oxirs-gql 0.2.4

//! Neuromorphic GraphQL Query Processor
//!
//! This module implements a brain-inspired query processing system that mimics
//! neural networks and cognitive patterns for adaptive GraphQL query optimization.

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

use crate::ai_query_predictor::AIQueryPredictor;

/// Configuration for neuromorphic query processing
#[derive(Debug, Clone)]
pub struct NeuromorphicConfig {
    pub enable_neural_adaptation: bool,
    pub enable_synaptic_plasticity: bool,
    pub enable_memory_formation: bool,
    pub enable_pattern_recognition: bool,
    pub enable_cognitive_caching: bool,
    pub neuron_count: usize,
    pub synapse_density: f64,
    pub learning_rate: f64,
    pub memory_retention: f64,
    pub adaptation_threshold: f64,
    pub plasticity_decay: f64,
    pub cognitive_depth: usize,
    pub pattern_recognition_sensitivity: f64,
}

impl Default for NeuromorphicConfig {
    fn default() -> Self {
        Self {
            enable_neural_adaptation: true,
            enable_synaptic_plasticity: true,
            enable_memory_formation: true,
            enable_pattern_recognition: true,
            enable_cognitive_caching: true,
            neuron_count: 1000,
            synapse_density: 0.1,
            learning_rate: 0.01,
            memory_retention: 0.95,
            adaptation_threshold: 0.7,
            plasticity_decay: 0.99,
            cognitive_depth: 5,
            pattern_recognition_sensitivity: 0.8,
        }
    }
}

/// Artificial neuron for query processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Neuron {
    pub id: usize,
    pub activation: f64,
    pub bias: f64,
    pub threshold: f64,
    pub memory_trace: f64,
    pub adaptation_strength: f64,
    pub last_activation_time: SystemTime,
    pub activation_count: u64,
    pub average_activation: f64,
}

impl Neuron {
    pub fn new(id: usize) -> Self {
        Self {
            id,
            activation: 0.0,
            bias: fastrand::f64() * 0.2 - 0.1, // Random bias [-0.1, 0.1]
            threshold: 0.5,
            memory_trace: 0.0,
            adaptation_strength: 1.0,
            last_activation_time: SystemTime::now(),
            activation_count: 0,
            average_activation: 0.0,
        }
    }

    /// Activate neuron with input signal
    pub fn activate(&mut self, input: f64, learning_rate: f64) -> f64 {
        let weighted_input = input + self.bias;

        // Sigmoid activation function with adaptation
        let raw_activation = 1.0 / (1.0 + (-weighted_input).exp());
        self.activation = raw_activation * self.adaptation_strength;

        // Update memory trace (exponential decay)
        self.memory_trace = self.memory_trace * 0.99 + self.activation * 0.01;

        // Synaptic plasticity - strengthen frequently used pathways
        if self.activation > self.threshold {
            self.adaptation_strength = (self.adaptation_strength + learning_rate * 0.1).min(2.0);
            self.activation_count += 1;
        } else {
            self.adaptation_strength = (self.adaptation_strength - learning_rate * 0.05).max(0.1);
        }

        // Update running average
        let count = self.activation_count.max(1) as f64;
        self.average_activation =
            (self.average_activation * (count - 1.0) + self.activation) / count;

        self.last_activation_time = SystemTime::now();
        self.activation
    }

    /// Apply homeostatic regulation
    pub fn homeostatic_regulation(&mut self, target_activity: f64) {
        let activity_diff = self.average_activation - target_activity;

        // Adjust threshold to maintain target activity level
        if activity_diff > 0.1 {
            self.threshold = (self.threshold + 0.01).min(0.9); // Increase threshold
        } else if activity_diff < -0.1 {
            self.threshold = (self.threshold - 0.01).max(0.1); // Decrease threshold
        }

        // Adjust bias for stability
        self.bias += -activity_diff * 0.001;
        self.bias = self.bias.clamp(-0.5, 0.5);
    }
}

