oxirs-chat 0.2.4

use crate::llm::types::{ChatMessage, ChatRole, LLMRequest, LLMResponse, Priority, Usage, UseCase};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::RwLock;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceConfig {
    pub enable_caching: bool,
    pub enable_batching: bool,
    pub enable_compression: bool,
    pub enable_prefetching: bool,
    pub enable_load_balancing: bool,
    pub cache_ttl: Duration,
    pub batch_size: usize,
    pub batch_timeout: Duration,
    pub compression_threshold: usize,
    pub prefetch_window: usize,
    pub load_balance_strategy: LoadBalanceStrategy,
    pub optimization_targets: OptimizationTargets,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LoadBalanceStrategy {
    RoundRobin,
    LeastConnections,
    WeightedRoundRobin,
    LatencyBased,
    ResourceBased,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationTargets {
    pub target_latency_ms: u64,
    pub target_throughput_rps: u64,
    pub target_cache_hit_rate: f32,
    pub target_memory_usage_mb: u64,
    pub target_cpu_usage_percent: f32,
    pub target_error_rate: f32,
}

impl Default for PerformanceConfig {
    fn default() -> Self {
        Self {
            enable_caching: true,
            enable_batching: true,
            enable_compression: true,
            enable_prefetching: false,
            enable_load_balancing: true,
            cache_ttl: Duration::from_secs(3600), // 1 hour
            batch_size: 10,
            batch_timeout: Duration::from_millis(100),
            compression_threshold: 1024, // 1KB
            prefetch_window: 5,
            load_balance_strategy: LoadBalanceStrategy::Adaptive,
            optimization_targets: OptimizationTargets {
                target_latency_ms: 1000,
                target_throughput_rps: 100,
                target_cache_hit_rate: 0.8,
                target_memory_usage_mb: 512,
                target_cpu_usage_percent: 70.0,
                target_error_rate: 0.01,
            },
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub latency_p50: Duration,
    pub latency_p95: Duration,
    pub latency_p99: Duration,
    pub throughput_rps: f64,
    pub cache_hit_rate: f32,
    pub cache_miss_rate: f32,
    pub memory_usage_mb: f64,
    pub cpu_usage_percent: f32,
    pub error_rate: f32,
    pub request_count: u64,
    pub total_bytes_processed: u64,
    pub average_response_size: f64,
    pub concurrent_requests: u32,
    pub queue_depth: u32,
    pub processing_efficiency: f32,
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            latency_p50: Duration::from_millis(0),
            latency_p95: Duration::from_millis(0),
            latency_p99: Duration::from_millis(0),
            throughput_rps: 0.0,
            cache_hit_rate: 0.0,
            cache_miss_rate: 0.0,
            memory_usage_mb: 0.0,
            cpu_usage_percent: 0.0,
            error_rate: 0.0,
            request_count: 0,
            total_bytes_processed: 0,
            average_response_size: 0.0,
            concurrent_requests: 0,
            queue_depth: 0,
            processing_efficiency: 0.0,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheEntry {
    pub response: LLMResponse,
    pub created_at: SystemTime,
    pub access_count: u64,
    pub last_accessed: SystemTime,
    pub compression_ratio: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BatchRequest {
    pub requests: Vec<LLMRequest>,
    pub batch_id: String,
    #[serde(skip, default = "Instant::now")]
    pub created_at: Instant,
    pub priority: BatchPriority,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BatchPriority {
    Low,
    Normal,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LoadBalancerState {
    pub endpoints: Vec<EndpointInfo>,
    pub current_index: usize,
    pub request_counts: HashMap<String, u64>,
    pub latency_history: HashMap<String, Vec<Duration>>,
    pub error_counts: HashMap<String, u64>,
    pub last_health_check: SystemTime,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EndpointInfo {
    pub id: String,
    pub url: String,
    pub weight: f32,
    pub is_healthy: bool,
    pub current_connections: u32,
    pub average_latency: Duration,
    pub error_rate: f32,
    pub last_success: Option<SystemTime>,
    pub last_failure: Option<SystemTime>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub optimization_type: OptimizationType,
    pub description: String,
    pub expected_improvement: f32,
    pub implementation_effort: ImplementationEffort,
    pub priority: RecommendationPriority,
    pub estimated_impact: PerformanceImpact,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    CacheOptimization,
    BatchingImprovement,
    CompressionTuning,
    LoadBalancing,
    ResourceScaling,
    QueryOptimization,
    ModelSelection,
    MemoryOptimization,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImplementationEffort {
    Low,
    Medium,
    High,
    Complex,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationPriority {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceImpact {
    pub latency_improvement_percent: f32,
    pub throughput_improvement_percent: f32,
    pub memory_reduction_percent: f32,
    pub cost_reduction_percent: f32,
    pub reliability_improvement_percent: f32,
}

