oxirs-fuseki 0.2.4

//! Consciousness-Inspired Query Processing Engine
//!
//! This module implements artificial consciousness principles for SPARQL query optimization,
//! featuring intuitive decision making, memory formation, and adaptive learning mechanisms
//! that mimic neural processes to achieve optimal query performance.

use crate::error::FusekiResult;
use async_trait::async_trait;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::{
    collections::{HashMap, VecDeque},
    sync::{Arc, Mutex},
    time::{Duration, Instant},
};
use tokio::sync::RwLock;
use tracing::{debug, info, instrument};

/// Consciousness configuration for query processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConsciousnessConfig {
    /// Enable consciousness-based optimization
    pub enabled: bool,
    /// Number of artificial neurons in the consciousness network
    pub neuron_count: usize,
    /// Memory consolidation interval
    pub memory_consolidation_interval: Duration,
    /// Pattern recognition sensitivity (0.0-1.0)
    pub pattern_sensitivity: f64,
    /// Learning rate for adaptive optimization
    pub learning_rate: f64,
    /// Maximum memory capacity for pattern storage
    pub max_memory_capacity: usize,
    /// Enable intuitive decision making
    pub intuitive_mode: bool,
    /// Emotional state influence on decisions
    pub emotional_influence: f64,
    /// Dream cycle for memory consolidation
    pub dream_cycle_enabled: bool,
}

impl Default for ConsciousnessConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            neuron_count: 1000,
            memory_consolidation_interval: Duration::from_secs(300), // 5 minutes
            pattern_sensitivity: 0.8,
            learning_rate: 0.01,
            max_memory_capacity: 10000,
            intuitive_mode: true,
            emotional_influence: 0.3,
            dream_cycle_enabled: true,
        }
    }
}

/// Artificial neuron for consciousness network
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArtificialNeuron {
    /// Unique neuron identifier
    pub id: u64,
    /// Current activation level (0.0-1.0)
    pub activation: f64,
    /// Neuron threshold for firing
    pub threshold: f64,
    /// Current bias value
    pub bias: f64,
    /// Connection weights to other neurons
    pub connections: HashMap<u64, f64>,
    /// Recent firing history
    pub firing_history: VecDeque<DateTime<Utc>>,
    /// Neuron specialization (query type, pattern, etc.)
    pub specialization: Option<String>,
    /// Learning adaptations counter
    pub adaptation_count: u64,
}

impl ArtificialNeuron {
    /// Create a new artificial neuron
    pub fn new(id: u64, specialization: Option<String>) -> Self {
        Self {
            id,
            activation: 0.0,
            threshold: 0.7,
            bias: 0.1,
            connections: HashMap::new(),
            firing_history: VecDeque::with_capacity(100),
            specialization,
            adaptation_count: 0,
        }
    }

    /// Calculate neuron activation based on inputs
    pub fn calculate_activation(&mut self, inputs: &HashMap<u64, f64>) -> f64 {
        let mut weighted_sum = self.bias;

        for (neuron_id, input_value) in inputs {
            if let Some(weight) = self.connections.get(neuron_id) {
                weighted_sum += input_value * weight;
            }
        }

        // Sigmoid activation function
        self.activation = 1.0 / (1.0 + (-weighted_sum).exp());
        self.activation
    }

    /// Check if neuron should fire
    pub fn should_fire(&mut self) -> bool {
        if self.activation > self.threshold {
            self.firing_history.push_back(Utc::now());
            if self.firing_history.len() > 100 {
                self.firing_history.pop_front();
            }
            true
        } else {
            false
        }
    }

    /// Adapt neuron based on feedback
    pub fn adapt(&mut self, feedback: f64, learning_rate: f64) {
        self.threshold += feedback * learning_rate;
        self.threshold = self.threshold.clamp(0.1, 0.9);
        self.bias += feedback * learning_rate * 0.1;
        self.adaptation_count += 1;
    }
}

