"] for strategy in optimization_strategies)
 
            span.set_attribute("memory.optimization_strategies", len(optimization_strategies))
            span.set_attribute("memory.potential_savings", total_savings)
 
            return {
                "current_usage": memory_analysis,
                "waste_analysis": memory_waste,
                "optimization_strategies": optimization_strategies,
                "savings_percent": total_savings,
                "implementation_priority": self.prioritize_memory_optimizations(optimization_strategies)
            }
 
# Automated Performance Monitoring
class AutomatedPerformanceMonitor:
    """Continuously monitor and optimize performance."""
 
    def __init__(self):
        self.performance_baselines = {}
        self.optimization_triggers = {}
        self.auto_optimization_enabled = True
 
    @noveum_trace.trace_monitoring(monitor_type="automated_performance")
    def setup_automated_monitoring(self, monitoring_config: Dict) -> Dict:
        """Set up automated performance monitoring and optimization."""
 
        with noveum_trace.trace("automated_monitoring_setup") as span:
            span.set_attribute("monitoring.auto_optimization", self.auto_optimization_enabled)
            span.set_attribute("monitoring.check_interval", monitoring_config.get("check_interval"))
 
            # Set up performance thresholds
            self.setup_performance_thresholds(monitoring_config.get("thresholds", {}))
 
            # Set up automated triggers
            self.setup_optimization_triggers(monitoring_config.get("triggers", {}))
 
            # Set up alerting
            alerting_config = self.setup_performance_alerting(monitoring_config.get("alerting", {}))
 
            span.set_attribute("monitoring.thresholds_configured", len(self.performance_baselines))
            span.set_attribute("monitoring.triggers_configured", len(self.optimization_triggers))
 
            return {
                "monitoring_active": True,
                "thresholds": self.performance_baselines,
                "triggers": self.optimization_triggers,
                "alerting": alerting_config
            }
 
    def setup_performance_thresholds(self, thresholds: Dict):
        """Set up performance monitoring thresholds."""
 
        default_thresholds = {
            "latency_p95": 5.0,        # 5 seconds
            "latency_p99": 10.0,       # 10 seconds
            "error_rate": 0.01,        # 1%
            "token_efficiency": 0.7,   # 70%
            "cost_per_request": 0.05,  # $0.05
            "memory_usage": 1000,      # 1GB
            "cpu_usage": 80            # 80%
        }
 
        self.performance_baselines = {**default_thresholds, **thresholds}
 
    def setup_optimization_triggers(self, triggers: Dict):
        """Set up automated optimization triggers."""
 
        default_triggers = {
            "latency_degradation": {
                "threshold": 1.5,  # 50% increase from baseline
                "action": "enable_caching",
                "cooldown": 3600   # 1 hour
            },
            "cost_spike": {
                "threshold": 1.3,  # 30% increase from baseline
                "action": "optimize_context",
                "cooldown": 1800   # 30 minutes
            },
            "memory_pressure": {
                "threshold": 0.9,  # 90% of available memory
                "action": "trigger_gc",
                "cooldown": 300    # 5 minutes
            }
        }
 
        self.optimization_triggers = {**default_triggers, **triggers}
 
    @noveum_trace.trace_monitoring(monitor_type="performance_check")
    def perform_automated_performance_check(self) -> Dict:
        """Perform automated performance check and trigger optimizations."""
 
        with noveum_trace.trace("automated_performance_check") as span:
            # Collect current performance metrics
            current_metrics = self.collect_current_metrics()
 
            # Compare against baselines
            performance_analysis = self.analyze_performance_against_baselines(current_metrics)
 
            # Check for optimization triggers
            triggered_optimizations = self.check_optimization_triggers(performance_analysis)
 
            span.set_attribute("check.metrics_collected", len(current_metrics))
            span.set_attribute("check.threshold_violations", len(performance_analysis["violations"]))
            span.set_attribute("check.optimizations_triggered", len(triggered_optimizations))
 
            # Execute triggered optimizations
            if self.auto_optimization_enabled and triggered_optimizations:
                optimization_results = self.execute_triggered_optimizations(triggered_optimizations)
                span.set_attribute("check.optimizations_executed", len(optimization_results))
            else:
                optimization_results = []
 
            return {
                "current_metrics": current_metrics,
                "performance_analysis": performance_analysis,
                "triggered_optimizations": triggered_optimizations,
                "optimization_results": optimization_results,
                "recommendations": self.generate_manual_recommendations(performance_analysis)
            }

# Always establish baselines before optimization
@noveum_trace.trace_baseline(baseline_type="performance")
def establish_performance_baseline():
    """Establish performance baselines for optimization comparison."""
 
    baseline_metrics = {
        "latency_percentiles": measure_latency_percentiles(),
        "throughput": measure_throughput(),
        "resource_usage": measure_resource_usage(),
        "cost_metrics": measure_cost_metrics(),
        "quality_scores": measure_quality_scores()
    }
 
    return baseline_metrics

# Implement optimizations gradually with A/B testing
@noveum_trace.trace_experiment(experiment_type="optimization_ab_test")
def gradual_optimization_rollout(optimization_config: Dict):
    """Gradually roll out optimizations with monitoring."""
 
    # Start with small traffic percentage
    traffic_percentages = [5, 10, 25, 50, 100]
 
    for percentage in traffic_percentages:
        results = test_optimization_with_traffic(optimization_config, percentage)
 
        if results["performance_improvement"] > 0 and results["error_rate"] < 0.01:
            continue  # Proceed to next percentage
        else:
            rollback_optimization()
            break

# Always monitor the impact of optimizations
@noveum_trace.trace_optimization_impact(optimization_id="context_optimization_v1")
def monitor_optimization_impact(optimization_id: str, duration_hours: int = 24):
    """Monitor the impact of applied optimizations."""
 
    # Collect post-optimization metrics
    post_optimization_metrics = collect_metrics_for_period(duration_hours)
 
    # Compare with baseline
    impact_analysis = compare_with_baseline(post_optimization_metrics)
 
    # Generate impact report
    return generate_optimization_impact_report(impact_analysis)