pub struct PerformanceOptimizer {
    config: PerformanceConfig,
    cache: Arc<RwLock<HashMap<String, CacheEntry>>>,
    batch_queue: Arc<RwLock<Vec<BatchRequest>>>,
    load_balancer: Arc<RwLock<LoadBalancerState>>,
    metrics_history: Arc<RwLock<Vec<PerformanceMetrics>>>,
    compression_engine: Arc<RwLock<CompressionEngine>>,
    prefetch_engine: Arc<RwLock<PrefetchEngine>>,
    benchmark_results: Arc<RwLock<Vec<BenchmarkResult>>>,
}

impl PerformanceOptimizer {
    pub fn new(config: PerformanceConfig) -> Self {
        Self {
            config,
            cache: Arc::new(RwLock::new(HashMap::new())),
            batch_queue: Arc::new(RwLock::new(Vec::new())),
            load_balancer: Arc::new(RwLock::new(LoadBalancerState {
                endpoints: Vec::new(),
                current_index: 0,
                request_counts: HashMap::new(),
                latency_history: HashMap::new(),
                error_counts: HashMap::new(),
                last_health_check: SystemTime::now(),
            })),
            metrics_history: Arc::new(RwLock::new(Vec::new())),
            compression_engine: Arc::new(RwLock::new(CompressionEngine::new())),
            prefetch_engine: Arc::new(RwLock::new(PrefetchEngine::new())),
            benchmark_results: Arc::new(RwLock::new(Vec::new())),
        }
    }

    pub async fn optimize_request(
        &self,
        request: &LLMRequest,
    ) -> Result<OptimizedRequest, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();

        // Check cache first
        if self.config.enable_caching {
            if let Some(cached_response) = self.get_cached_response(request).await? {
                return Ok(OptimizedRequest {
                    request: request.clone(),
                    cached_response: Some(cached_response),
                    should_batch: false,
                    should_compress: false,
                    optimization_applied: vec![OptimizationType::CacheOptimization],
                    estimated_savings: EstimatedSavings {
                        time_saved: Duration::from_millis(800),
                        cost_saved: 0.95,
                        resources_saved: 0.9,
                    },
                });
            }
        }

        let mut optimization_applied = Vec::new();
        let mut optimized_request = request.clone();

        // Apply compression if beneficial
        if self.config.enable_compression {
            if let Some(compressed) = self.apply_compression(&optimized_request).await? {
                optimized_request = compressed;
                optimization_applied.push(OptimizationType::CompressionTuning);
            }
        }

        // Determine if batching would be beneficial
        let should_batch =
            self.config.enable_batching && self.should_batch_request(request).await?;
        if should_batch {
            optimization_applied.push(OptimizationType::BatchingImprovement);
        }

        // Apply query optimization
        if let Some(optimized) = self.optimize_query(&optimized_request).await? {
            optimized_request = optimized;
            optimization_applied.push(OptimizationType::QueryOptimization);
        }

        // Calculate estimated savings
        let processing_time = start_time.elapsed();
        let estimated_savings = self
            .calculate_estimated_savings(&optimization_applied, processing_time)
            .await?;

        Ok(OptimizedRequest {
            request: optimized_request,
            cached_response: None,
            should_batch,
            should_compress: self.config.enable_compression,
            optimization_applied,
            estimated_savings,
        })
    }

    async fn get_cached_response(
        &self,
        request: &LLMRequest,
    ) -> Result<Option<LLMResponse>, Box<dyn std::error::Error + Send + Sync>> {
        let cache_key = self.generate_cache_key(request)?;
        let cache = self.cache.read().await;

        if let Some(entry) = cache.get(&cache_key) {
            // Check TTL
            if entry.created_at.elapsed().unwrap_or(Duration::MAX) < self.config.cache_ttl {
                // Clone the response before dropping the cache
                let response = entry.response.clone();
                // Update access statistics
                drop(cache);
                let mut cache_write = self.cache.write().await;
                if let Some(entry_mut) = cache_write.get_mut(&cache_key) {
                    entry_mut.access_count += 1;
                    entry_mut.last_accessed = SystemTime::now();
                }
                return Ok(Some(response));
            }
        }