/// Synaptic connection between neurons
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Synapse {
    pub from_neuron: usize,
    pub to_neuron: usize,
    pub weight: f64,
    pub strength: f64,
    pub plasticity: f64,
    pub last_transmission: SystemTime,
    pub transmission_count: u64,
    pub efficacy: f64,
}

impl Synapse {
    pub fn new(from_neuron: usize, to_neuron: usize) -> Self {
        Self {
            from_neuron,
            to_neuron,
            weight: fastrand::f64() * 2.0 - 1.0, // Random weight [-1.0, 1.0]
            strength: 1.0,
            plasticity: 1.0,
            last_transmission: SystemTime::now(),
            transmission_count: 0,
            efficacy: 1.0,
        }
    }

    /// Transmit signal through synapse with plasticity
    pub fn transmit(&mut self, signal: f64, learning_rate: f64) -> f64 {
        let transmitted_signal = signal * self.weight * self.strength * self.efficacy;

        // Hebbian learning: "neurons that fire together, wire together"
        if signal > 0.5 && transmitted_signal > 0.0 {
            self.weight += learning_rate * signal * self.plasticity;
            self.strength = (self.strength + learning_rate * 0.1).min(2.0);
        } else if signal > 0.5 && transmitted_signal < 0.0 {
            // Anti-Hebbian learning for negative correlations
            self.weight -= learning_rate * signal * self.plasticity * 0.5;
            self.strength = (self.strength - learning_rate * 0.05).max(0.1);
        }

        // Weight bounds
        self.weight = self.weight.clamp(-2.0, 2.0);

        // Synaptic plasticity decay
        self.plasticity *= 0.999;
        self.plasticity = self.plasticity.max(0.1);

        // Update transmission statistics
        self.transmission_count += 1;
        self.last_transmission = SystemTime::now();

        // Synaptic efficacy based on usage
        let time_since_last = SystemTime::now()
            .duration_since(self.last_transmission)
            .unwrap_or(Duration::from_secs(0))
            .as_secs() as f64;

        self.efficacy = (1.0 / (1.0 + time_since_last / 3600.0)).max(0.1); // Decay over time

        transmitted_signal
    }
}

/// Memory pattern for query recognition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPattern {
    pub pattern_id: String,
    pub activation_pattern: Vec<f64>,
    pub query_features: HashMap<String, f64>,
    pub performance_metrics: HashMap<String, f64>,
    pub formation_time: SystemTime,
    pub access_count: u64,
    pub strength: f64,
    pub consolidation_level: f64,
}

impl MemoryPattern {
    pub fn new(pattern_id: String, activation_pattern: Vec<f64>) -> Self {
        Self {
            pattern_id,
            activation_pattern,
            query_features: HashMap::new(),
            performance_metrics: HashMap::new(),
            formation_time: SystemTime::now(),
            access_count: 0,
            strength: 1.0,
            consolidation_level: 0.0,
        }
    }

    /// Calculate similarity to another pattern
    pub fn similarity(&self, other: &MemoryPattern) -> f64 {
        if self.activation_pattern.len() != other.activation_pattern.len() {
            return 0.0;
        }

        let dot_product: f64 = self
            .activation_pattern
            .iter()
            .zip(other.activation_pattern.iter())
            .map(|(a, b)| a * b)
            .sum();

        let norm_self: f64 = self
            .activation_pattern
            .iter()
            .map(|x| x * x)
            .sum::<f64>()
            .sqrt();
        let norm_other: f64 = other
            .activation_pattern
            .iter()
            .map(|x| x * x)
            .sum::<f64>()
            .sqrt();

        if norm_self == 0.0 || norm_other == 0.0 {
            return 0.0;
        }

        dot_product / (norm_self * norm_other)
    }

    /// Strengthen memory through access
    pub fn access(&mut self) {
        self.access_count += 1;
        self.strength = (self.strength + 0.1).min(2.0);

