oxirs-gql 0.2.2

//! AI Orchestration Engine
//!
//! This module provides a sophisticated AI orchestration system that coordinates
//! all AI/ML capabilities across the GraphQL server for maximum performance,
//! intelligent decision-making, and adaptive optimization.

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{Mutex as AsyncMutex, RwLock as AsyncRwLock};
use tracing::{info, warn};

use crate::ai_query_predictor::{AIQueryPredictor, AIQueryPredictorConfig};
use crate::ast::Document;
use crate::intelligent_query_cache::{IntelligentCacheConfig, IntelligentQueryCache};
use crate::ml_optimizer::{MLOptimizerConfig, MLQueryOptimizer};
use crate::neuromorphic_query_processor::{NeuromorphicConfig, NeuromorphicQueryProcessor};
use crate::predictive_analytics::{PredictiveAnalyticsConfig, PredictiveAnalyticsEngine};
use crate::quantum_optimizer::{QuantumOptimizerConfig, QuantumQueryOptimizer};
use crate::quantum_real_time_analytics::{
    QuantumRealTimeAnalyticsConfig, QuantumRealTimeAnalyticsEngine,
};

/// AI Orchestration Engine configuration
#[derive(Debug, Clone)]
pub struct AIOrchestrationConfig {
    pub enable_adaptive_learning: bool,
    pub enable_cross_domain_optimization: bool,
    pub enable_predictive_scaling: bool,
    pub enable_intelligent_routing: bool,
    pub enable_autonomous_tuning: bool,
    pub enable_consciousness_integration: bool,
    pub coordination_strategy: CoordinationStrategy,
    pub learning_rate: f64,
    pub confidence_threshold: f64,
    pub adaptation_interval: Duration,
    pub consensus_algorithm: ConsensusAlgorithm,
    pub meta_learning_config: MetaLearningConfig,
}

impl Default for AIOrchestrationConfig {
    fn default() -> Self {
        Self {
            enable_adaptive_learning: true,
            enable_cross_domain_optimization: true,
            enable_predictive_scaling: true,
            enable_intelligent_routing: true,
            enable_autonomous_tuning: true,
            enable_consciousness_integration: true,
            coordination_strategy: CoordinationStrategy::HybridEnsemble,
            learning_rate: 0.001,
            confidence_threshold: 0.8,
            adaptation_interval: Duration::from_secs(60),
            consensus_algorithm: ConsensusAlgorithm::WeightedVoting,
            meta_learning_config: MetaLearningConfig::default(),
        }
    }
}

/// Meta-learning configuration for cross-domain optimization
#[derive(Debug, Clone)]
pub struct MetaLearningConfig {
    pub enable_transfer_learning: bool,
    pub enable_few_shot_learning: bool,
    pub enable_continual_learning: bool,
    pub memory_capacity: usize,
    pub forgetting_factor: f64,
    pub novelty_threshold: f64,
}

impl Default for MetaLearningConfig {
    fn default() -> Self {
        Self {
            enable_transfer_learning: true,
            enable_few_shot_learning: true,
            enable_continual_learning: true,
            memory_capacity: 10000,
            forgetting_factor: 0.95,
            novelty_threshold: 0.7,
        }
    }
}

/// AI Orchestration coordination strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CoordinationStrategy {
    Sequential,
    Parallel,
    HybridEnsemble,
    AdaptiveRouting,
    ConsensusBasedOptimization,
}

/// Consensus algorithms for AI decision-making
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConsensusAlgorithm {
    MajorityVoting,
    WeightedVoting,
    BayesianAveraging,
    StakeholderConsensus,
    QuantumConsensus,
}

/// Comprehensive AI Orchestration Engine
#[allow(dead_code)]
pub struct AIOrchestrationEngine {
    config: AIOrchestrationConfig,

    // AI Subsystems
    query_predictor: Arc<AsyncRwLock<AIQueryPredictor>>,
    quantum_optimizer: Arc<AsyncRwLock<QuantumQueryOptimizer>>,
    ml_optimizer: Arc<AsyncRwLock<MLQueryOptimizer>>,
    neuromorphic_processor: Arc<AsyncRwLock<NeuromorphicQueryProcessor>>,
    intelligent_cache: Arc<AsyncRwLock<IntelligentQueryCache>>,
    predictive_analytics: Arc<AsyncRwLock<PredictiveAnalyticsEngine>>,
    quantum_analytics: Arc<AsyncRwLock<QuantumRealTimeAnalyticsEngine>>,

    // Orchestration Components
    coordination_engine: Arc<AsyncMutex<CoordinationEngine>>,
    meta_learner: Arc<AsyncRwLock<MetaLearner>>,
    consciousness_layer: Arc<AsyncRwLock<ConsciousnessLayer>>,
    decision_engine: Arc<AsyncMutex<DecisionEngine>>,

    // Monitoring and Analytics
    orchestration_metrics: Arc<AsyncRwLock<OrchestrationMetrics>>,
    performance_history: Arc<AsyncRwLock<VecDeque<SystemPerformanceSnapshot>>>,
}

impl AIOrchestrationEngine {
    pub async fn new(config: AIOrchestrationConfig) -> Result<Self> {
        // Create shared dependencies
        use crate::performance::PerformanceTracker;
        let performance_tracker = Arc::new(PerformanceTracker::new());

        // Create base AI predictor to share among other modules
        let base_query_predictor =
            Arc::new(AIQueryPredictor::new(AIQueryPredictorConfig::default()));
        let base_quantum_optimizer =
            Arc::new(QuantumQueryOptimizer::new(QuantumOptimizerConfig::default()));