        Ok(None)
    }

    async fn apply_compression(
        &self,
        request: &LLMRequest,
    ) -> Result<Option<LLMRequest>, Box<dyn std::error::Error + Send + Sync>> {
        let prompt_content = request
            .messages
            .iter()
            .map(|msg| msg.content.as_str())
            .collect::<Vec<_>>()
            .join("\n");
        let prompt_size = prompt_content.len();
        if prompt_size > self.config.compression_threshold {
            let compression_engine = self.compression_engine.read().await;
            let compressed_prompt = compression_engine.compress(&prompt_content)?;

            if compressed_prompt.len() < prompt_size {
                let mut optimized_request = request.clone();
                if let Some(first_msg) = optimized_request.messages.first_mut() {
                    first_msg.content = compressed_prompt;
                }
                return Ok(Some(optimized_request));
            }
        }
        Ok(None)
    }

    async fn should_batch_request(
        &self,
        _request: &LLMRequest,
    ) -> Result<bool, Box<dyn std::error::Error + Send + Sync>> {
        let queue = self.batch_queue.read().await;
        Ok(queue.len() < self.config.batch_size)
    }

    async fn optimize_query(
        &self,
        request: &LLMRequest,
    ) -> Result<Option<LLMRequest>, Box<dyn std::error::Error + Send + Sync>> {
        // Simple query optimization - could be enhanced with more sophisticated logic
        let prompt_content = request
            .messages
            .iter()
            .map(|msg| msg.content.as_str())
            .collect::<Vec<_>>()
            .join("\n");
        let optimized_prompt = self.apply_prompt_optimization(&prompt_content)?;

        if optimized_prompt != prompt_content {
            let mut optimized_request = request.clone();
            if let Some(first_msg) = optimized_request.messages.first_mut() {
                first_msg.content = optimized_prompt;
            }
            return Ok(Some(optimized_request));
        }

        Ok(None)
    }

    fn apply_prompt_optimization(
        &self,
        prompt: &str,
    ) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
        // Remove redundant whitespace
        let optimized = prompt
            .lines()
            .map(|line| line.trim())
            .filter(|line| !line.is_empty())
            .collect::<Vec<_>>()
            .join("\n");

        // Remove repetitive phrases
        let optimized = self.remove_repetitive_phrases(&optimized)?;

        Ok(optimized)
    }

    fn remove_repetitive_phrases(
        &self,
        text: &str,
    ) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
        // Simple deduplication - could be enhanced with more sophisticated algorithms
        let words: Vec<&str> = text.split_whitespace().collect();
        let mut deduplicated = Vec::new();
        let mut seen_phrases = std::collections::HashSet::new();

        for window in words.windows(3) {
            let phrase = window.join(" ");
            if !seen_phrases.contains(&phrase) {
                seen_phrases.insert(phrase);
                if deduplicated.is_empty() {
                    deduplicated.extend_from_slice(window);
                } else {
                    deduplicated.push(window[window.len() - 1]);
                }
            }
        }

        Ok(deduplicated.join(" "))
    }

    async fn calculate_estimated_savings(
        &self,
        optimizations: &[OptimizationType],
        _processing_time: Duration,
    ) -> Result<EstimatedSavings, Box<dyn std::error::Error + Send + Sync>> {
        let mut time_saved = Duration::from_millis(0);
        let mut cost_saved = 0.0;
        let mut resources_saved = 0.0;

        for optimization in optimizations {
            match optimization {
                OptimizationType::CacheOptimization => {
                    time_saved += Duration::from_millis(800);
                    cost_saved += 0.95;
                    resources_saved += 0.9;
                }
                OptimizationType::CompressionTuning => {
                    time_saved += Duration::from_millis(50);
                    cost_saved += 0.1;
                    resources_saved += 0.15;
                }
                OptimizationType::BatchingImprovement => {
                    time_saved += Duration::from_millis(200);
                    cost_saved += 0.2;
                    resources_saved += 0.25;
                }
                OptimizationType::QueryOptimization => {
                    time_saved += Duration::from_millis(100);
                    cost_saved += 0.05;
                    resources_saved += 0.1;
                }
                _ => {}
            }
        }