        // Memory consolidation over time and usage
        let age = SystemTime::now()
            .duration_since(self.formation_time)
            .unwrap_or(Duration::from_secs(0))
            .as_secs() as f64;

        let usage_factor = (self.access_count as f64).ln().max(1.0);
        self.consolidation_level = ((age / 3600.0) * usage_factor * 0.1).min(1.0);
    }

    /// Memory decay over time
    pub fn decay(&mut self, decay_rate: f64) {
        self.strength *= 1.0 - decay_rate;
        self.strength = self.strength.max(0.1);
    }
}

/// Neuromorphic query processor
pub struct NeuromorphicQueryProcessor {
    config: NeuromorphicConfig,
    neurons: Arc<AsyncRwLock<Vec<Neuron>>>,
    synapses: Arc<AsyncRwLock<Vec<Synapse>>>,
    memory_patterns: Arc<AsyncRwLock<HashMap<String, MemoryPattern>>>,
    #[allow(dead_code)]
    ai_predictor: Arc<AIQueryPredictor>,
    processing_stats: Arc<AsyncRwLock<ProcessingStats>>,
}

/// Processing statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessingStats {
    pub total_queries_processed: u64,
    pub pattern_matches_found: u64,
    pub new_patterns_formed: u64,
    pub average_processing_time: Duration,
    pub neural_efficiency: f64,
    pub memory_utilization: f64,
    pub adaptation_events: u64,
}

impl Default for ProcessingStats {
    fn default() -> Self {
        Self {
            total_queries_processed: 0,
            pattern_matches_found: 0,
            new_patterns_formed: 0,
            average_processing_time: Duration::from_millis(0),
            neural_efficiency: 0.0,
            memory_utilization: 0.0,
            adaptation_events: 0,
        }
    }
}

/// Query processing result with neuromorphic insights
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicResult {
    pub processing_time: Duration,
    pub neural_activation_pattern: Vec<f64>,
    pub pattern_matches: Vec<String>,
    pub cognitive_load: f64,
    pub memory_formation: bool,
    pub adaptation_triggered: bool,
    pub optimization_suggestions: Vec<String>,
    pub confidence_score: f64,
}

impl NeuromorphicQueryProcessor {
    /// Create a new neuromorphic query processor
    pub async fn new(
        config: NeuromorphicConfig,
        ai_predictor: Arc<AIQueryPredictor>,
    ) -> Result<Self> {
        // Initialize neurons
        let mut neurons = Vec::with_capacity(config.neuron_count);
        for i in 0..config.neuron_count {
            neurons.push(Neuron::new(i));
        }

        // Initialize synapses with specified density
        let mut synapses = Vec::new();
        let total_possible_connections = config.neuron_count * (config.neuron_count - 1);
        let target_connections =
            (total_possible_connections as f64 * config.synapse_density) as usize;

        for _ in 0..target_connections {
            let from = fastrand::usize(0..config.neuron_count);
            let to = fastrand::usize(0..config.neuron_count);

            if from != to {
                synapses.push(Synapse::new(from, to));
            }
        }

        Ok(Self {
            config,
            neurons: Arc::new(AsyncRwLock::new(neurons)),
            synapses: Arc::new(AsyncRwLock::new(synapses)),
            memory_patterns: Arc::new(AsyncRwLock::new(HashMap::new())),
            ai_predictor,
            processing_stats: Arc::new(AsyncRwLock::new(ProcessingStats::default())),
        })
    }

    /// Process a GraphQL query using neuromorphic computation
    pub async fn process_query(&self, query: &str) -> Result<NeuromorphicResult> {
        let start_time = Instant::now();

        // Extract query features
        let query_features = self.extract_query_features(query);

        // Create input activation pattern
        let input_pattern = self.create_input_pattern(&query_features);

        // Process through neural network
        let neural_activation_pattern = self.neural_forward_pass(&input_pattern).await?;

        // Pattern recognition and memory retrieval
        let pattern_matches = self.recognize_patterns(&neural_activation_pattern).await?;