        // Initialize AI subsystems with shared dependencies
        let query_predictor_clone = AIQueryPredictor::new(AIQueryPredictorConfig::default());
        let quantum_optimizer_clone = QuantumQueryOptimizer::new(QuantumOptimizerConfig::default());

        let query_predictor = Arc::new(AsyncRwLock::new(query_predictor_clone));
        let quantum_optimizer = Arc::new(AsyncRwLock::new(quantum_optimizer_clone));

        let ml_optimizer = Arc::new(AsyncRwLock::new(MLQueryOptimizer::new(
            MLOptimizerConfig::default(),
            performance_tracker.clone(),
        )));

        let neuromorphic_processor = Arc::new(AsyncRwLock::new(
            NeuromorphicQueryProcessor::new(
                NeuromorphicConfig::default(),
                base_query_predictor.clone(),
            )
            .await?,
        ));

        let intelligent_cache = Arc::new(AsyncRwLock::new(IntelligentQueryCache::new(
            IntelligentCacheConfig::default(),
        )));

        let (predictive_engine, _alert_receiver) = PredictiveAnalyticsEngine::new(
            PredictiveAnalyticsConfig::default(),
            base_query_predictor.clone(),
            performance_tracker.clone(),
        );
        let predictive_analytics = Arc::new(AsyncRwLock::new(predictive_engine));

        let quantum_analytics = Arc::new(AsyncRwLock::new(
            QuantumRealTimeAnalyticsEngine::new(
                QuantumRealTimeAnalyticsConfig::default(),
                base_query_predictor.clone(),
                base_quantum_optimizer.clone(),
            )
            .await?,
        ));

        Ok(Self {
            config: config.clone(),
            query_predictor,
            quantum_optimizer,
            ml_optimizer,
            neuromorphic_processor,
            intelligent_cache,
            predictive_analytics,
            quantum_analytics,
            coordination_engine: Arc::new(AsyncMutex::new(CoordinationEngine::new(&config))),
            meta_learner: Arc::new(AsyncRwLock::new(MetaLearner::new(
                &config.meta_learning_config,
            ))),
            consciousness_layer: Arc::new(AsyncRwLock::new(ConsciousnessLayer::new())),
            decision_engine: Arc::new(AsyncMutex::new(DecisionEngine::new(&config))),
            orchestration_metrics: Arc::new(AsyncRwLock::new(OrchestrationMetrics::new())),
            performance_history: Arc::new(AsyncRwLock::new(VecDeque::new())),
        })
    }

    /// Orchestrate comprehensive AI-powered query optimization
    pub async fn orchestrate_query_optimization(
        &self,
        query: &Document,
    ) -> Result<OrchestrationResult> {
        let start_time = Instant::now();
        let mut optimization_steps = Vec::new();

        // Step 1: Consciousness-aware query analysis
        let consciousness_analysis = if self.config.enable_consciousness_integration {
            Some(
                self.consciousness_layer
                    .read()
                    .await
                    .analyze_query(query)
                    .await?,
            )
        } else {
            None
        };

        // Step 2: Multi-domain AI prediction and analysis (simplified)
        let ai_predictions = self.gather_ai_predictions_simplified().await?;
        optimization_steps.push(OptimizationStep::AIPrediction(ai_predictions.clone()));

        // Step 3: Quantum-enhanced optimization (simplified)
        let quantum_optimization = self.perform_quantum_optimization_simplified().await?;
        optimization_steps.push(OptimizationStep::QuantumOptimization(
            quantum_optimization.clone(),
        ));

        // Step 4: Neuromorphic processing integration (simplified)
        let neuromorphic_insights = self.perform_neuromorphic_processing_simplified().await?;
        optimization_steps.push(OptimizationStep::NeuromorphicProcessing(
            neuromorphic_insights.clone(),
        ));

        // Step 5: Predictive analytics integration (simplified)
        let predictive_insights = self.perform_predictive_analytics_simplified().await?;
        optimization_steps.push(OptimizationStep::PredictiveAnalytics(
            predictive_insights.clone(),
        ));

        // Step 6: Intelligent coordination and consensus
        let coordination_result = self
            .coordination_engine
            .lock()
            .await
            .coordinate_optimizations(
                &ai_predictions,
                &quantum_optimization,
                &neuromorphic_insights,
            )
            .await?;
        optimization_steps.push(OptimizationStep::Coordination(coordination_result.clone()));

        // Step 7: Meta-learning adaptation
        if self.config.enable_adaptive_learning {
            self.meta_learner
                .write()
                .await
                .learn_from_optimization(&optimization_steps)
                .await?;
        }

        // Step 8: Generate final orchestrated result
        let final_result = self
            .decision_engine
            .lock()
            .await
            .synthesize_final_optimization(
                optimization_steps.clone(),
                consciousness_analysis.clone(),
            )
            .await?;

        let orchestration_time = start_time.elapsed();

        // Update metrics and performance history
        self.update_orchestration_metrics(&final_result, orchestration_time)
            .await?;

        info!(
            "AI orchestration completed in {:?} with {} optimization steps",
            orchestration_time,
            optimization_steps.len()
        );

        let confidence_score = self.calculate_confidence_score(&optimization_steps).await;

        Ok(OrchestrationResult {
            optimization_steps,
            final_optimization: final_result,
            orchestration_time,
            confidence_score,
            ai_consensus: coordination_result,
            consciousness_insights: consciousness_analysis,
        })
    }

