oxirs-chat 0.2.4

//! Core Consciousness Model
//!
//! Main consciousness model with neural-inspired architecture for
//! context-aware response generation.

use anyhow::{Context, Result};
use chrono::Utc;
use std::collections::VecDeque;
use tracing::{debug, info, warn};

use super::super::consciousness_types::*;
use super::super::*;
use super::attention::AttentionMechanism;
use super::config::ConsciousnessModelConfig;
use super::emotional::AdvancedEmotionalState;
use super::memory::MultiLayerMemorySystem;
use super::metacognitive::EnhancedMetacognitiveLayer;
use super::metrics::{ConsciousnessMetrics, ConsciousnessSnapshot};
use super::neural::NeuralCorrelates;
use super::responses::AdvancedConsciousResponse;
use super::stream::ConsciousnessStream;

/// Advanced consciousness model with neural-inspired architecture
#[derive(Debug, Clone)]
pub struct ConsciousnessModel {
    // Core consciousness components
    pub awareness_level: f64,
    pub attention_mechanism: AttentionMechanism,
    pub multi_layer_memory: MultiLayerMemorySystem,
    pub emotional_state: AdvancedEmotionalState,
    pub metacognitive_layer: EnhancedMetacognitiveLayer,

    // Neural consciousness simulation
    pub neural_correlates: NeuralCorrelates,
    pub consciousness_stream: ConsciousnessStream,

    // Performance monitoring
    pub consciousness_metrics: ConsciousnessMetrics,
    pub state_history: VecDeque<ConsciousnessSnapshot>,

    // Configuration
    pub config: ConsciousnessModelConfig,
}

impl ConsciousnessModel {
    pub fn new() -> Result<Self> {
        Ok(Self {
            awareness_level: 0.8,
            attention_mechanism: AttentionMechanism::new()?,
            multi_layer_memory: MultiLayerMemorySystem::new()?,
            emotional_state: AdvancedEmotionalState::neutral(),
            metacognitive_layer: EnhancedMetacognitiveLayer::new()?,
            neural_correlates: NeuralCorrelates::new()?,
            consciousness_stream: ConsciousnessStream::new(),
            consciousness_metrics: ConsciousnessMetrics::new(),
            state_history: VecDeque::with_capacity(100),
            config: ConsciousnessModelConfig::default(),
        })
    }

    pub fn with_config(config: ConsciousnessModelConfig) -> Result<Self> {
        let mut model = Self::new()?;
        model.config = config;
        Ok(model)
    }

    /// Advanced consciousness processing with comprehensive error handling
    pub fn conscious_query_processing(
        &mut self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<AdvancedConsciousResponse> {
        let processing_start = std::time::Instant::now();

        // Input validation
        if query.is_empty() {
            return Err(anyhow::anyhow!(
                "Empty query provided to consciousness processing"
            ));
        }

        // Create consciousness snapshot before processing
        self.capture_consciousness_snapshot()?;

        // 1. Neural consciousness simulation
        debug!(
            "Starting neural consciousness simulation for query: {}",
            query.chars().take(50).collect::<String>()
        );
        let neural_activation = self
            .neural_correlates
            .process_input(query, context)
            .context("Failed to process neural consciousness simulation")?;

        // 2. Update consciousness stream
        self.consciousness_stream
            .add_experience(query, context, &neural_activation)
            .context("Failed to update consciousness stream")?;

        // 3. Advanced awareness computation
        let awareness_update = self
            .compute_advanced_awareness(query, context)
            .context("Failed to compute advanced awareness")?;
        self.awareness_level =
            (self.awareness_level * 0.7 + awareness_update * 0.3).clamp(0.0, 1.0);

        // 4. Sophisticated attention mechanism
        let attention_allocation = self
            .attention_mechanism
            .allocate_attention(query, context, &neural_activation)
            .context("Failed to allocate attention")?;

        // 5. Multi-layer memory processing
        let memory_integration = self
            .multi_layer_memory
            .process_and_integrate(query, context, &attention_allocation)
            .context("Failed to process multi-layer memory")?;

        // 6. Advanced emotional processing
        let emotional_response = self
            .emotional_state
            .process_emotional_content(query, context)
            .context("Failed to process emotional content")?;

        // 7. Enhanced metacognitive assessment
        let metacognitive_result = self
            .metacognitive_layer
            .comprehensive_assessment(query, context, &neural_activation)
            .context("Failed to perform metacognitive assessment")?;