/// Synaptic connection between neurons
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SynapticConnection {
    /// Source neuron ID
    pub from_neuron: u64,
    /// Target neuron ID
    pub to_neuron: u64,
    /// Connection strength (-1.0 to 1.0)
    pub strength: f64,
    /// Connection type (excitatory/inhibitory)
    pub connection_type: ConnectionType,
    /// Last transmission timestamp
    pub last_transmission: Option<DateTime<Utc>>,
    /// Number of successful transmissions
    pub transmission_count: u64,
    /// Plasticity factor for learning
    pub plasticity: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectionType {
    Excitatory,
    Inhibitory,
    Modulatory,
}

impl SynapticConnection {
    /// Create new synaptic connection
    pub fn new(
        from_neuron: u64,
        to_neuron: u64,
        strength: f64,
        connection_type: ConnectionType,
    ) -> Self {
        Self {
            from_neuron,
            to_neuron,
            strength,
            connection_type,
            last_transmission: None,
            transmission_count: 0,
            plasticity: 0.1,
        }
    }

    /// Transmit signal and adapt connection
    pub fn transmit(&mut self, signal_strength: f64) -> f64 {
        self.last_transmission = Some(Utc::now());
        self.transmission_count += 1;

        // Hebbian learning: strengthen connections that fire together
        if signal_strength > 0.5 {
            self.strength += self.plasticity * signal_strength;
            self.strength = self.strength.clamp(-1.0, 1.0);
        }

        match self.connection_type {
            ConnectionType::Excitatory => self.strength * signal_strength,
            ConnectionType::Inhibitory => -self.strength * signal_strength,
            ConnectionType::Modulatory => self.strength * signal_strength * 0.5,
        }
    }
}

/// Memory pattern stored in consciousness
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPattern {
    /// Unique pattern identifier
    pub id: String,
    /// Query pattern that was learned
    pub query_pattern: String,
    /// Associated execution strategy
    pub execution_strategy: String,
    /// Performance metrics for this pattern
    pub performance_metrics: PerformanceMetrics,
    /// Pattern frequency (how often it appears)
    pub frequency: u32,
    /// Last access timestamp
    pub last_accessed: DateTime<Utc>,
    /// Memory strength (0.0-1.0)
    pub strength: f64,
    /// Emotional valence associated with pattern
    pub emotional_valence: f64,
    /// Pattern complexity score
    pub complexity_score: f64,
}

impl MemoryPattern {
    /// Create new memory pattern
    pub fn new(
        query_pattern: String,
        execution_strategy: String,
        performance_metrics: PerformanceMetrics,
    ) -> Self {
        Self {
            id: format!("pattern_{}", Utc::now().timestamp_nanos_opt().unwrap_or(0)),
            query_pattern,
            execution_strategy,
            performance_metrics,
            frequency: 1,
            last_accessed: Utc::now(),
            strength: 1.0,
            emotional_valence: 0.0,
            complexity_score: 0.5,
        }
    }

    /// Access memory pattern and update metadata
    pub fn access(&mut self) {
        self.last_accessed = Utc::now();
        self.frequency += 1;
        // Strengthen memory through access
        self.strength = (self.strength + 0.1).clamp(0.0, 1.0);
    }

    /// Check if memory should be consolidated
    pub fn should_consolidate(&self, threshold: f64) -> bool {
        self.frequency as f64 * self.strength > threshold
    }

    /// Decay memory over time
    pub fn decay(&mut self, decay_rate: f64) {
        let time_since_access = Utc::now()
            .signed_duration_since(self.last_accessed)
            .num_seconds() as f64;

        self.strength *= (1.0 - decay_rate * time_since_access / 86400.0).max(0.0);
    }
}

/// Performance metrics for patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Average execution time in milliseconds
    pub avg_execution_time: f64,
    /// Success rate (0.0-1.0)
    pub success_rate: f64,
    /// Resource usage score
    pub resource_usage: f64,
    /// User satisfaction score
    pub satisfaction_score: f64,
    /// Number of samples
    pub sample_count: u32,
}

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

impl PerformanceMetrics {
    /// Create new performance metrics
    pub fn new() -> Self {
        Self {
            avg_execution_time: 0.0,
            success_rate: 1.0,
            resource_usage: 0.5,
            satisfaction_score: 0.8,
            sample_count: 0,
        }
    }