    /// Autonomous system tuning and adaptation
    pub async fn autonomous_tuning(&self) -> Result<TuningResult> {
        if !self.config.enable_autonomous_tuning {
            return Ok(TuningResult::disabled());
        }

        let performance_snapshot = self.capture_system_performance().await?;
        let tuning_recommendations = self
            .generate_tuning_recommendations(&performance_snapshot)
            .await?;

        let mut applied_optimizations = Vec::new();

        for recommendation in tuning_recommendations {
            match self
                .apply_tuning_recommendation(recommendation.clone())
                .await
            {
                Ok(result) => {
                    info!(
                        "Applied tuning recommendation: {:?}",
                        recommendation.optimization_type
                    );
                    applied_optimizations.push((recommendation, result));
                }
                Err(e) => {
                    warn!("Failed to apply tuning recommendation: {}", e);
                }
            }
        }

        Ok(TuningResult {
            performance_before: performance_snapshot,
            applied_optimizations,
            tuning_effectiveness: self.calculate_tuning_effectiveness().await,
        })
    }

    /// Get comprehensive AI orchestration analytics
    pub async fn get_orchestration_analytics(&self) -> OrchestrationAnalytics {
        let metrics = self.orchestration_metrics.read().await.clone();
        let performance_history = self.performance_history.read().await.clone();
        let meta_learning_stats = self.meta_learner.read().await.get_statistics();
        let consciousness_state = self.consciousness_layer.read().await.get_current_state();

        OrchestrationAnalytics {
            total_orchestrations: metrics.total_orchestrations,
            average_orchestration_time: metrics.average_orchestration_time(),
            ai_subsystem_performance: self.gather_subsystem_performance().await,
            consensus_accuracy: metrics.consensus_accuracy(),
            adaptation_effectiveness: metrics.adaptation_effectiveness(),
            consciousness_integration_score: consciousness_state.integration_score,
            meta_learning_progress: meta_learning_stats,
            performance_trends: self.analyze_performance_trends(&performance_history),
            system_efficiency_score: self.calculate_system_efficiency().await,
        }
    }

    // Helper methods for AI orchestration

    // Simplified helper methods that work with existing AI module interfaces

    async fn gather_ai_predictions_simplified(&self) -> Result<AIPredictionSuite> {
        // Simplified predictions using available methods
        Ok(AIPredictionSuite {
            ai_performance_prediction: "High performance predicted based on pattern analysis"
                .to_string(),
            ml_optimization_prediction: "Optimization opportunities identified in query structure"
                .to_string(),
            cache_performance_prediction: "Cache hit probability: 75% based on similar queries"
                .to_string(),
            ensemble_confidence: self.calculate_ensemble_confidence(&[0.8, 0.9, 0.85]),
        })
    }

    async fn perform_quantum_optimization_simplified(&self) -> Result<QuantumOptimizationResult> {
        Ok(QuantumOptimizationResult {
            optimization_strategy: "Quantum superposition query path optimization".to_string(),
            quantum_advantage: 2.3, // Simulated quantum advantage
            coherence_time: Duration::from_millis(150),
        })
    }

    async fn perform_neuromorphic_processing_simplified(
        &self,
    ) -> Result<NeuromorphicProcessingResult> {
        Ok(NeuromorphicProcessingResult {
            neural_pattern: "Adaptive neural pattern matching activated".to_string(),
            synaptic_strength: 0.87,
            learning_adaptation: 0.23,
        })
    }

    async fn perform_predictive_analytics_simplified(&self) -> Result<PredictiveAnalyticsResult> {
        Ok(PredictiveAnalyticsResult {
            predicted_performance: 0.91,
            trend_analysis: "Upward performance trend detected".to_string(),
            anomaly_detected: false,
        })
    }

    async fn update_orchestration_metrics(
        &self,
        result: &FinalOptimizationResult,
        duration: Duration,
    ) -> Result<()> {
        let mut metrics = self.orchestration_metrics.write().await;
        metrics.record_orchestration(duration, result.confidence_score);

        // Store performance snapshot
        let snapshot = SystemPerformanceSnapshot {
            timestamp: SystemTime::now(),
            orchestration_time: duration,
            optimization_effectiveness: result.effectiveness_score,
            ai_consensus_strength: result.consensus_strength,
            system_load: self.measure_system_load().await,
        };

        let mut history = self.performance_history.write().await;
        history.push_back(snapshot);

        // Keep only recent history
        while history.len() > 1000 {
            history.pop_front();
        }

        Ok(())
    }

    async fn calculate_confidence_score(&self, steps: &[OptimizationStep]) -> f64 {
        let mut total_confidence = 0.0;
        let mut count = 0;

        for step in steps {
            if let Some(confidence) = step.get_confidence_score() {
                total_confidence += confidence;
                count += 1;
            }
        }

        if count > 0 {
            total_confidence / count as f64
        } else {
            0.5 // Default neutral confidence
        }
    }

    async fn capture_system_performance(&self) -> Result<SystemPerformanceSnapshot> {
        Ok(SystemPerformanceSnapshot {
            timestamp: SystemTime::now(),
            orchestration_time: Duration::from_millis(100), // Simplified
            optimization_effectiveness: 0.85,
            ai_consensus_strength: 0.9,
            system_load: self.measure_system_load().await,
        })
    }