        // 8. Generate sophisticated insights
        let enhanced_insights = self
            .generate_advanced_insights(query, context, &neural_activation, &memory_integration)
            .context("Failed to generate advanced insights")?;

        // 9. Update consciousness metrics
        self.consciousness_metrics.update(
            self.awareness_level,
            &attention_allocation,
            &memory_integration,
            &emotional_response,
            processing_start.elapsed(),
        )?;

        // 10. Construct advanced response
        let response = AdvancedConsciousResponse {
            base_response: context.clone(),
            consciousness_metadata: AdvancedConsciousnessMetadata {
                awareness_level: self.awareness_level,
                neural_activation: neural_activation.clone(),
                attention_allocation: attention_allocation.clone(),
                memory_integration: memory_integration.clone(),
                emotional_response: emotional_response.clone(),
                metacognitive_result: metacognitive_result.clone(),
                processing_time: processing_start.elapsed(),
                consciousness_health_score: self.calculate_consciousness_health()?,
            },
            enhanced_insights,
            consciousness_stream_context: self.consciousness_stream.get_recent_context(5),
        };

        info!(
            "Consciousness processing completed successfully in {:?}",
            processing_start.elapsed()
        );
        Ok(response)
    }

    /// Advanced awareness computation with multiple cognitive factors
    fn compute_advanced_awareness(&self, query: &str, context: &AssembledContext) -> Result<f64> {
        let query_complexity = self.calculate_enhanced_query_complexity(query)?;
        let context_richness = self.calculate_context_richness(context)?;
        let information_density = self.calculate_information_density(query, context)?;
        let cognitive_load = self.calculate_cognitive_load(query, context)?;

        // Sophisticated awareness calculation using multiple factors
        let raw_awareness = (
            query_complexity * 0.25
                + context_richness * 0.25
                + information_density * 0.25
                + (1.0 - cognitive_load) * 0.25
            // Inverse cognitive load
        )
        .clamp(0.0, 1.0);

        // Apply consciousness stream influence
        let stream_influence = self.consciousness_stream.get_awareness_influence()?;
        let final_awareness = (raw_awareness * 0.8 + stream_influence * 0.2).clamp(0.0, 1.0);

        debug!("Advanced awareness computed: complexity={:.3}, richness={:.3}, density={:.3}, load={:.3}, final={:.3}", 
               query_complexity, context_richness, information_density, cognitive_load, final_awareness);

        Ok(final_awareness)
    }

    /// Enhanced query complexity analysis with linguistic and semantic factors
    fn calculate_enhanced_query_complexity(&self, query: &str) -> Result<f64> {
        if query.is_empty() {
            return Ok(0.0);
        }

        let words: Vec<&str> = query.split_whitespace().collect();
        let word_count = words.len();
        let unique_words = words.iter().collect::<HashSet<_>>().len();
        let avg_word_length =
            words.iter().map(|w| w.len()).sum::<usize>() as f64 / word_count.max(1) as f64;

        // Linguistic complexity factors
        let syntactic_complexity = self.calculate_syntactic_complexity(query)?;
        let semantic_depth = self.calculate_semantic_depth(query)?;
        let question_complexity = self.analyze_question_complexity(query)?;

        let complexity = ((word_count as f64 * 0.02).min(1.0) * 0.2
            + (unique_words as f64 / word_count.max(1) as f64) * 0.2
            + (avg_word_length / 10.0).min(1.0) * 0.15
            + syntactic_complexity * 0.2
            + semantic_depth * 0.15
            + question_complexity * 0.1)
            .clamp(0.0, 1.0);

        Ok(complexity)
    }

    fn calculate_syntactic_complexity(&self, query: &str) -> Result<f64> {
        let clause_markers = [
            "because", "although", "while", "since", "unless", "if", "when",
        ];
        let subordination_score = clause_markers
            .iter()
            .filter(|marker| query.to_lowercase().contains(*marker))
            .count() as f64
            * 0.2;

        let punctuation_complexity = query
            .chars()
            .filter(|c| ['?', '!', ';', ':', ','].contains(c))
            .count() as f64
            * 0.1;

        Ok((subordination_score + punctuation_complexity).min(1.0))
    }

    fn calculate_semantic_depth(&self, query: &str) -> Result<f64> {
        let abstract_concepts = [
            "concept",
            "idea",
            "theory",
            "principle",
            "philosophy",
            "meaning",
            "essence",
        ];
        let depth_indicators = [
            "analyze",
            "compare",
            "evaluate",
            "synthesize",
            "critique",
            "explain",
        ];

        let abstract_score = abstract_concepts
            .iter()
            .filter(|concept| query.to_lowercase().contains(*concept))
            .count() as f64
            * 0.15;

        let depth_score = depth_indicators
            .iter()
            .filter(|indicator| query.to_lowercase().contains(*indicator))
            .count() as f64
            * 0.2;