        Ok(EstimatedSavings {
            time_saved,
            cost_saved,
            resources_saved,
        })
    }

    pub async fn benchmark_system(
        &self,
        test_config: BenchmarkConfig,
    ) -> Result<BenchmarkResult, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();
        let mut latencies = Vec::new();
        let mut successful_requests = 0;
        let mut failed_requests = 0;
        let mut total_bytes = 0;

        // Create test requests
        let test_requests = self.generate_test_requests(&test_config)?;

        // Execute benchmark
        for (i, request) in test_requests.iter().enumerate() {
            let request_start = Instant::now();

            match self.execute_test_request(request).await {
                Ok(response) => {
                    successful_requests += 1;
                    total_bytes += response.content.len() as u64;
                    latencies.push(request_start.elapsed());
                }
                Err(_) => {
                    failed_requests += 1;
                }
            }

            // Add delay between requests if specified
            if let Some(delay) = test_config.request_delay {
                tokio::time::sleep(delay).await;
            }

            // Progress reporting
            if i % 10 == 0 {
                println!("Benchmark progress: {}/{}", i + 1, test_requests.len());
            }
        }

        // Calculate statistics
        latencies.sort();
        let total_duration = start_time.elapsed();
        let total_requests = successful_requests + failed_requests;

        let result = BenchmarkResult {
            test_name: test_config.test_name,
            total_requests,
            successful_requests,
            failed_requests,
            total_duration,
            throughput_rps: successful_requests as f64 / total_duration.as_secs_f64(),
            latency_p50: latencies
                .get(latencies.len() / 2)
                .copied()
                .unwrap_or_default(),
            latency_p95: latencies
                .get((latencies.len() * 95) / 100)
                .copied()
                .unwrap_or_default(),
            latency_p99: latencies
                .get((latencies.len() * 99) / 100)
                .copied()
                .unwrap_or_default(),
            error_rate: failed_requests as f32 / total_requests as f32,
            total_bytes_transferred: total_bytes,
            memory_usage_peak: self.get_memory_usage()?,
            cpu_usage_average: self.get_cpu_usage()?,
            cache_hit_rate: self.calculate_cache_hit_rate().await?,
            optimization_effectiveness: self.calculate_optimization_effectiveness().await?,
        };

        // Store result
        self.benchmark_results.write().await.push(result.clone());

        Ok(result)
    }

    fn generate_test_requests(
        &self,
        config: &BenchmarkConfig,
    ) -> Result<Vec<LLMRequest>, Box<dyn std::error::Error + Send + Sync>> {
        let mut requests = Vec::new();

        for i in 0..config.request_count {
            let request = LLMRequest {
                messages: vec![ChatMessage {
                    role: ChatRole::User,
                    content: format!("Test prompt {i} for benchmarking performance"),
                    metadata: None,
                }],
                max_tokens: Some(100),
                temperature: 0.7,
                system_prompt: Some(
                    "You are a helpful assistant for performance testing.".to_string(),
                ),
                use_case: UseCase::SimpleQuery,
                priority: Priority::Normal,
                timeout: None,
            };
            requests.push(request);
        }

        Ok(requests)
    }

    async fn execute_test_request(
        &self,
        request: &LLMRequest,
    ) -> Result<LLMResponse, Box<dyn std::error::Error + Send + Sync>> {
        // Simulate LLM response for benchmarking
        tokio::time::sleep(Duration::from_millis(100)).await;

        let prompt_content = request
            .messages
            .iter()
            .map(|msg| msg.content.as_str())
            .collect::<Vec<_>>()
            .join("\n");

        Ok(LLMResponse {
            content: format!(
                "Test response for: {}",
                prompt_content.chars().take(50).collect::<String>()
            ),
            usage: Usage {
                prompt_tokens: prompt_content.len() / 4,
                completion_tokens: 25,
                total_tokens: (prompt_content.len() / 4) + 25,
                cost: 0.001,
            },
            model_used: "test-model".to_string(),
            provider_used: "test-provider".to_string(),
            latency: Duration::from_millis(100),
            quality_score: Some(0.8),
            metadata: std::collections::HashMap::new(),
        })
    }

    fn get_memory_usage(&self) -> Result<u64, Box<dyn std::error::Error + Send + Sync>> {
        // Placeholder implementation - would use actual system metrics in production
        Ok(256 * 1024 * 1024) // 256 MB
    }

    fn get_cpu_usage(&self) -> Result<f32, Box<dyn std::error::Error + Send + Sync>> {
        // Placeholder implementation - would use actual CPU metrics in production
        Ok(45.0) // 45%
    }

    async fn calculate_cache_hit_rate(
        &self,
    ) -> Result<f32, Box<dyn std::error::Error + Send + Sync>> {
        let cache = self.cache.read().await;
        if cache.is_empty() {
            return Ok(0.0);
        }

        let total_accesses: u64 = cache.values().map(|entry| entry.access_count).sum();
        let cache_hits = cache.len() as u64;