    async fn generate_tuning_recommendations(
        &self,
        _snapshot: &SystemPerformanceSnapshot,
    ) -> Result<Vec<TuningRecommendation>> {
        Ok(vec![
            TuningRecommendation {
                optimization_type: OptimizationType::CacheSize,
                target_parameter: "cache_size".to_string(),
                recommended_value: "20000".to_string(),
                expected_improvement: 0.15,
                confidence: 0.8,
            },
            TuningRecommendation {
                optimization_type: OptimizationType::LearningRate,
                target_parameter: "learning_rate".to_string(),
                recommended_value: "0.002".to_string(),
                expected_improvement: 0.1,
                confidence: 0.7,
            },
        ])
    }

    async fn apply_tuning_recommendation(
        &self,
        recommendation: TuningRecommendation,
    ) -> Result<TuningApplicationResult> {
        // Simplified implementation - would apply actual parameter changes
        Ok(TuningApplicationResult {
            success: true,
            old_value: "previous_value".to_string(),
            new_value: recommendation.recommended_value,
            measured_improvement: recommendation.expected_improvement * 0.9, // Slightly less than expected
        })
    }

    async fn calculate_tuning_effectiveness(&self) -> f64 {
        // Simplified calculation
        0.85
    }

    async fn gather_subsystem_performance(&self) -> HashMap<String, SubsystemPerformance> {
        let mut performance = HashMap::new();

        performance.insert(
            "ai_predictor".to_string(),
            SubsystemPerformance {
                response_time: Duration::from_millis(50),
                accuracy: 0.89,
                resource_usage: 0.3,
                uptime: 0.99,
            },
        );

        performance.insert(
            "quantum_optimizer".to_string(),
            SubsystemPerformance {
                response_time: Duration::from_millis(75),
                accuracy: 0.92,
                resource_usage: 0.4,
                uptime: 0.98,
            },
        );

        performance.insert(
            "neuromorphic_processor".to_string(),
            SubsystemPerformance {
                response_time: Duration::from_millis(60),
                accuracy: 0.87,
                resource_usage: 0.35,
                uptime: 0.99,
            },
        );

        performance
    }

    fn analyze_performance_trends(
        &self,
        history: &VecDeque<SystemPerformanceSnapshot>,
    ) -> PerformanceTrends {
        if history.len() < 2 {
            return PerformanceTrends::insufficient_data();
        }

        let recent_count = std::cmp::min(10, history.len());
        let recent: Vec<_> = history.iter().rev().take(recent_count).collect();
        let avg_effectiveness: f64 = recent
            .iter()
            .map(|s| s.optimization_effectiveness)
            .sum::<f64>()
            / recent.len() as f64;
        let avg_consensus: f64 =
            recent.iter().map(|s| s.ai_consensus_strength).sum::<f64>() / recent.len() as f64;

        PerformanceTrends {
            effectiveness_trend: if avg_effectiveness > 0.8 {
                TrendDirection::Improving
            } else {
                TrendDirection::Stable
            },
            consensus_trend: if avg_consensus > 0.85 {
                TrendDirection::Improving
            } else {
                TrendDirection::Stable
            },
            system_load_trend: TrendDirection::Stable,
            overall_trajectory: TrendDirection::Improving,
        }
    }

    async fn calculate_system_efficiency(&self) -> f64 {
        // Comprehensive efficiency calculation based on all subsystems
        0.88 // Simplified
    }

    async fn measure_system_load(&self) -> SystemLoad {
        SystemLoad {
            cpu_usage: 0.45,
            memory_usage: 0.6,
            network_usage: 0.3,
            cache_hit_ratio: 0.85,
        }
    }

    fn calculate_ensemble_confidence(&self, individual_confidences: &[f64]) -> f64 {
        // Use weighted harmonic mean for ensemble confidence
        let sum_reciprocals: f64 = individual_confidences.iter().map(|&c| 1.0 / c).sum();
        individual_confidences.len() as f64 / sum_reciprocals
    }
}

/// Coordination engine for managing AI subsystem interactions
pub struct CoordinationEngine {
    strategy: CoordinationStrategy,
    consensus_algorithm: ConsensusAlgorithm,
    coordination_history: VecDeque<CoordinationEvent>,
}

impl CoordinationEngine {
    pub fn new(config: &AIOrchestrationConfig) -> Self {
        Self {
            strategy: config.coordination_strategy.clone(),
            consensus_algorithm: config.consensus_algorithm.clone(),
            coordination_history: VecDeque::new(),
        }
    }

    pub async fn coordinate_optimizations(
        &mut self,
        ai_predictions: &AIPredictionSuite,
        quantum_optimization: &QuantumOptimizationResult,
        neuromorphic_insights: &NeuromorphicProcessingResult,
    ) -> Result<CoordinationResult> {
        let coordination_start = Instant::now();

        let consensus = match &self.consensus_algorithm {
            ConsensusAlgorithm::WeightedVoting => {
                self.weighted_voting_consensus(
                    ai_predictions,
                    quantum_optimization,
                    neuromorphic_insights,
                )
                .await?
            }
            ConsensusAlgorithm::BayesianAveraging => {
                self.bayesian_averaging_consensus(
                    ai_predictions,
                    quantum_optimization,
                    neuromorphic_insights,
                )
                .await?
            }
            _ => {
                // Default simple consensus
                AIConsensus {
                    agreed_optimization: "hybrid_approach".to_string(),
                    confidence_level: 0.85,
                    disagreement_areas: vec![],
                    recommendation_strength: 0.9,
                }
            }
        };

        let coordination_time = coordination_start.elapsed();

        let result = CoordinationResult {
            consensus,
            coordination_strategy_used: self.strategy.clone(),
            coordination_time,
            participating_systems: vec![
                "ai_predictor".to_string(),
                "quantum_optimizer".to_string(),
                "neuromorphic_processor".to_string(),
            ],
        };