        // Memory formation for new patterns
        let memory_formation = self
            .form_memory_if_novel(&neural_activation_pattern, &query_features)
            .await?;

        // Neural adaptation
        let adaptation_triggered = self.neural_adaptation(&neural_activation_pattern).await?;

        // Calculate cognitive load
        let cognitive_load = self.calculate_cognitive_load(&neural_activation_pattern);

        // Generate optimization suggestions
        let optimization_suggestions = self
            .generate_neuromorphic_optimizations(&neural_activation_pattern, &pattern_matches)
            .await?;

        // Calculate confidence score
        let confidence_score =
            self.calculate_confidence(&neural_activation_pattern, &pattern_matches);

        let processing_time = start_time.elapsed();

        // Update statistics
        self.update_processing_stats(
            processing_time,
            &pattern_matches,
            memory_formation,
            adaptation_triggered,
        )
        .await?;

        Ok(NeuromorphicResult {
            processing_time,
            neural_activation_pattern,
            pattern_matches,
            cognitive_load,
            memory_formation,
            adaptation_triggered,
            optimization_suggestions,
            confidence_score,
        })
    }

    /// Extract features from GraphQL query
    fn extract_query_features(&self, query: &str) -> HashMap<String, f64> {
        let mut features = HashMap::new();

        // Basic query characteristics
        features.insert("length".to_string(), query.len() as f64);
        features.insert("complexity".to_string(), query.matches('{').count() as f64);
        features.insert(
            "depth".to_string(),
            query.matches("  ").count() as f64 / 2.0,
        );

        // Operation types
        let operations = ["query", "mutation", "subscription", "fragment"];
        for op in &operations {
            features.insert(
                format!("has_{op}"),
                if query.to_lowercase().contains(op) {
                    1.0
                } else {
                    0.0
                },
            );
        }

        // Field patterns
        let field_patterns = ["user", "order", "product", "id", "name", "email"];
        for pattern in &field_patterns {
            features.insert(
                format!("field_{pattern}"),
                query.to_lowercase().matches(pattern).count() as f64,
            );
        }

        // Directive usage
        let directives = ["@include", "@skip", "@deprecated"];
        for directive in &directives {
            features.insert(
                format!("directive_{}", directive.replace("@", "")),
                query.matches(directive).count() as f64,
            );
        }

        features
    }

    /// Create neural input pattern from query features
    fn create_input_pattern(&self, features: &HashMap<String, f64>) -> Vec<f64> {
        let mut pattern = vec![0.0; self.config.neuron_count];

        // Map features to neural inputs using hash-based distribution
        for (feature_name, &value) in features {
            let hash = self.simple_hash(feature_name) % self.config.neuron_count;
            pattern[hash] = value / 100.0; // Normalize
        }

        // Add some noise for robustness
        for value in &mut pattern {
            *value += (fastrand::f64() - 0.5) * 0.01;
        }

        pattern
    }

    /// Simple hash function for feature mapping
    fn simple_hash(&self, s: &str) -> usize {
        s.bytes().fold(0usize, |acc, b| {
            acc.wrapping_mul(31).wrapping_add(b as usize)
        })
    }

    /// Neural network forward pass
    async fn neural_forward_pass(&self, input: &[f64]) -> Result<Vec<f64>> {
        let mut neurons = self.neurons.write().await;
        let synapses = self.synapses.read().await;

        // Initialize neuron activations
        for (i, neuron) in neurons.iter_mut().enumerate() {
            if i < input.len() {
                neuron.activation = input[i];
            } else {
                neuron.activation = 0.0;
            }
        }

        // Process through network layers
        for _layer in 0..self.config.cognitive_depth {
            let current_activations: Vec<f64> = neurons.iter().map(|n| n.activation).collect();

            // Calculate new activations based on synaptic inputs
            for neuron in neurons.iter_mut() {
                let mut synaptic_input = 0.0;
                let mut input_count = 0;

                for synapse in synapses.iter() {
                    if synapse.to_neuron == neuron.id {
                        if let Some(&from_activation) = current_activations.get(synapse.from_neuron)
                        {
                            synaptic_input += from_activation * synapse.weight * synapse.strength;
                            input_count += 1;
                        }
                    }
                }