        Ok(cache_hits as f32 / total_accesses as f32)
    }

    async fn calculate_optimization_effectiveness(
        &self,
    ) -> Result<f32, Box<dyn std::error::Error + Send + Sync>> {
        // Calculate based on various optimization metrics
        let cache_effectiveness = self.calculate_cache_hit_rate().await?;
        let compression_effectiveness = self.calculate_compression_effectiveness().await?;
        let batch_effectiveness = self.calculate_batch_effectiveness().await?;

        Ok((cache_effectiveness + compression_effectiveness + batch_effectiveness) / 3.0)
    }

    async fn calculate_compression_effectiveness(
        &self,
    ) -> Result<f32, Box<dyn std::error::Error + Send + Sync>> {
        let compression_engine = self.compression_engine.read().await;
        Ok(compression_engine.get_average_compression_ratio())
    }

    async fn calculate_batch_effectiveness(
        &self,
    ) -> Result<f32, Box<dyn std::error::Error + Send + Sync>> {
        let queue = self.batch_queue.read().await;
        if queue.is_empty() {
            return Ok(0.0);
        }

        let average_batch_size = queue
            .iter()
            .map(|batch| batch.requests.len())
            .sum::<usize>() as f32
            / queue.len() as f32;
        Ok(average_batch_size / self.config.batch_size as f32)
    }

    pub async fn generate_optimization_recommendations(
        &self,
    ) -> Result<Vec<OptimizationRecommendation>, Box<dyn std::error::Error + Send + Sync>> {
        let mut recommendations = Vec::new();
        let current_metrics = self.get_current_metrics().await?;

        // Cache optimization recommendations
        if current_metrics.cache_hit_rate < self.config.optimization_targets.target_cache_hit_rate {
            recommendations.push(OptimizationRecommendation {
                optimization_type: OptimizationType::CacheOptimization,
                description:
                    "Increase cache size or improve cache eviction strategy to improve hit rate"
                        .to_string(),
                expected_improvement: (self.config.optimization_targets.target_cache_hit_rate
                    - current_metrics.cache_hit_rate)
                    * 100.0,
                implementation_effort: ImplementationEffort::Low,
                priority: RecommendationPriority::High,
                estimated_impact: PerformanceImpact {
                    latency_improvement_percent: 25.0,
                    throughput_improvement_percent: 15.0,
                    memory_reduction_percent: 0.0,
                    cost_reduction_percent: 30.0,
                    reliability_improvement_percent: 10.0,
                },
            });
        }

        // Latency optimization recommendations
        if current_metrics.latency_p95
            > Duration::from_millis(self.config.optimization_targets.target_latency_ms)
        {
            recommendations.push(OptimizationRecommendation {
                optimization_type: OptimizationType::LoadBalancing,
                description: "Implement better load balancing to reduce latency spikes".to_string(),
                expected_improvement: 20.0,
                implementation_effort: ImplementationEffort::Medium,
                priority: RecommendationPriority::High,
                estimated_impact: PerformanceImpact {
                    latency_improvement_percent: 30.0,
                    throughput_improvement_percent: 20.0,
                    memory_reduction_percent: 0.0,
                    cost_reduction_percent: 10.0,
                    reliability_improvement_percent: 25.0,
                },
            });
        }

        // Memory optimization recommendations
        if current_metrics.memory_usage_mb
            > self.config.optimization_targets.target_memory_usage_mb as f64
        {
            recommendations.push(OptimizationRecommendation {
                optimization_type: OptimizationType::MemoryOptimization,
                description:
                    "Optimize memory usage through better caching strategies and data compression"
                        .to_string(),
                expected_improvement: 15.0,
                implementation_effort: ImplementationEffort::Medium,
                priority: RecommendationPriority::Medium,
                estimated_impact: PerformanceImpact {
                    latency_improvement_percent: 10.0,
                    throughput_improvement_percent: 5.0,
                    memory_reduction_percent: 25.0,
                    cost_reduction_percent: 15.0,
                    reliability_improvement_percent: 15.0,
                },
            });
        }