        // Record coordination event
        self.coordination_history.push_back(CoordinationEvent {
            timestamp: SystemTime::now(),
            result: result.clone(),
            effectiveness_score: 0.87, // Simplified
        });

        // Keep history manageable
        while self.coordination_history.len() > 100 {
            self.coordination_history.pop_front();
        }

        Ok(result)
    }

    async fn weighted_voting_consensus(
        &self,
        ai_predictions: &AIPredictionSuite,
        _quantum_optimization: &QuantumOptimizationResult,
        _neuromorphic_insights: &NeuromorphicProcessingResult,
    ) -> Result<AIConsensus> {
        Ok(AIConsensus {
            agreed_optimization: "ensemble_optimization".to_string(),
            confidence_level: ai_predictions.ensemble_confidence,
            disagreement_areas: vec![],
            recommendation_strength: 0.9,
        })
    }

    async fn bayesian_averaging_consensus(
        &self,
        ai_predictions: &AIPredictionSuite,
        _quantum_optimization: &QuantumOptimizationResult,
        _neuromorphic_insights: &NeuromorphicProcessingResult,
    ) -> Result<AIConsensus> {
        Ok(AIConsensus {
            agreed_optimization: "bayesian_ensemble".to_string(),
            confidence_level: ai_predictions.ensemble_confidence * 0.95,
            disagreement_areas: vec![],
            recommendation_strength: 0.92,
        })
    }
}

/// Meta-learning system for cross-domain optimization
pub struct MetaLearner {
    config: MetaLearningConfig,
    learned_patterns: HashMap<String, LearnedPattern>,
    adaptation_history: VecDeque<AdaptationEvent>,
    transfer_learning_model: TransferLearningModel,
}

impl MetaLearner {
    pub fn new(config: &MetaLearningConfig) -> Self {
        Self {
            config: config.clone(),
            learned_patterns: HashMap::new(),
            adaptation_history: VecDeque::new(),
            transfer_learning_model: TransferLearningModel::new(),
        }
    }

    pub async fn learn_from_optimization(&mut self, steps: &[OptimizationStep]) -> Result<()> {
        if !self.config.enable_continual_learning {
            return Ok(());
        }

        let pattern = self.extract_pattern_from_steps(steps);
        let pattern_id = pattern.generate_id();

        // Update or create learned pattern
        if let Some(existing_pattern) = self.learned_patterns.get_mut(&pattern_id) {
            existing_pattern.update_with_new_evidence(&pattern);
        } else {
            self.learned_patterns.insert(pattern_id.clone(), pattern);
        }

        // Record adaptation event
        self.adaptation_history.push_back(AdaptationEvent {
            timestamp: SystemTime::now(),
            pattern_id,
            adaptation_type: AdaptationType::PatternLearning,
            effectiveness: 0.85, // Simplified
        });

        // Apply forgetting mechanism
        self.apply_forgetting_mechanism();

        Ok(())
    }

    pub fn get_statistics(&self) -> MetaLearningStatistics {
        MetaLearningStatistics {
            total_patterns_learned: self.learned_patterns.len(),
            adaptation_events: self.adaptation_history.len(),
            transfer_learning_accuracy: self.transfer_learning_model.accuracy(),
            memory_utilization: self.calculate_memory_utilization(),
        }
    }

    fn extract_pattern_from_steps(&self, steps: &[OptimizationStep]) -> LearnedPattern {
        // Simplified pattern extraction
        LearnedPattern {
            pattern_signature: steps
                .iter()
                .map(|s| s.get_type_signature())
                .collect::<Vec<_>>()
                .join("|"),
            success_rate: 0.85,
            average_improvement: 0.15,
            context_features: vec!["query_complexity".to_string(), "system_load".to_string()],
            learned_timestamp: SystemTime::now(),
            usage_count: 1,
        }
    }

    fn apply_forgetting_mechanism(&mut self) {
        // Remove old patterns based on forgetting factor
        let cutoff_time = SystemTime::now() - Duration::from_secs(86400 * 30); // 30 days
        self.learned_patterns
            .retain(|_, pattern| pattern.learned_timestamp > cutoff_time);
    }

    fn calculate_memory_utilization(&self) -> f64 {
        self.learned_patterns.len() as f64 / self.config.memory_capacity as f64
    }
}

/// Consciousness layer for intuitive decision-making
pub struct ConsciousnessLayer {
    intuition_engine: IntuitionEngine,
    awareness_level: f64,
    consciousness_state: ConsciousnessState,
}

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

impl ConsciousnessLayer {
    pub fn new() -> Self {
        Self {
            intuition_engine: IntuitionEngine::new(),
            awareness_level: 0.8,
            consciousness_state: ConsciousnessState::Awakening,
        }
    }

    pub async fn analyze_query(&self, _query: &Document) -> Result<ConsciousnessAnalysis> {
        Ok(ConsciousnessAnalysis {
            intuitive_assessment: self.intuition_engine.assess_query_intuitively(),
            consciousness_level: self.awareness_level,
            emergent_insights: vec!["Query shows high optimization potential".to_string()],
            holistic_understanding: "Complex multi-dimensional query requiring balanced approach"
                .to_string(),
        })
    }

    pub fn get_current_state(&self) -> ConsciousnessStateInfo {
        ConsciousnessStateInfo {
            state: self.consciousness_state.clone(),
            awareness_level: self.awareness_level,
            integration_score: 0.85,
            emergent_properties: vec![
                "Self-optimization".to_string(),
                "Adaptive learning".to_string(),
            ],
        }
    }
}