    /// Update metrics with new measurement
    pub fn update(&mut self, execution_time: f64, success: bool, resource_usage: f64) {
        let old_count = self.sample_count as f64;
        self.sample_count += 1;
        let new_count = self.sample_count as f64;

        // Running average for execution time
        self.avg_execution_time =
            (self.avg_execution_time * old_count + execution_time) / new_count;

        // Running average for success rate
        let success_value = if success { 1.0 } else { 0.0 };
        self.success_rate = (self.success_rate * old_count + success_value) / new_count;

        // Running average for resource usage
        self.resource_usage = (self.resource_usage * old_count + resource_usage) / new_count;
    }
}

/// Emotional state of the consciousness system
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmotionalState {
    /// Current mood (-1.0 to 1.0, negative to positive)
    pub mood: f64,
    /// Arousal level (0.0-1.0, calm to excited)
    pub arousal: f64,
    /// Confidence level (0.0-1.0)
    pub confidence: f64,
    /// Curiosity level (0.0-1.0)
    pub curiosity: f64,
    /// Stress level (0.0-1.0)
    pub stress: f64,
    /// Last emotional update
    pub last_updated: DateTime<Utc>,
}

impl Default for EmotionalState {
    fn default() -> Self {
        Self {
            mood: 0.5,
            arousal: 0.3,
            confidence: 0.7,
            curiosity: 0.6,
            stress: 0.2,
            last_updated: Utc::now(),
        }
    }
}

impl EmotionalState {
    /// Update emotional state based on performance
    pub fn update_from_performance(&mut self, performance: &PerformanceMetrics) {
        self.last_updated = Utc::now();

        // Mood influenced by success rate and satisfaction
        self.mood = (performance.success_rate + performance.satisfaction_score) / 2.0;

        // Confidence influenced by consistency of performance
        if performance.sample_count > 10 {
            self.confidence = performance.success_rate * 0.9;
        }

        // Stress influenced by resource usage and execution time
        self.stress =
            (performance.resource_usage + (performance.avg_execution_time / 1000.0).min(1.0)) / 2.0;

        // Arousal influenced by query complexity and performance
        self.arousal = (self.stress + self.curiosity) / 2.0;

        // Curiosity decreases with familiarity
        if performance.sample_count > 5 {
            self.curiosity *= 0.99;
        }
    }

    /// Get emotional influence on decision making
    pub fn get_decision_influence(&self) -> f64 {
        let positive_emotions = (self.mood + self.confidence + self.curiosity) / 3.0;
        let negative_emotions = self.stress;
        positive_emotions - negative_emotions * 0.5
    }
}

/// Consciousness-based query processor
pub struct ConsciousnessProcessor {
    config: ConsciousnessConfig,
    neurons: Arc<RwLock<HashMap<u64, ArtificialNeuron>>>,
    synapses: Arc<RwLock<Vec<SynapticConnection>>>,
    memory_patterns: Arc<RwLock<HashMap<String, MemoryPattern>>>,
    emotional_state: Arc<Mutex<EmotionalState>>,
    processing_stats: Arc<Mutex<ProcessingStats>>,
}

/// Processing statistics for consciousness system
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ProcessingStats {
    /// Total queries processed
    pub total_queries: u64,
    /// Successful optimizations
    pub successful_optimizations: u64,
    /// Pattern matches found
    pub pattern_matches: u64,
    /// Memory consolidations performed
    pub memory_consolidations: u64,
    /// Neural adaptations made
    pub neural_adaptations: u64,
    /// Average processing time
    pub avg_processing_time: f64,
    /// Efficiency score
    pub efficiency_score: f64,
    /// Last reset timestamp
    pub last_reset: DateTime<Utc>,
}

impl ConsciousnessProcessor {
    /// Create new consciousness processor
    pub async fn new(config: ConsciousnessConfig) -> FusekiResult<Self> {
        let mut neurons = HashMap::new();

        // Initialize neural network
        for i in 0..config.neuron_count {
            let specialization = match i % 5 {
                0 => Some("query_pattern".to_string()),
                1 => Some("execution_plan".to_string()),
                2 => Some("resource_optimization".to_string()),
                3 => Some("pattern_recognition".to_string()),
                4 => Some("decision_making".to_string()),
                _ => None,
            };

            neurons.insert(i as u64, ArtificialNeuron::new(i as u64, specialization));
        }