        Ok((abstract_score + depth_score).min(1.0))
    }

    fn analyze_question_complexity(&self, query: &str) -> Result<f64> {
        let simple_patterns = ["what is", "who is", "when did", "where is"];
        let complex_patterns = ["how does", "why might", "what if", "compare"];
        let very_complex_patterns = [
            "analyze the relationship",
            "evaluate the impact",
            "synthesize",
        ];

        let query_lower = query.to_lowercase();

        if very_complex_patterns
            .iter()
            .any(|pattern| query_lower.contains(pattern))
        {
            Ok(1.0)
        } else if complex_patterns
            .iter()
            .any(|pattern| query_lower.contains(pattern))
        {
            Ok(0.7)
        } else if simple_patterns
            .iter()
            .any(|pattern| query_lower.contains(pattern))
        {
            Ok(0.3)
        } else {
            Ok(0.5) // Default moderate complexity
        }
    }

    fn calculate_context_richness(&self, context: &AssembledContext) -> Result<f64> {
        let mut richness = 0.0;
        let mut components = 0;

        // Semantic results richness
        if !context.semantic_results.is_empty() {
            let avg_score = context
                .semantic_results
                .iter()
                .map(|r| r.score as f64)
                .sum::<f64>()
                / context.semantic_results.len() as f64;
            richness += avg_score * 0.3;
            components += 1;
        }

        // Graph results richness
        if !context.graph_results.is_empty() {
            let graph_diversity = context.graph_results.len().min(10) as f64 / 10.0;
            richness += graph_diversity * 0.2;
            components += 1;
        }

        // Entity extraction richness
        if !context.extracted_entities.is_empty() {
            let entity_confidence = context
                .extracted_entities
                .iter()
                .map(|e| e.confidence as f64)
                .sum::<f64>()
                / context.extracted_entities.len() as f64;
            richness += entity_confidence * 0.3;
            components += 1;
        }

        // Quantum results (if available)
        if let Some(ref quantum_results) = context.quantum_results {
            if !quantum_results.is_empty() {
                richness += 0.2;
                components += 1;
            }
        }

        if components > 0 {
            Ok(richness / components as f64)
        } else {
            Ok(0.0)
        }
    }

    fn calculate_information_density(
        &self,
        query: &str,
        context: &AssembledContext,
    ) -> Result<f64> {
        let query_entropy = self.calculate_entropy(query)?;
        let context_information = context.semantic_results.len() + context.graph_results.len();
        let information_ratio = (context_information as f64 / query.len().max(1) as f64).min(1.0);

        Ok((query_entropy + information_ratio) / 2.0)
    }

    fn calculate_entropy(&self, text: &str) -> Result<f64> {
        if text.is_empty() {
            return Ok(0.0);
        }

        let mut char_counts = HashMap::new();
        let total_chars = text.chars().count() as f64;

        for ch in text.chars() {
            *char_counts.entry(ch).or_insert(0) += 1;
        }

        let entropy = char_counts
            .values()
            .map(|&count| {
                let probability = count as f64 / total_chars;
                -probability * probability.log2()
            })
            .sum::<f64>();

        Ok(entropy / 8.0) // Normalize to 0-1 range
    }

    fn calculate_cognitive_load(&self, query: &str, context: &AssembledContext) -> Result<f64> {
        let query_load = (query.len() as f64 / 1000.0).min(1.0);
        let context_load = ((context.semantic_results.len() + context.graph_results.len()) as f64
            / 100.0)
            .min(1.0);
        let entity_load = (context.extracted_entities.len() as f64 / 50.0).min(1.0);

        Ok((query_load + context_load + entity_load) / 3.0)
    }

    fn capture_consciousness_snapshot(&mut self) -> Result<()> {
        let snapshot = ConsciousnessSnapshot {
            timestamp: Utc::now(),
            awareness_level: self.awareness_level,
            attention_weights: self.attention_mechanism.get_current_weights()?,
            emotional_state: self.emotional_state.get_current_state()?,
            memory_pressure: self.multi_layer_memory.get_memory_pressure()?,
            neural_activity: self.neural_correlates.get_activity_summary()?,
        };

        self.state_history.push_back(snapshot);