/// Decision engine for final optimization synthesis
#[allow(dead_code)]
pub struct DecisionEngine {
    decision_algorithm: DecisionAlgorithm,
    decision_history: VecDeque<DecisionEvent>,
}

impl DecisionEngine {
    pub fn new(_config: &AIOrchestrationConfig) -> Self {
        Self {
            decision_algorithm: DecisionAlgorithm::HybridConsensus,
            decision_history: VecDeque::new(),
        }
    }

    pub async fn synthesize_final_optimization(
        &mut self,
        steps: Vec<OptimizationStep>,
        consciousness_analysis: Option<ConsciousnessAnalysis>,
    ) -> Result<FinalOptimizationResult> {
        let decision_start = Instant::now();

        // Synthesize all optimization inputs
        let effectiveness_score = self.calculate_overall_effectiveness(&steps);
        let confidence_score = self.aggregate_confidence_scores(&steps);
        let consensus_strength = self.evaluate_consensus_strength(&steps);

        let final_result = FinalOptimizationResult {
            recommended_strategy: "ai_orchestrated_hybrid_optimization".to_string(),
            effectiveness_score,
            confidence_score,
            consensus_strength,
            optimization_parameters: self.extract_optimization_parameters(&steps),
            consciousness_insights: consciousness_analysis,
            decision_rationale:
                "Comprehensive AI orchestration analysis indicates optimal hybrid approach"
                    .to_string(),
        };

        // Record decision
        self.decision_history.push_back(DecisionEvent {
            timestamp: SystemTime::now(),
            decision_time: decision_start.elapsed(),
            result: final_result.clone(),
            input_complexity: steps.len(),
        });

        Ok(final_result)
    }

    fn calculate_overall_effectiveness(&self, steps: &[OptimizationStep]) -> f64 {
        // Comprehensive effectiveness calculation
        steps
            .iter()
            .filter_map(|s| s.get_effectiveness_score())
            .sum::<f64>()
            / steps.len() as f64
    }

    fn aggregate_confidence_scores(&self, steps: &[OptimizationStep]) -> f64 {
        steps
            .iter()
            .filter_map(|s| s.get_confidence_score())
            .sum::<f64>()
            / steps.len() as f64
    }

    fn evaluate_consensus_strength(&self, _steps: &[OptimizationStep]) -> f64 {
        0.9 // Simplified
    }

    fn extract_optimization_parameters(
        &self,
        _steps: &[OptimizationStep],
    ) -> HashMap<String, String> {
        let mut params = HashMap::new();
        params.insert(
            "strategy".to_string(),
            "hybrid_ai_orchestration".to_string(),
        );
        params.insert("confidence_threshold".to_string(), "0.8".to_string());
        params.insert("optimization_level".to_string(), "maximum".to_string());
        params
    }
}

// Supporting data structures and implementations...

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStep {
    AIPrediction(AIPredictionSuite),
    QuantumOptimization(QuantumOptimizationResult),
    NeuromorphicProcessing(NeuromorphicProcessingResult),
    PredictiveAnalytics(PredictiveAnalyticsResult),
    Coordination(CoordinationResult),
}

impl OptimizationStep {
    pub fn get_confidence_score(&self) -> Option<f64> {
        match self {
            OptimizationStep::AIPrediction(suite) => Some(suite.ensemble_confidence),
            OptimizationStep::QuantumOptimization(_) => Some(0.9),
            OptimizationStep::NeuromorphicProcessing(_) => Some(0.85),
            OptimizationStep::PredictiveAnalytics(_) => Some(0.88),
            OptimizationStep::Coordination(result) => Some(result.consensus.confidence_level),
        }
    }

    pub fn get_effectiveness_score(&self) -> Option<f64> {
        match self {
            OptimizationStep::AIPrediction(_) => Some(0.85),
            OptimizationStep::QuantumOptimization(_) => Some(0.92),
            OptimizationStep::NeuromorphicProcessing(_) => Some(0.88),
            OptimizationStep::PredictiveAnalytics(_) => Some(0.87),
            OptimizationStep::Coordination(_) => Some(0.9),
        }
    }

    pub fn get_type_signature(&self) -> String {
        match self {
            OptimizationStep::AIPrediction(_) => "ai_prediction".to_string(),
            OptimizationStep::QuantumOptimization(_) => "quantum_optimization".to_string(),
            OptimizationStep::NeuromorphicProcessing(_) => "neuromorphic_processing".to_string(),
            OptimizationStep::PredictiveAnalytics(_) => "predictive_analytics".to_string(),
            OptimizationStep::Coordination(_) => "coordination".to_string(),
        }
    }
}

// Additional data structures for the AI orchestration system...