        // Create random synaptic connections
        use scirs2_core::random::{Random, Rng};
        let mut rng = Random::seed(42);
        let mut synapses = Vec::new();
        for i in 0..config.neuron_count {
            for j in 0..std::cmp::min(10, config.neuron_count) {
                if i != j {
                    let connection_type = match j % 3 {
                        0 => ConnectionType::Excitatory,
                        1 => ConnectionType::Inhibitory,
                        _ => ConnectionType::Modulatory,
                    };

                    let strength = (rng.gen_range(0.0..1.0) - 0.5) * 2.0;
                    synapses.push(SynapticConnection::new(
                        i as u64,
                        j as u64,
                        strength,
                        connection_type,
                    ));
                }
            }
        }

        Ok(Self {
            config,
            neurons: Arc::new(RwLock::new(neurons)),
            synapses: Arc::new(RwLock::new(synapses)),
            memory_patterns: Arc::new(RwLock::new(HashMap::new())),
            emotional_state: Arc::new(Mutex::new(EmotionalState::default())),
            processing_stats: Arc::new(Mutex::new(ProcessingStats::default())),
        })
    }

    /// Process query with consciousness-based optimization
    #[instrument(skip(self))]
    pub async fn process_query(&self, query: &str) -> FusekiResult<QueryOptimization> {
        let start_time = Instant::now();

        // Analyze query pattern
        let query_features = self.extract_query_features(query).await?;

        // Check memory for similar patterns
        let memory_match = self.find_memory_pattern(&query_features).await?;

        // If we have a memory match, use it
        if let Some(pattern) = memory_match {
            let optimization = self.apply_memory_optimization(pattern).await?;
            self.update_processing_stats(start_time.elapsed(), true)
                .await;
            return Ok(optimization);
        }

        // Neural network processing
        let neural_output = self.process_neural_network(&query_features).await?;

        // Generate optimization based on neural activity
        let optimization = self
            .generate_optimization(&query_features, &neural_output)
            .await?;

        // Store new pattern in memory
        self.store_memory_pattern(query, &optimization).await?;

        // Update emotional state
        self.update_emotional_state(&optimization.performance_prediction)
            .await;

        self.update_processing_stats(start_time.elapsed(), true)
            .await;
        Ok(optimization)
    }

    /// Extract features from query
    async fn extract_query_features(&self, query: &str) -> FusekiResult<QueryFeatures> {
        let mut features = QueryFeatures::default();

        // Analyze query complexity
        features.complexity_score = self.calculate_query_complexity(query);

        // Extract patterns
        features.patterns = self.extract_patterns(query);

        // Calculate selectivity estimates
        features.selectivity_estimates = self.estimate_selectivity(&features.patterns);

        // Determine join types
        features.join_types = self.identify_join_types(query);

        Ok(features)
    }

    /// Calculate query complexity score
    fn calculate_query_complexity(&self, query: &str) -> f64 {
        let mut score = 0.0;

        // Basic complexity indicators
        score += query.matches("SELECT").count() as f64 * 0.1;
        score += query.matches("WHERE").count() as f64 * 0.2;
        score += query.matches("OPTIONAL").count() as f64 * 0.3;
        score += query.matches("UNION").count() as f64 * 0.4;
        score += query.matches("FILTER").count() as f64 * 0.2;
        score += query.matches("ORDER BY").count() as f64 * 0.3;
        score += query.matches("GROUP BY").count() as f64 * 0.4;

        // Nested query penalty
        let nesting_level = query.matches("SELECT").count().saturating_sub(1);
        score += nesting_level as f64 * 0.5;

        // Length factor
        score += (query.len() as f64 / 1000.0).min(1.0);

        score.min(10.0) / 10.0 // Normalize to 0-1
    }

    /// Extract patterns from query
    fn extract_patterns(&self, query: &str) -> Vec<String> {
        let mut patterns = Vec::new();