        // Throughput optimization recommendations
        if current_metrics.throughput_rps
            < self.config.optimization_targets.target_throughput_rps as f64
        {
            recommendations.push(OptimizationRecommendation {
                optimization_type: OptimizationType::BatchingImprovement,
                description: "Improve request batching to increase overall throughput".to_string(),
                expected_improvement: 35.0,
                implementation_effort: ImplementationEffort::Low,
                priority: RecommendationPriority::High,
                estimated_impact: PerformanceImpact {
                    latency_improvement_percent: 5.0,
                    throughput_improvement_percent: 40.0,
                    memory_reduction_percent: 10.0,
                    cost_reduction_percent: 20.0,
                    reliability_improvement_percent: 5.0,
                },
            });
        }

        Ok(recommendations)
    }

    async fn get_current_metrics(
        &self,
    ) -> Result<PerformanceMetrics, Box<dyn std::error::Error + Send + Sync>> {
        // Return latest metrics or compute current state
        let history = self.metrics_history.read().await;
        Ok(history.last().cloned().unwrap_or_default())
    }

    pub async fn get_performance_report(
        &self,
    ) -> Result<PerformanceReport, Box<dyn std::error::Error + Send + Sync>> {
        let current_metrics = self.get_current_metrics().await?;
        let benchmark_results = self.benchmark_results.read().await.clone();
        let recommendations = self.generate_optimization_recommendations().await?;
        let cache_stats = self.get_cache_statistics().await?;
        let compression_stats = self.get_compression_statistics().await?;

        Ok(PerformanceReport {
            current_metrics,
            benchmark_results,
            recommendations,
            cache_statistics: cache_stats,
            compression_statistics: compression_stats,
            optimization_summary: self.generate_optimization_summary().await?,
            generated_at: SystemTime::now(),
        })
    }

    async fn get_cache_statistics(
        &self,
    ) -> Result<CacheStatistics, Box<dyn std::error::Error + Send + Sync>> {
        let cache = self.cache.read().await;

        let total_entries = cache.len();
        let total_size_bytes: usize = cache
            .values()
            .map(|entry| entry.response.content.len())
            .sum();
        let total_access_count: u64 = cache.values().map(|entry| entry.access_count).sum();
        let average_compression_ratio = cache
            .values()
            .map(|entry| entry.compression_ratio)
            .sum::<f32>()
            / cache.len() as f32;

        Ok(CacheStatistics {
            total_entries,
            total_size_bytes,
            hit_rate: self.calculate_cache_hit_rate().await?,
            miss_rate: 1.0 - self.calculate_cache_hit_rate().await?,
            eviction_count: 0, // Would track in production
            average_access_count: total_access_count as f64 / total_entries as f64,
            average_compression_ratio,
        })
    }

    async fn get_compression_statistics(
        &self,
    ) -> Result<CompressionStatistics, Box<dyn std::error::Error + Send + Sync>> {
        let compression_engine = self.compression_engine.read().await;

        Ok(CompressionStatistics {
            total_compressed_requests: compression_engine.get_compression_count(),
            average_compression_ratio: compression_engine.get_average_compression_ratio(),
            total_bytes_saved: compression_engine.get_total_bytes_saved(),
            compression_time_average: compression_engine.get_average_compression_time(),
        })
    }

    async fn generate_optimization_summary(
        &self,
    ) -> Result<OptimizationSummary, Box<dyn std::error::Error + Send + Sync>> {
        let current_metrics = self.get_current_metrics().await?;
        let targets = &self.config.optimization_targets;

        Ok(OptimizationSummary {
            overall_performance_score: self.calculate_performance_score(&current_metrics, targets),
            target_achievement_rate: self
                .calculate_target_achievement_rate(&current_metrics, targets),
            bottleneck_analysis: self.analyze_bottlenecks(&current_metrics, targets).await?,
            improvement_potential: self.calculate_improvement_potential(&current_metrics, targets),
            optimization_status: self.get_optimization_status(&current_metrics, targets),
        })
    }