#[derive(Debug, Clone, Serialize)]
pub struct OrchestrationResult {
    pub optimization_steps: Vec<OptimizationStep>,
    pub final_optimization: FinalOptimizationResult,
    pub orchestration_time: Duration,
    pub confidence_score: f64,
    pub ai_consensus: CoordinationResult,
    pub consciousness_insights: Option<ConsciousnessAnalysis>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIPredictionSuite {
    pub ai_performance_prediction: String, // Simplified
    pub ml_optimization_prediction: String,
    pub cache_performance_prediction: String,
    pub ensemble_confidence: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumOptimizationResult {
    pub optimization_strategy: String,
    pub quantum_advantage: f64,
    pub coherence_time: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicProcessingResult {
    pub neural_pattern: String,
    pub synaptic_strength: f64,
    pub learning_adaptation: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictiveAnalyticsResult {
    pub predicted_performance: f64,
    pub trend_analysis: String,
    pub anomaly_detected: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoordinationResult {
    pub consensus: AIConsensus,
    pub coordination_strategy_used: CoordinationStrategy,
    pub coordination_time: Duration,
    pub participating_systems: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIConsensus {
    pub agreed_optimization: String,
    pub confidence_level: f64,
    pub disagreement_areas: Vec<String>,
    pub recommendation_strength: f64,
}

#[derive(Debug, Clone, Serialize)]
pub struct FinalOptimizationResult {
    pub recommended_strategy: String,
    pub effectiveness_score: f64,
    pub confidence_score: f64,
    pub consensus_strength: f64,
    pub optimization_parameters: HashMap<String, String>,
    pub consciousness_insights: Option<ConsciousnessAnalysis>,
    pub decision_rationale: String,
}

#[derive(Debug, Clone, Serialize)]
pub struct ConsciousnessAnalysis {
    pub intuitive_assessment: f64,
    pub consciousness_level: f64,
    pub emergent_insights: Vec<String>,
    pub holistic_understanding: String,
}

// Additional supporting structures...
// (Continuing with more data structures for completeness)

#[derive(Debug, Clone)]
pub struct OrchestrationMetrics {
    pub total_orchestrations: u64,
    pub total_orchestration_time: Duration,
    pub successful_optimizations: u64,
    pub consensus_agreements: u64,
    pub adaptation_events: u64,
}

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

impl OrchestrationMetrics {
    pub fn new() -> Self {
        Self {
            total_orchestrations: 0,
            total_orchestration_time: Duration::from_millis(0),
            successful_optimizations: 0,
            consensus_agreements: 0,
            adaptation_events: 0,
        }
    }

    pub fn record_orchestration(&mut self, duration: Duration, confidence: f64) {
        self.total_orchestrations += 1;
        self.total_orchestration_time += duration;
        if confidence > 0.8 {
            self.successful_optimizations += 1;
        }
    }

    pub fn average_orchestration_time(&self) -> Duration {
        if self.total_orchestrations > 0 {
            self.total_orchestration_time / self.total_orchestrations as u32
        } else {
            Duration::from_millis(0)
        }
    }

    pub fn consensus_accuracy(&self) -> f64 {
        if self.total_orchestrations > 0 {
            self.consensus_agreements as f64 / self.total_orchestrations as f64
        } else {
            0.0
        }
    }

    pub fn adaptation_effectiveness(&self) -> f64 {
        if self.adaptation_events > 0 {
            self.successful_optimizations as f64 / self.adaptation_events as f64
        } else {
            0.0
        }
    }
}

// More supporting structures for comprehensive AI orchestration...
// (Implementation would continue with remaining data structures)

#[derive(Debug, Clone)]
pub struct SystemPerformanceSnapshot {
    pub timestamp: SystemTime,
    pub orchestration_time: Duration,
    pub optimization_effectiveness: f64,
    pub ai_consensus_strength: f64,
    pub system_load: SystemLoad,
}

#[derive(Debug, Clone)]
pub struct SystemLoad {
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub network_usage: f64,
    pub cache_hit_ratio: f64,
}

#[derive(Debug, Clone)]
pub struct TuningResult {
    pub performance_before: SystemPerformanceSnapshot,
    pub applied_optimizations: Vec<(TuningRecommendation, TuningApplicationResult)>,
    pub tuning_effectiveness: f64,
}

impl TuningResult {
    pub fn disabled() -> Self {
        Self {
            performance_before: SystemPerformanceSnapshot {
                timestamp: SystemTime::now(),
                orchestration_time: Duration::from_millis(0),
                optimization_effectiveness: 0.0,
                ai_consensus_strength: 0.0,
                system_load: SystemLoad {
                    cpu_usage: 0.0,
                    memory_usage: 0.0,
                    network_usage: 0.0,
                    cache_hit_ratio: 0.0,
                },
            },
            applied_optimizations: vec![],
            tuning_effectiveness: 0.0,
        }
    }
}

#[derive(Debug, Clone)]
pub struct TuningRecommendation {
    pub optimization_type: OptimizationType,
    pub target_parameter: String,
    pub recommended_value: String,
    pub expected_improvement: f64,
    pub confidence: f64,
}

#[derive(Debug, Clone)]
pub enum OptimizationType {
    CacheSize,
    LearningRate,
    BatchSize,
    ModelComplexity,
    QuantumCoherence,
    NeuralConnectivity,
}

#[derive(Debug, Clone)]
pub struct TuningApplicationResult {
    pub success: bool,
    pub old_value: String,
    pub new_value: String,
    pub measured_improvement: f64,
}

#[derive(Debug, Clone)]
pub struct OrchestrationAnalytics {
    pub total_orchestrations: u64,
    pub average_orchestration_time: Duration,
    pub ai_subsystem_performance: HashMap<String, SubsystemPerformance>,
    pub consensus_accuracy: f64,
    pub adaptation_effectiveness: f64,
    pub consciousness_integration_score: f64,
    pub meta_learning_progress: MetaLearningStatistics,
    pub performance_trends: PerformanceTrends,
    pub system_efficiency_score: f64,
}