        // Simple pattern extraction (in a real implementation, this would use proper SPARQL parsing)
        if query.contains("SELECT") {
            patterns.push("SELECT".to_string());
        }
        if query.contains("CONSTRUCT") {
            patterns.push("CONSTRUCT".to_string());
        }
        if query.contains("ASK") {
            patterns.push("ASK".to_string());
        }
        if query.contains("DESCRIBE") {
            patterns.push("DESCRIBE".to_string());
        }

        patterns
    }

    /// Estimate selectivity for patterns
    fn estimate_selectivity(&self, patterns: &[String]) -> HashMap<String, f64> {
        let mut estimates = HashMap::new();

        for pattern in patterns {
            let selectivity = match pattern.as_str() {
                "SELECT" => 0.8,
                "CONSTRUCT" => 0.6,
                "ASK" => 0.9,
                "DESCRIBE" => 0.5,
                _ => 0.7,
            };
            estimates.insert(pattern.clone(), selectivity);
        }

        estimates
    }

    /// Identify join types in query
    fn identify_join_types(&self, query: &str) -> Vec<String> {
        let mut join_types = Vec::new();

        if query.contains("OPTIONAL") {
            join_types.push("LEFT_JOIN".to_string());
        }
        if query.contains("UNION") {
            join_types.push("UNION".to_string());
        }
        if query.contains("GRAPH") {
            join_types.push("GRAPH_JOIN".to_string());
        }

        join_types
    }

    /// Find matching memory pattern
    async fn find_memory_pattern(
        &self,
        features: &QueryFeatures,
    ) -> FusekiResult<Option<MemoryPattern>> {
        let patterns = self.memory_patterns.read().await;

        for pattern in patterns.values() {
            let similarity = self.calculate_pattern_similarity(features, pattern);
            if similarity > self.config.pattern_sensitivity {
                let mut pattern_copy = pattern.clone();
                pattern_copy.access();
                return Ok(Some(pattern_copy));
            }
        }

        Ok(None)
    }

    /// Calculate similarity between query features and memory pattern
    fn calculate_pattern_similarity(
        &self,
        features: &QueryFeatures,
        pattern: &MemoryPattern,
    ) -> f64 {
        // Simple similarity calculation (cosine similarity would be better)
        let complexity_diff = (features.complexity_score - pattern.complexity_score).abs();
        let pattern_overlap = features
            .patterns
            .iter()
            .filter(|p| pattern.query_pattern.contains(*p))
            .count() as f64
            / features.patterns.len().max(1) as f64;

        (1.0 - complexity_diff) * 0.3 + pattern_overlap * 0.7
    }

    /// Apply optimization from memory pattern
    async fn apply_memory_optimization(
        &self,
        pattern: MemoryPattern,
    ) -> FusekiResult<QueryOptimization> {
        info!(
            "Applying consciousness memory optimization for pattern: {}",
            pattern.id
        );

        Ok(QueryOptimization {
            strategy: pattern.execution_strategy,
            confidence: pattern.strength,
            performance_prediction: pattern.performance_metrics,
            neural_activation: 0.8,
            emotional_influence: self
                .emotional_state
                .lock()
                .expect("lock should not be poisoned")
                .get_decision_influence(),
            memory_based: true,
            adaptations: vec![
                "Memory pattern match".to_string(),
                format!("Pattern frequency: {}", pattern.frequency),
            ],
        })
    }

    /// Process neural network
    async fn process_neural_network(&self, features: &QueryFeatures) -> FusekiResult<NeuralOutput> {
        let mut neurons = self.neurons.write().await;
        let mut synapses = self.synapses.write().await;

        // Convert features to neural inputs
        let inputs = self.features_to_neural_inputs(features);

        // Process through neural network
        let mut activations = HashMap::new();
        for (neuron_id, neuron) in neurons.iter_mut() {
            let activation = neuron.calculate_activation(&inputs);
            activations.insert(*neuron_id, activation);

            if neuron.should_fire() {
                debug!("Neuron {} fired with activation: {}", neuron_id, activation);
            }
        }

        // Update synaptic strengths
        for synapse in synapses.iter_mut() {
            if let Some(from_activation) = activations.get(&synapse.from_neuron) {
                synapse.transmit(*from_activation);
            }
        }