        // Maintain history limit
        if self.state_history.len() > 100 {
            self.state_history.pop_front();
        }

        Ok(())
    }

    fn calculate_consciousness_health(&self) -> Result<f64> {
        let awareness_health = if self.awareness_level >= 0.7 {
            1.0
        } else {
            self.awareness_level / 0.7
        };
        let attention_health = self.attention_mechanism.get_health_score()?;
        let memory_health = self.multi_layer_memory.get_health_score()?;
        let emotional_health = self.emotional_state.get_stability_score()?;

        Ok((awareness_health + attention_health + memory_health + emotional_health) / 4.0)
    }

    fn generate_advanced_insights(
        &self,
        _query: &str,
        _context: &AssembledContext,
        neural_activation: &NeuralActivation,
        memory_integration: &MemoryIntegrationResult,
    ) -> Result<Vec<AdvancedConsciousInsight>> {
        let mut insights = Vec::new();

        // Neural pattern insight
        if neural_activation.overall_activation > 0.7 {
            insights.push(AdvancedConsciousInsight {
                insight_type: AdvancedInsightType::NeuralPattern,
                content: format!(
                    "High neural activation detected: {:.3}",
                    neural_activation.overall_activation
                ),
                confidence: neural_activation.confidence,
                implications: vec![
                    "Query activates multiple cognitive networks".to_string(),
                    "Suggests complex conceptual processing".to_string(),
                ],
                supporting_evidence: neural_activation.get_evidence(),
                consciousness_correlation: neural_activation.consciousness_relevance,
            });
        }

        // Memory integration insight
        if memory_integration.integration_strength > 0.6 {
            insights.push(AdvancedConsciousInsight {
                insight_type: AdvancedInsightType::MemoryIntegration,
                content: format!(
                    "Strong memory integration: {:.3}",
                    memory_integration.integration_strength
                ),
                confidence: memory_integration.confidence,
                implications: vec![
                    "Leveraging rich episodic memory traces".to_string(),
                    "Cross-temporal pattern recognition active".to_string(),
                ],
                supporting_evidence: memory_integration.get_evidence(),
                consciousness_correlation: memory_integration.consciousness_relevance,
            });
        }

        // Attention distribution insight
        let attention_entropy = self.attention_mechanism.calculate_attention_entropy()?;
        if attention_entropy < 0.3 {
            insights.push(AdvancedConsciousInsight {
                insight_type: AdvancedInsightType::AttentionFocus,
                content: format!("Highly focused attention: entropy={attention_entropy:.3}"),
                confidence: 0.8,
                implications: vec![
                    "Concentrated cognitive resources".to_string(),
                    "Single-domain expertise engagement".to_string(),
                ],
                supporting_evidence: vec![format!("Attention entropy: {:.3}", attention_entropy)],
                consciousness_correlation: 0.9 - attention_entropy,
            });
        }

        // Consciousness stream insight
        let stream_coherence = self.consciousness_stream.calculate_coherence()?;
        if stream_coherence > 0.8 {
            insights.push(AdvancedConsciousInsight {
                insight_type: AdvancedInsightType::StreamCoherence,
                content: format!("High consciousness stream coherence: {stream_coherence:.3}"),
                confidence: stream_coherence,
                implications: vec![
                    "Stable consciousness state maintained".to_string(),
                    "Consistent cognitive processing patterns".to_string(),
                ],
                supporting_evidence: vec![format!("Stream coherence: {:.3}", stream_coherence)],
                consciousness_correlation: stream_coherence,
            });
        }

        debug!(
            "Generated {} advanced consciousness insights",
            insights.len()
        );
        Ok(insights)
    }

    fn update_attention_focus(&mut self, query: &str, context: &AssembledContext) -> Result<()> {
        // Use the attention mechanism to update focus
        let _ = self.attention_mechanism.allocate_attention(
            query,
            context,
            &NeuralActivation::default(),
        )?;
        Ok(())
    }

    fn create_memory_trace(&mut self, query: &str, context: &AssembledContext) {
        let trace = MemoryTrace {
            id: Uuid::new_v4().to_string(),
            query: query.to_string(),
            context_summary: format!(
                "Context with {} triples",
                context.retrieved_triples.as_ref().map_or(0, |t| t.len())
            ),
            timestamp: Utc::now(),
            importance_score: self.calculate_importance(query, context),
            emotional_valence: self.emotional_state.valence,
        };