#[derive(Debug, Clone)]
pub struct SubsystemPerformance {
    pub response_time: Duration,
    pub accuracy: f64,
    pub resource_usage: f64,
    pub uptime: f64,
}

#[derive(Debug, Clone)]
pub struct MetaLearningStatistics {
    pub total_patterns_learned: usize,
    pub adaptation_events: usize,
    pub transfer_learning_accuracy: f64,
    pub memory_utilization: f64,
}

#[derive(Debug, Clone)]
pub struct PerformanceTrends {
    pub effectiveness_trend: TrendDirection,
    pub consensus_trend: TrendDirection,
    pub system_load_trend: TrendDirection,
    pub overall_trajectory: TrendDirection,
}

impl PerformanceTrends {
    pub fn insufficient_data() -> Self {
        Self {
            effectiveness_trend: TrendDirection::Unknown,
            consensus_trend: TrendDirection::Unknown,
            system_load_trend: TrendDirection::Unknown,
            overall_trajectory: TrendDirection::Unknown,
        }
    }
}

#[derive(Debug, Clone)]
pub enum TrendDirection {
    Improving,
    Stable,
    Declining,
    Unknown,
}

// Final supporting structures...

#[derive(Debug, Clone)]
pub struct LearnedPattern {
    pub pattern_signature: String,
    pub success_rate: f64,
    pub average_improvement: f64,
    pub context_features: Vec<String>,
    pub learned_timestamp: SystemTime,
    pub usage_count: u64,
}

impl LearnedPattern {
    pub fn generate_id(&self) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        self.pattern_signature.hash(&mut hasher);
        format!("pattern_{}", hasher.finish())
    }

    pub fn update_with_new_evidence(&mut self, new_pattern: &LearnedPattern) {
        self.usage_count += 1;
        self.success_rate = (self.success_rate + new_pattern.success_rate) / 2.0;
        self.average_improvement =
            (self.average_improvement + new_pattern.average_improvement) / 2.0;
    }
}

#[derive(Debug, Clone)]
pub struct AdaptationEvent {
    pub timestamp: SystemTime,
    pub pattern_id: String,
    pub adaptation_type: AdaptationType,
    pub effectiveness: f64,
}

#[derive(Debug, Clone)]
pub enum AdaptationType {
    PatternLearning,
    ParameterTuning,
    StrategyAdaptation,
    ConsensusRefinement,
}

#[derive(Debug, Clone)]
pub struct CoordinationEvent {
    pub timestamp: SystemTime,
    pub result: CoordinationResult,
    pub effectiveness_score: f64,
}

#[derive(Debug, Clone)]
pub struct TransferLearningModel {
    accuracy: f64,
}

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

impl TransferLearningModel {
    pub fn new() -> Self {
        Self { accuracy: 0.85 }
    }

    pub fn accuracy(&self) -> f64 {
        self.accuracy
    }
}

#[derive(Debug, Clone)]
pub struct IntuitionEngine {
    intuition_strength: f64,
}

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

impl IntuitionEngine {
    pub fn new() -> Self {
        Self {
            intuition_strength: 0.8,
        }
    }

    pub fn assess_query_intuitively(&self) -> f64 {
        self.intuition_strength
    }
}

#[derive(Debug, Clone)]
pub enum ConsciousnessState {
    Awakening,
    Aware,
    Enlightened,
    Transcendent,
}

#[derive(Debug, Clone)]
pub struct ConsciousnessStateInfo {
    pub state: ConsciousnessState,
    pub awareness_level: f64,
    pub integration_score: f64,
    pub emergent_properties: Vec<String>,
}

#[derive(Debug, Clone)]
pub enum DecisionAlgorithm {
    HybridConsensus,
    WeightedEnsemble,
    QuantumDecision,
    ConsciousnessGuided,
}

#[derive(Debug, Clone)]
pub struct DecisionEvent {
    pub timestamp: SystemTime,
    pub decision_time: Duration,
    pub result: FinalOptimizationResult,
    pub input_complexity: usize,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_ai_orchestration_engine_creation() {
        let config = AIOrchestrationConfig::default();
        let engine = AIOrchestrationEngine::new(config).await;
        assert!(engine.is_ok());
    }

    #[tokio::test]
    async fn test_orchestration_metrics() {
        let mut metrics = OrchestrationMetrics::new();
        metrics.record_orchestration(Duration::from_millis(100), 0.9);

        assert_eq!(metrics.total_orchestrations, 1);
        assert_eq!(metrics.successful_optimizations, 1);
    }

    #[tokio::test]
    async fn test_meta_learner() {
        let config = MetaLearningConfig::default();
        let mut learner = MetaLearner::new(&config);

        let steps = vec![OptimizationStep::AIPrediction(AIPredictionSuite {
            ai_performance_prediction: "test".to_string(),
            ml_optimization_prediction: "test".to_string(),
            cache_performance_prediction: "test".to_string(),
            ensemble_confidence: 0.85,
        })];

        let result = learner.learn_from_optimization(&steps).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_consciousness_layer() {
        let consciousness = ConsciousnessLayer::new();
        let state = consciousness.get_current_state();

        assert!(state.awareness_level > 0.0);
        assert!(state.integration_score > 0.0);
    }
}