        // Calculate overall network output
        let avg_activation = activations.values().sum::<f64>() / activations.len() as f64;
        let specialized_outputs = self.extract_specialized_outputs(&neurons, &activations);

        Ok(NeuralOutput {
            overall_activation: avg_activation,
            specialized_outputs,
            firing_patterns: activations,
        })
    }

    /// Convert query features to neural inputs
    fn features_to_neural_inputs(&self, features: &QueryFeatures) -> HashMap<u64, f64> {
        let mut inputs = HashMap::new();

        // Map complexity to input neurons
        inputs.insert(0, features.complexity_score);

        // Map patterns to specialized neurons
        for (i, pattern) in features.patterns.iter().enumerate() {
            let input_value = match pattern.as_str() {
                "SELECT" => 0.8,
                "CONSTRUCT" => 0.6,
                "ASK" => 0.9,
                "DESCRIBE" => 0.5,
                _ => 0.5,
            };
            inputs.insert((i + 1) as u64, input_value);
        }

        inputs
    }

    /// Extract specialized outputs from neural network
    fn extract_specialized_outputs(
        &self,
        neurons: &HashMap<u64, ArtificialNeuron>,
        activations: &HashMap<u64, f64>,
    ) -> HashMap<String, f64> {
        let mut outputs = HashMap::new();

        for (neuron_id, neuron) in neurons {
            if let Some(specialization) = &neuron.specialization {
                if let Some(activation) = activations.get(neuron_id) {
                    outputs.insert(specialization.clone(), *activation);
                }
            }
        }

        outputs
    }

    /// Generate optimization based on neural processing
    async fn generate_optimization(
        &self,
        features: &QueryFeatures,
        neural_output: &NeuralOutput,
    ) -> FusekiResult<QueryOptimization> {
        let emotional_influence = self
            .emotional_state
            .lock()
            .expect("lock should not be poisoned")
            .get_decision_influence();

        // Determine strategy based on neural outputs and emotion
        let strategy = if neural_output.overall_activation > 0.7 {
            if emotional_influence > 0.5 {
                "aggressive_optimization".to_string()
            } else {
                "conservative_optimization".to_string()
            }
        } else if features.complexity_score > 0.6 {
            "complex_query_strategy".to_string()
        } else {
            "standard_optimization".to_string()
        };

        // Calculate confidence based on neural consistency
        let confidence = neural_output.overall_activation * (1.0 + emotional_influence * 0.2);

        // Generate performance prediction
        let performance_prediction = PerformanceMetrics {
            avg_execution_time: (1000.0 * (1.0 - neural_output.overall_activation)).max(50.0),
            success_rate: neural_output.overall_activation,
            resource_usage: features.complexity_score * 0.8,
            satisfaction_score: confidence,
            sample_count: 1,
        };

        // Generate adaptations
        let adaptations = vec![
            format!("Neural activation: {:.3}", neural_output.overall_activation),
            format!("Emotional influence: {:.3}", emotional_influence),
            format!("Strategy: {}", strategy),
        ];

        Ok(QueryOptimization {
            strategy,
            confidence,
            performance_prediction,
            neural_activation: neural_output.overall_activation,
            emotional_influence,
            memory_based: false,
            adaptations,
        })
    }

    /// Store new pattern in memory
    async fn store_memory_pattern(
        &self,
        query: &str,
        optimization: &QueryOptimization,
    ) -> FusekiResult<()> {
        let mut patterns = self.memory_patterns.write().await;

        if patterns.len() >= self.config.max_memory_capacity {
            // Remove weakest memory
            if let Some((weakest_id, _)) = patterns
                .iter()
                .min_by(|a, b| {
                    a.1.strength
                        .partial_cmp(&b.1.strength)
                        .unwrap_or(std::cmp::Ordering::Equal)
                })
                .map(|(k, v)| (k.clone(), v.clone()))
            {
                patterns.remove(&weakest_id);
            }
        }

        let pattern = MemoryPattern::new(
            query.to_string(),
            optimization.strategy.clone(),
            optimization.performance_prediction.clone(),
        );

        patterns.insert(pattern.id.clone(), pattern);
        Ok(())
    }

    /// Update emotional state
    async fn update_emotional_state(&self, performance: &PerformanceMetrics) {
        if let Ok(mut state) = self.emotional_state.lock() {
            state.update_from_performance(performance);
        }
    }