    fn calculate_performance_score(
        &self,
        metrics: &PerformanceMetrics,
        targets: &OptimizationTargets,
    ) -> f32 {
        let latency_score = if metrics.latency_p95.as_millis() <= targets.target_latency_ms as u128
        {
            1.0
        } else {
            targets.target_latency_ms as f32 / metrics.latency_p95.as_millis() as f32
        };
        let throughput_score = if metrics.throughput_rps >= targets.target_throughput_rps as f64 {
            1.0
        } else {
            metrics.throughput_rps as f32 / targets.target_throughput_rps as f32
        };
        let cache_score = metrics.cache_hit_rate / targets.target_cache_hit_rate;
        let memory_score = if metrics.memory_usage_mb <= targets.target_memory_usage_mb as f64 {
            1.0
        } else {
            targets.target_memory_usage_mb as f32 / metrics.memory_usage_mb as f32
        };
        let error_score = if metrics.error_rate <= targets.target_error_rate {
            1.0
        } else {
            targets.target_error_rate / metrics.error_rate
        };

        (latency_score + throughput_score + cache_score + memory_score + error_score) / 5.0 * 100.0
    }

    fn calculate_target_achievement_rate(
        &self,
        metrics: &PerformanceMetrics,
        targets: &OptimizationTargets,
    ) -> f32 {
        let mut achieved = 0;
        let total = 5;

        if metrics.latency_p95.as_millis() <= targets.target_latency_ms as u128 {
            achieved += 1;
        }
        if metrics.throughput_rps >= targets.target_throughput_rps as f64 {
            achieved += 1;
        }
        if metrics.cache_hit_rate >= targets.target_cache_hit_rate {
            achieved += 1;
        }
        if metrics.memory_usage_mb <= targets.target_memory_usage_mb as f64 {
            achieved += 1;
        }
        if metrics.error_rate <= targets.target_error_rate {
            achieved += 1;
        }

        achieved as f32 / total as f32 * 100.0
    }

    async fn analyze_bottlenecks(
        &self,
        metrics: &PerformanceMetrics,
        targets: &OptimizationTargets,
    ) -> Result<Vec<BottleneckInfo>, Box<dyn std::error::Error + Send + Sync>> {
        let mut bottlenecks = Vec::new();

        if metrics.latency_p95.as_millis() > targets.target_latency_ms as u128 {
            bottlenecks.push(BottleneckInfo {
                bottleneck_type: BottleneckType::Latency,
                severity: BottleneckSeverity::High,
                description: "Response latency exceeds target".to_string(),
                suggested_action: "Implement caching and load balancing".to_string(),
            });
        }

        if metrics.cache_hit_rate < targets.target_cache_hit_rate {
            bottlenecks.push(BottleneckInfo {
                bottleneck_type: BottleneckType::Cache,
                severity: BottleneckSeverity::Medium,
                description: "Cache hit rate below target".to_string(),
                suggested_action: "Optimize cache size and eviction policy".to_string(),
            });
        }

        if metrics.memory_usage_mb > targets.target_memory_usage_mb as f64 {
            bottlenecks.push(BottleneckInfo {
                bottleneck_type: BottleneckType::Memory,
                severity: BottleneckSeverity::Medium,
                description: "Memory usage exceeds target".to_string(),
                suggested_action: "Implement memory optimization strategies".to_string(),
            });
        }

        Ok(bottlenecks)
    }

    fn calculate_improvement_potential(
        &self,
        metrics: &PerformanceMetrics,
        targets: &OptimizationTargets,
    ) -> f32 {
        let current_score = self.calculate_performance_score(metrics, targets);
        100.0 - current_score
    }

    fn get_optimization_status(
        &self,
        metrics: &PerformanceMetrics,
        targets: &OptimizationTargets,
    ) -> OptimizationStatus {
        let achievement_rate = self.calculate_target_achievement_rate(metrics, targets);

        if achievement_rate >= 90.0 {
            OptimizationStatus::Excellent
        } else if achievement_rate >= 75.0 {
            OptimizationStatus::Good
        } else if achievement_rate >= 50.0 {
            OptimizationStatus::NeedsImprovement
        } else {
            OptimizationStatus::Critical
        }
    }

    fn generate_cache_key(
        &self,
        request: &LLMRequest,
    ) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        let prompt_content = request
            .messages
            .iter()
            .map(|msg| msg.content.as_str())
            .collect::<Vec<_>>()
            .join("\n");
        prompt_content.hash(&mut hasher);
        request.max_tokens.hash(&mut hasher);
        request.temperature.to_bits().hash(&mut hasher);
        request.use_case.hash(&mut hasher);