                // Average synaptic input
                if input_count > 0 {
                    synaptic_input /= input_count as f64;
                }

                // Activate neuron
                neuron.activate(synaptic_input, self.config.learning_rate);

                // Apply homeostatic regulation
                neuron.homeostatic_regulation(0.1); // Target 10% activity
            }
        }

        // Return final activation pattern
        Ok(neurons.iter().map(|n| n.activation).collect())
    }

    /// Recognize patterns in neural activation
    async fn recognize_patterns(&self, activation: &[f64]) -> Result<Vec<String>> {
        let memory_patterns = self.memory_patterns.read().await;
        let mut matches = Vec::new();

        for (pattern_id, pattern) in memory_patterns.iter() {
            if activation.len() == pattern.activation_pattern.len() {
                let similarity =
                    self.calculate_pattern_similarity(activation, &pattern.activation_pattern);

                if similarity > self.config.pattern_recognition_sensitivity {
                    matches.push(pattern_id.clone());
                }
            }
        }

        Ok(matches)
    }

    /// Calculate similarity between activation patterns
    fn calculate_pattern_similarity(&self, pattern1: &[f64], pattern2: &[f64]) -> f64 {
        if pattern1.len() != pattern2.len() {
            return 0.0;
        }

        let dot_product: f64 = pattern1
            .iter()
            .zip(pattern2.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm1: f64 = pattern1.iter().map(|x| x * x).sum::<f64>().sqrt();
        let norm2: f64 = pattern2.iter().map(|x| x * x).sum::<f64>().sqrt();

        if norm1 == 0.0 || norm2 == 0.0 {
            return 0.0;
        }

        dot_product / (norm1 * norm2)
    }

    /// Form memory for novel patterns
    async fn form_memory_if_novel(
        &self,
        activation: &[f64],
        features: &HashMap<String, f64>,
    ) -> Result<bool> {
        let mut memory_patterns = self.memory_patterns.write().await;

        // Check if pattern is sufficiently novel
        let mut max_similarity: f64 = 0.0;
        for pattern in memory_patterns.values() {
            let similarity =
                self.calculate_pattern_similarity(activation, &pattern.activation_pattern);
            max_similarity = max_similarity.max(similarity);
        }

        if max_similarity < 0.8 {
            // Novel enough to store
            let pattern_id = format!("pattern_{}", memory_patterns.len());
            let mut new_pattern = MemoryPattern::new(pattern_id.clone(), activation.to_vec());
            new_pattern.query_features = features.clone();

            memory_patterns.insert(pattern_id, new_pattern);

            info!(
                "New memory pattern formed with similarity threshold: {:.3}",
                max_similarity
            );
            return Ok(true);
        }

        Ok(false)
    }

    /// Neural adaptation based on experience
    async fn neural_adaptation(&self, activation: &[f64]) -> Result<bool> {
        let mut adaptation_triggered = false;

        // Adaptive threshold based on activation statistics
        let mean_activation = activation.iter().sum::<f64>() / activation.len() as f64;
        let activation_variance = activation
            .iter()
            .map(|x| (x - mean_activation).powi(2))
            .sum::<f64>()
            / activation.len() as f64;

        if activation_variance > self.config.adaptation_threshold {
            // High variance indicates need for adaptation
            let mut synapses = self.synapses.write().await;

            // Adapt synaptic weights
            for synapse in synapses.iter_mut() {
                if fastrand::f64() < 0.1 {
                    // 10% chance of adaptation per synapse
                    synapse.plasticity *= 1.1; // Increase plasticity temporarily
                    adaptation_triggered = true;
                }
            }

            info!(
                "Neural adaptation triggered due to high activation variance: {:.3}",
                activation_variance
            );
        }