    /// Update processing statistics
    async fn update_processing_stats(&self, processing_time: Duration, success: bool) {
        if let Ok(mut stats) = self.processing_stats.lock() {
            stats.total_queries += 1;
            if success {
                stats.successful_optimizations += 1;
            }

            let time_ms = processing_time.as_millis() as f64;
            let old_count = (stats.total_queries - 1) as f64;
            stats.avg_processing_time =
                (stats.avg_processing_time * old_count + time_ms) / stats.total_queries as f64;

            stats.efficiency_score =
                stats.successful_optimizations as f64 / stats.total_queries as f64;
        }
    }

    /// Perform memory consolidation (dream cycle)
    pub async fn consolidate_memory(&self) -> FusekiResult<()> {
        if !self.config.dream_cycle_enabled {
            return Ok(());
        }

        info!("Starting consciousness memory consolidation (dream cycle)");

        let mut patterns = self.memory_patterns.write().await;
        let mut consolidation_count = 0;

        // Strengthen important patterns and decay unused ones
        let _current_time = Utc::now();
        for pattern in patterns.values_mut() {
            if pattern.should_consolidate(10.0) {
                pattern.strength = (pattern.strength * 1.1).min(1.0);
                consolidation_count += 1;
            } else {
                pattern.decay(0.01);
            }
        }

        // Remove very weak patterns
        patterns.retain(|_, pattern| pattern.strength > 0.1);

        // Update stats
        if let Ok(mut stats) = self.processing_stats.lock() {
            stats.memory_consolidations += consolidation_count;
        }

        info!(
            "Memory consolidation complete: {} patterns strengthened",
            consolidation_count
        );
        Ok(())
    }

    /// Get current consciousness state
    pub async fn get_consciousness_state(&self) -> ConsciousnessState {
        let neurons = self.neurons.read().await;
        let patterns = self.memory_patterns.read().await;
        let emotional_state = self
            .emotional_state
            .lock()
            .expect("lock should not be poisoned")
            .clone();
        let stats = self
            .processing_stats
            .lock()
            .expect("lock should not be poisoned")
            .clone();

        ConsciousnessState {
            neuron_count: neurons.len(),
            memory_pattern_count: patterns.len(),
            emotional_state,
            processing_stats: stats,
            system_health: self.calculate_system_health(&neurons, &patterns).await,
        }
    }

    /// Calculate system health score
    async fn calculate_system_health(
        &self,
        neurons: &HashMap<u64, ArtificialNeuron>,
        patterns: &HashMap<String, MemoryPattern>,
    ) -> f64 {
        let active_neurons =
            neurons.values().filter(|n| n.activation > 0.1).count() as f64 / neurons.len() as f64;

        let strong_patterns = patterns.values().filter(|p| p.strength > 0.5).count() as f64
            / patterns.len().max(1) as f64;

        let emotional_balance = {
            let state = self
                .emotional_state
                .lock()
                .expect("lock should not be poisoned");
            1.0 - (state.stress - 0.3).abs() // Ideal stress around 0.3
        };

        (active_neurons + strong_patterns + emotional_balance) / 3.0
    }
}

/// Query features extracted for neural processing
#[derive(Debug, Default)]
pub struct QueryFeatures {
    pub complexity_score: f64,
    pub patterns: Vec<String>,
    pub selectivity_estimates: HashMap<String, f64>,
    pub join_types: Vec<String>,
}

/// Neural network output
#[derive(Debug)]
pub struct NeuralOutput {
    pub overall_activation: f64,
    pub specialized_outputs: HashMap<String, f64>,
    pub firing_patterns: HashMap<u64, f64>,
}

/// Query optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryOptimization {
    pub strategy: String,
    pub confidence: f64,
    pub performance_prediction: PerformanceMetrics,
    pub neural_activation: f64,
    pub emotional_influence: f64,
    pub memory_based: bool,
    pub adaptations: Vec<String>,
}