        Ok(format!("cache_{:x}", hasher.finish()))
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizedRequest {
    pub request: LLMRequest,
    pub cached_response: Option<LLMResponse>,
    pub should_batch: bool,
    pub should_compress: bool,
    pub optimization_applied: Vec<OptimizationType>,
    pub estimated_savings: EstimatedSavings,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EstimatedSavings {
    pub time_saved: Duration,
    pub cost_saved: f64,
    pub resources_saved: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkConfig {
    pub test_name: String,
    pub request_count: usize,
    pub concurrent_requests: usize,
    pub request_delay: Option<Duration>,
    pub test_duration: Duration,
    pub warmup_requests: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkResult {
    pub test_name: String,
    pub total_requests: u64,
    pub successful_requests: u64,
    pub failed_requests: u64,
    pub total_duration: Duration,
    pub throughput_rps: f64,
    pub latency_p50: Duration,
    pub latency_p95: Duration,
    pub latency_p99: Duration,
    pub error_rate: f32,
    pub total_bytes_transferred: u64,
    pub memory_usage_peak: u64,
    pub cpu_usage_average: f32,
    pub cache_hit_rate: f32,
    pub optimization_effectiveness: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceReport {
    pub current_metrics: PerformanceMetrics,
    pub benchmark_results: Vec<BenchmarkResult>,
    pub recommendations: Vec<OptimizationRecommendation>,
    pub cache_statistics: CacheStatistics,
    pub compression_statistics: CompressionStatistics,
    pub optimization_summary: OptimizationSummary,
    pub generated_at: SystemTime,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheStatistics {
    pub total_entries: usize,
    pub total_size_bytes: usize,
    pub hit_rate: f32,
    pub miss_rate: f32,
    pub eviction_count: u64,
    pub average_access_count: f64,
    pub average_compression_ratio: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionStatistics {
    pub total_compressed_requests: u64,
    pub average_compression_ratio: f32,
    pub total_bytes_saved: u64,
    pub compression_time_average: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSummary {
    pub overall_performance_score: f32,
    pub target_achievement_rate: f32,
    pub bottleneck_analysis: Vec<BottleneckInfo>,
    pub improvement_potential: f32,
    pub optimization_status: OptimizationStatus,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BottleneckInfo {
    pub bottleneck_type: BottleneckType,
    pub severity: BottleneckSeverity,
    pub description: String,
    pub suggested_action: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BottleneckType {
    Latency,
    Throughput,
    Memory,
    Cache,
    Network,
    CPU,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BottleneckSeverity {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStatus {
    Excellent,
    Good,
    NeedsImprovement,
    Critical,
}

// Supporting engines for optimization
pub struct CompressionEngine {
    compression_count: u64,
    total_bytes_saved: u64,
    total_compression_time: Duration,
}

impl Default for CompressionEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl CompressionEngine {
    pub fn new() -> Self {
        Self {
            compression_count: 0,
            total_bytes_saved: 0,
            total_compression_time: Duration::from_millis(0),
        }
    }

    pub fn compress(&self, text: &str) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
        // Simple compression simulation - could use actual compression algorithms
        let compressed = text.replace("  ", " ").replace("\n\n", "\n");
        Ok(compressed)
    }

    pub fn get_compression_count(&self) -> u64 {
        self.compression_count
    }

    pub fn get_average_compression_ratio(&self) -> f32 {
        0.85 // Placeholder - would calculate actual ratio
    }

    pub fn get_total_bytes_saved(&self) -> u64 {
        self.total_bytes_saved
    }

    pub fn get_average_compression_time(&self) -> Duration {
        if self.compression_count > 0 {
            self.total_compression_time / self.compression_count as u32
        } else {
            Duration::from_millis(0)
        }
    }
}

pub struct PrefetchEngine {
    prefetch_cache: HashMap<String, Vec<LLMRequest>>,
}

impl Default for PrefetchEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl PrefetchEngine {
    pub fn new() -> Self {
        Self {
            prefetch_cache: HashMap::new(),
        }
    }

    pub fn predict_next_requests(&self, _current_request: &LLMRequest) -> Vec<LLMRequest> {
        // Placeholder implementation - would use ML models for prediction
        Vec::new()
    }

    pub fn prefetch_responses(
        &mut self,
        _requests: Vec<LLMRequest>,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        // Placeholder implementation - would asynchronously prefetch responses
        Ok(())
    }
}