        Ok(adaptation_triggered)
    }

    /// Calculate cognitive load
    fn calculate_cognitive_load(&self, activation: &[f64]) -> f64 {
        // Cognitive load based on activation intensity and distribution
        let total_activation: f64 = activation.iter().map(|x| x.abs()).sum();
        let active_neurons = activation.iter().filter(|&&x| x > 0.1).count();
        let activation_entropy = self.calculate_entropy(activation);

        // Combine metrics
        let load = (total_activation / activation.len() as f64)
            * (active_neurons as f64 / activation.len() as f64)
            * activation_entropy;

        load.min(1.0)
    }

    /// Calculate entropy of activation pattern
    fn calculate_entropy(&self, activation: &[f64]) -> f64 {
        let sum: f64 = activation.iter().map(|x| x.abs()).sum();
        if sum == 0.0 {
            return 0.0;
        }

        let probabilities: Vec<f64> = activation
            .iter()
            .map(|x| x.abs() / sum)
            .filter(|&p| p > 1e-10)
            .collect();

        -probabilities.iter().map(|&p| p * p.ln()).sum::<f64>()
    }

    /// Generate neuromorphic optimization suggestions
    async fn generate_neuromorphic_optimizations(
        &self,
        activation: &[f64],
        pattern_matches: &[String],
    ) -> Result<Vec<String>> {
        let mut suggestions = Vec::new();

        // Analyze activation patterns for optimization opportunities
        let high_activation_regions = activation
            .iter()
            .enumerate()
            .filter(|&(_, &v)| v > 0.8)
            .map(|(i, _)| i)
            .collect::<Vec<_>>();

        if high_activation_regions.len() > activation.len() / 4 {
            suggestions
                .push("High neural load detected - consider query simplification".to_string());
        }

        if !pattern_matches.is_empty() {
            suggestions.push(format!(
                "Pattern match found - reuse cached results from {} similar queries",
                pattern_matches.len()
            ));
        }

        // Memory-based suggestions
        let memory_patterns = self.memory_patterns.read().await;
        if memory_patterns.len() > 100 {
            suggestions
                .push("Rich memory bank available - enable pattern-based optimization".to_string());
        }

        // Neural efficiency suggestions
        let efficiency = self.calculate_neural_efficiency(activation);
        if efficiency < 0.5 {
            suggestions.push("Low neural efficiency - consider network pruning".to_string());
        }

        suggestions
            .push("Apply neuromorphic query reordering based on activation patterns".to_string());

        Ok(suggestions)
    }

    /// Calculate neural efficiency
    fn calculate_neural_efficiency(&self, activation: &[f64]) -> f64 {
        let active_count = activation.iter().filter(|&&x| x > 0.1).count();
        let total_activation: f64 = activation.iter().sum();

        if active_count == 0 {
            return 0.0;
        }

        // Efficiency as useful activation per active neuron
        total_activation / active_count as f64
    }

    /// Calculate confidence score
    fn calculate_confidence(&self, activation: &[f64], pattern_matches: &[String]) -> f64 {
        let activation_strength =
            activation.iter().map(|x| x.abs()).sum::<f64>() / activation.len() as f64;
        let pattern_boost = if pattern_matches.is_empty() { 0.0 } else { 0.3 };
        let activation_consistency =
            1.0 - self.calculate_entropy(activation) / activation.len() as f64;

        (activation_strength + pattern_boost + activation_consistency) / 3.0
    }

    /// Update processing statistics
    async fn update_processing_stats(
        &self,
        processing_time: Duration,
        pattern_matches: &[String],
        memory_formation: bool,
        adaptation_triggered: bool,
    ) -> Result<()> {
        let mut stats = self.processing_stats.write().await;

        stats.total_queries_processed += 1;
        stats.pattern_matches_found += pattern_matches.len() as u64;

        if memory_formation {
            stats.new_patterns_formed += 1;
        }

        if adaptation_triggered {
            stats.adaptation_events += 1;
        }

        // Update average processing time
        let count = stats.total_queries_processed as f64;
        let current_avg_millis = stats.average_processing_time.as_millis() as f64;
        let new_avg_millis =
            (current_avg_millis * (count - 1.0) + processing_time.as_millis() as f64) / count;
        stats.average_processing_time = Duration::from_millis(new_avg_millis as u64);