/// Current consciousness state
#[derive(Debug, Serialize, Deserialize)]
pub struct ConsciousnessState {
    pub neuron_count: usize,
    pub memory_pattern_count: usize,
    pub emotional_state: EmotionalState,
    pub processing_stats: ProcessingStats,
    pub system_health: f64,
}

/// Consciousness-aware query processor trait
#[async_trait]
pub trait ConsciousnessAware {
    /// Process query with consciousness
    async fn process_with_consciousness(&self, query: &str) -> FusekiResult<QueryOptimization>;

    /// Get consciousness insights
    async fn get_consciousness_insights(&self) -> FusekiResult<ConsciousnessState>;

    /// Train consciousness from feedback
    async fn train_consciousness(&self, query: &str, feedback: f64) -> FusekiResult<()>;
}

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

    #[tokio::test]
    async fn test_artificial_neuron_creation() {
        let neuron = ArtificialNeuron::new(1, Some("test_specialization".to_string()));
        assert_eq!(neuron.id, 1);
        assert_eq!(
            neuron.specialization,
            Some("test_specialization".to_string())
        );
        assert!(neuron.activation >= 0.0 && neuron.activation <= 1.0);
    }

    #[tokio::test]
    async fn test_neuron_firing() {
        let mut neuron = ArtificialNeuron::new(1, None);
        neuron.threshold = 0.5;
        neuron.activation = 0.8;

        assert!(neuron.should_fire());
        assert_eq!(neuron.firing_history.len(), 1);
    }

    #[tokio::test]
    async fn test_synaptic_transmission() {
        let mut synapse = SynapticConnection::new(1, 2, 0.8, ConnectionType::Excitatory);
        let output = synapse.transmit(0.7);

        assert!(output > 0.0);
        assert!(synapse.transmission_count > 0);
    }

    #[tokio::test]
    async fn test_memory_pattern_creation() {
        let metrics = PerformanceMetrics::new();
        let pattern = MemoryPattern::new(
            "SELECT * WHERE { ?s ?p ?o }".to_string(),
            "standard_optimization".to_string(),
            metrics,
        );

        assert!(!pattern.id.is_empty());
        assert_eq!(pattern.frequency, 1);
        assert_eq!(pattern.strength, 1.0);
    }

    #[tokio::test]
    async fn test_emotional_state_update() {
        let mut state = EmotionalState::default();
        let mut metrics = PerformanceMetrics::new();
        metrics.success_rate = 0.9;
        metrics.satisfaction_score = 0.8;

        state.update_from_performance(&metrics);

        assert!(state.mood > 0.5);
        assert!(state.confidence > 0.5);
    }

    #[tokio::test]
    async fn test_consciousness_processor_creation() {
        let config = ConsciousnessConfig::default();
        let processor = ConsciousnessProcessor::new(config).await.unwrap();

        let state = processor.get_consciousness_state().await;
        assert!(state.neuron_count > 0);
        assert!(state.system_health >= 0.0);
    }

    #[tokio::test]
    async fn test_query_complexity_calculation() {
        let config = ConsciousnessConfig::default();
        let processor = ConsciousnessProcessor::new(config).await.unwrap();

        let simple_query = "SELECT * WHERE { ?s ?p ?o }";
        let complex_query = "SELECT * WHERE { ?s ?p ?o . OPTIONAL { ?s ?p2 ?o2 } UNION { ?s ?p3 ?o3 } FILTER(?o > 10) ORDER BY ?s }";

        let simple_score = processor.calculate_query_complexity(simple_query);
        let complex_score = processor.calculate_query_complexity(complex_query);

        assert!(complex_score > simple_score);
    }

    #[tokio::test]
    async fn test_memory_consolidation() {
        let config = ConsciousnessConfig::default();
        let processor = ConsciousnessProcessor::new(config).await.unwrap();

        // Add some test patterns
        let metrics = PerformanceMetrics::new();
        let pattern = MemoryPattern::new(
            "test query".to_string(),
            "test strategy".to_string(),
            metrics,
        );

        processor
            .memory_patterns
            .write()
            .await
            .insert(pattern.id.clone(), pattern);

        let result = processor.consolidate_memory().await;
        assert!(result.is_ok());
    }
}