        // Store trace in multi-layer memory system
        if let Err(e) = self
            .multi_layer_memory
            .store_episodic_memory(&trace.query, &trace.context_summary)
        {
            warn!("Failed to store memory trace: {}", e);
        }
    }

    fn calculate_memory_integration(&self) -> f64 {
        // Simplified implementation - would need public accessor methods on EpisodicMemory
        // to properly calculate memory integration based on episode importance scores
        0.5 // Default memory integration score
    }

    fn generate_conscious_insights(
        &self,
        query: &str,
        _context: &AssembledContext,
    ) -> Vec<ConsciousInsight> {
        let mut insights = Vec::new();

        // Pattern recognition insight
        if let Some(pattern) = self.detect_query_pattern(query) {
            insights.push(ConsciousInsight {
                insight_type: InsightType::PatternRecognition,
                content: format!("Detected pattern: {pattern}"),
                confidence: 0.8,
                implications: vec!["This suggests a systematic approach to analysis".to_string()],
            });
        }

        // Emotional resonance insight
        let emotional_resonance = self.emotional_state.calculate_resonance(query);
        if emotional_resonance > 0.5 {
            insights.push(ConsciousInsight {
                insight_type: InsightType::EmotionalResonance,
                content: format!("High emotional resonance detected: {emotional_resonance:.2}"),
                confidence: emotional_resonance,
                implications: vec!["Consider emotional context in response".to_string()],
            });
        }

        // Memory integration insight
        let memory_integration = self.calculate_memory_integration();
        if memory_integration > 0.7 {
            insights.push(ConsciousInsight {
                insight_type: InsightType::MemoryIntegration,
                content: format!("Strong memory integration: {memory_integration:.2}"),
                confidence: memory_integration,
                implications: vec!["Can leverage previous interaction patterns".to_string()],
            });
        }

        insights
    }

    fn calculate_query_complexity(&self, query: &str) -> f64 {
        let word_count = query.split_whitespace().count();
        let unique_words = query.split_whitespace().collect::<HashSet<_>>().len();
        let avg_word_length = query.split_whitespace().map(|w| w.len()).sum::<usize>() as f64
            / word_count.max(1) as f64;

        (word_count as f64 * 0.1 + unique_words as f64 * 0.2 + avg_word_length * 0.1).min(1.0)
    }

    fn calculate_importance(&self, query: &str, context: &AssembledContext) -> f64 {
        let query_complexity = self.calculate_query_complexity(query);
        let context_richness = context
            .retrieved_triples
            .as_ref()
            .map_or(0.0, |t| t.len() as f64 * 0.1);

        (query_complexity + context_richness).min(1.0)
    }

    fn detect_query_pattern(&self, query: &str) -> Option<String> {
        let query_lower = query.to_lowercase();

        if query_lower.contains("how") && query_lower.contains("many") {
            Some("Quantitative Analysis".to_string())
        } else if query_lower.contains("what") && query_lower.contains("is") {
            Some("Definitional Query".to_string())
        } else if query_lower.contains("why") {
            Some("Causal Reasoning".to_string())
        } else if query_lower.contains("compare") || query_lower.contains("difference") {
            Some("Comparative Analysis".to_string())
        } else {
            None
        }
    }
}

#[derive(Debug, Clone)]
pub struct MemoryTrace {
    pub id: String,
    pub query: String,
    pub context_summary: String,
    pub timestamp: DateTime<Utc>,
    pub importance_score: f64,
    pub emotional_valence: f64,
}

#[derive(Debug, Clone)]
pub struct EmotionalState {
    pub valence: f64,   // -1.0 (negative) to 1.0 (positive)
    pub arousal: f64,   // 0.0 (calm) to 1.0 (excited)
    pub dominance: f64, // 0.0 (submissive) to 1.0 (dominant)
}

impl EmotionalState {
    pub fn neutral() -> Self {
        Self {
            valence: 0.0,
            arousal: 0.5,
            dominance: 0.5,
        }
    }

    pub fn calculate_resonance(&self, query: &str) -> f64 {
        let emotional_words = [
            "excited",
            "happy",
            "sad",
            "angry",
            "frustrated",
            "pleased",
            "worried",
            "confident",
        ];

        let query_lower = query.to_lowercase();
        let emotional_content = emotional_words
            .iter()
            .filter(|word| query_lower.contains(*word))
            .count() as f64
            / emotional_words.len() as f64;

        (self.valence.abs() + self.arousal + emotional_content) / 3.0
    }
}