        // Calculate neural efficiency and memory utilization
        let neurons = self.neurons.read().await;
        let active_neurons = neurons.iter().filter(|n| n.activation > 0.1).count();
        stats.neural_efficiency = active_neurons as f64 / neurons.len() as f64;

        let memory_patterns = self.memory_patterns.read().await;
        stats.memory_utilization = memory_patterns.len() as f64 / 1000.0; // Assuming max 1000 patterns

        Ok(())
    }

    /// Get processing statistics
    pub async fn get_processing_stats(&self) -> ProcessingStats {
        self.processing_stats.read().await.clone()
    }

    /// Get memory patterns
    pub async fn get_memory_patterns(&self) -> HashMap<String, MemoryPattern> {
        self.memory_patterns.read().await.clone()
    }

    /// Perform memory consolidation
    pub async fn consolidate_memory(&self) -> Result<()> {
        let mut memory_patterns = self.memory_patterns.write().await;

        // Apply memory decay
        for pattern in memory_patterns.values_mut() {
            pattern.decay(1.0 - self.config.memory_retention);
        }

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

        info!(
            "Memory consolidation completed. {} patterns retained",
            memory_patterns.len()
        );
        Ok(())
    }
}

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

    #[tokio::test]
    async fn test_neuromorphic_processor_creation() {
        let config = NeuromorphicConfig::default();
        let ai_config = AIQueryPredictorConfig::default();
        let ai_predictor = Arc::new(AIQueryPredictor::new(ai_config));

        let processor = NeuromorphicQueryProcessor::new(config, ai_predictor).await;
        assert!(processor.is_ok());
    }

    #[tokio::test]
    async fn test_query_processing() {
        let config = NeuromorphicConfig::default();
        let ai_config = AIQueryPredictorConfig::default();
        let ai_predictor = Arc::new(AIQueryPredictor::new(ai_config));

        let processor = NeuromorphicQueryProcessor::new(config, ai_predictor)
            .await
            .expect("should succeed");

        let query = "query { user(id: 1) { name email orders { id total } } }";
        let result = processor.process_query(query).await;

        assert!(result.is_ok());
        let result = result.expect("should succeed");
        assert!(!result.neural_activation_pattern.is_empty());
        assert!(result.confidence_score >= 0.0 && result.confidence_score <= 1.0);
    }

    #[tokio::test]
    async fn test_feature_extraction() {
        let config = NeuromorphicConfig::default();
        let ai_config = AIQueryPredictorConfig::default();
        let ai_predictor = Arc::new(AIQueryPredictor::new(ai_config));

        let processor = NeuromorphicQueryProcessor::new(config, ai_predictor)
            .await
            .expect("should succeed");

        let query = "query getUserOrders($userId: ID!) { user(id: $userId) { orders @include(if: true) { id } } }";
        let features = processor.extract_query_features(query);

        assert!(features.contains_key("length"));
        assert!(features.contains_key("has_query"));
        assert!(features.get("has_query").expect("should succeed") > &0.0);
    }

    #[tokio::test]
    async fn test_memory_consolidation() {
        let config = NeuromorphicConfig::default();
        let ai_config = AIQueryPredictorConfig::default();
        let ai_predictor = Arc::new(AIQueryPredictor::new(ai_config));

        let processor = NeuromorphicQueryProcessor::new(config, ai_predictor)
            .await
            .expect("should succeed");

        // Process several queries to form memory patterns
        let queries = [
            "query { user { name } }",
            "query { orders { total } }",
            "mutation { createUser(input: {}) { id } }",
        ];

        for query in &queries {
            let _ = processor
                .process_query(query)
                .await
                .expect("should succeed");
        }

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