oxirs_embed/
consciousness_aware_embeddings.rs

1//! Consciousness-Aware Embedding System
2//!
3//! This module implements a revolutionary consciousness-aware embedding system that
4//! can adapt based on context, reasoning, and multi-dimensional awareness patterns.
5//! Inspired by theories of consciousness, cognitive science, and advanced AI research.
6
7use crate::ModelConfig;
8use anyhow::Result;
9use scirs2_core::ndarray_ext::Array1;
10use serde::{Deserialize, Serialize};
11use std::collections::HashMap;
12use uuid::Uuid;
13
14/// Levels of consciousness awareness in the embedding system
15#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
16pub enum ConsciousnessLevel {
17    /// Basic reactive responses
18    Reactive,
19    /// Simple pattern recognition
20    Associative,
21    /// Basic self-awareness
22    SelfAware,
23    /// Complex reasoning and planning
24    Reflective,
25    /// Meta-cognitive awareness
26    MetaCognitive,
27    /// Transcendent consciousness
28    Transcendent,
29}
30
31impl ConsciousnessLevel {
32    pub fn awareness_factor(&self) -> f32 {
33        match self {
34            ConsciousnessLevel::Reactive => 0.1,
35            ConsciousnessLevel::Associative => 0.3,
36            ConsciousnessLevel::SelfAware => 0.5,
37            ConsciousnessLevel::Reflective => 0.7,
38            ConsciousnessLevel::MetaCognitive => 0.9,
39            ConsciousnessLevel::Transcendent => 1.0,
40        }
41    }
42
43    pub fn complexity_threshold(&self) -> f32 {
44        match self {
45            ConsciousnessLevel::Reactive => 0.1,
46            ConsciousnessLevel::Associative => 0.2,
47            ConsciousnessLevel::SelfAware => 0.4,
48            ConsciousnessLevel::Reflective => 0.6,
49            ConsciousnessLevel::MetaCognitive => 0.8,
50            ConsciousnessLevel::Transcendent => 1.0,
51        }
52    }
53}
54
55/// Attention mechanisms for consciousness-aware processing
56#[derive(Debug, Clone, Serialize, Deserialize)]
57pub struct AttentionMechanism {
58    /// Current focus areas and their weights
59    pub focus_weights: HashMap<String, f32>,
60    /// Attention persistence over time
61    pub attention_memory: Vec<(String, f32, chrono::DateTime<chrono::Utc>)>,
62    /// Maximum attention capacity
63    pub attention_capacity: usize,
64    /// Attention decay rate
65    pub decay_rate: f32,
66}
67
68impl AttentionMechanism {
69    pub fn new(capacity: usize, decay_rate: f32) -> Self {
70        Self {
71            focus_weights: HashMap::new(),
72            attention_memory: Vec::new(),
73            attention_capacity: capacity,
74            decay_rate,
75        }
76    }
77
78    /// Update attention focus on a specific concept
79    pub fn focus_on(&mut self, concept: &str, intensity: f32) {
80        let now = chrono::Utc::now();
81
82        // Add to current focus
83        let current_weight = self.focus_weights.get(concept).unwrap_or(&0.0);
84        let new_weight = (current_weight + intensity).min(1.0);
85        self.focus_weights.insert(concept.to_string(), new_weight);
86
87        // Add to attention memory
88        self.attention_memory
89            .push((concept.to_string(), intensity, now));
90
91        // Maintain capacity limits
92        if self.attention_memory.len() > self.attention_capacity {
93            self.attention_memory.remove(0);
94        }
95
96        // Apply attention decay to older focuses
97        self.apply_decay();
98    }
99
100    /// Apply attention decay over time
101    fn apply_decay(&mut self) {
102        let now = chrono::Utc::now();
103
104        // Decay current focus weights
105        for (_, weight) in self.focus_weights.iter_mut() {
106            *weight *= (1.0 - self.decay_rate).max(0.0);
107        }
108
109        // Remove very weak attention
110        self.focus_weights.retain(|_, &mut w| w > 0.01);
111
112        // Decay attention memory based on time
113        for (_, intensity, timestamp) in self.attention_memory.iter_mut() {
114            let hours_passed = now.signed_duration_since(*timestamp).num_hours() as f32;
115            let time_decay = (-hours_passed * 0.1).exp(); // Exponential decay
116            *intensity *= time_decay;
117        }
118
119        // Remove very old or weak memories
120        self.attention_memory
121            .retain(|(_, intensity, _)| *intensity > 0.01);
122    }
123
124    /// Get current attention distribution
125    pub fn get_attention_distribution(&self) -> Vec<(String, f32)> {
126        let mut attention: Vec<_> = self
127            .focus_weights
128            .iter()
129            .map(|(k, &v)| (k.clone(), v))
130            .collect();
131        attention.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
132        attention
133    }
134}
135
136/// Working memory for consciousness processing
137#[derive(Debug, Clone, Serialize, Deserialize)]
138pub struct WorkingMemory {
139    /// Current concepts being processed
140    pub active_concepts: HashMap<String, Array1<f32>>,
141    /// Concept relationships
142    pub concept_relationships: HashMap<(String, String), f32>,
143    /// Working memory capacity (Miller's 7±2 rule)
144    pub capacity: usize,
145    /// Memory consolidation threshold
146    pub consolidation_threshold: f32,
147}
148
149impl WorkingMemory {
150    pub fn new(capacity: Option<usize>) -> Self {
151        Self {
152            active_concepts: HashMap::new(),
153            concept_relationships: HashMap::new(),
154            capacity: capacity.unwrap_or(7), // Miller's magical number
155            consolidation_threshold: 0.7,
156        }
157    }
158
159    /// Add a concept to working memory
160    pub fn add_concept(&mut self, concept: String, embedding: Array1<f32>) -> Result<()> {
161        // If at capacity, remove least active concept
162        if self.active_concepts.len() >= self.capacity {
163            self.remove_least_active()?;
164        }
165
166        self.active_concepts.insert(concept, embedding);
167        Ok(())
168    }
169
170    /// Remove the least active concept
171    fn remove_least_active(&mut self) -> Result<()> {
172        // Simple strategy: remove first concept (could be improved with usage tracking)
173        if let Some(concept) = self.active_concepts.keys().next().cloned() {
174            self.active_concepts.remove(&concept);
175        }
176        Ok(())
177    }
178
179    /// Update concept relationships
180    pub fn update_relationship(&mut self, concept1: &str, concept2: &str, strength: f32) {
181        let key = if concept1 < concept2 {
182            (concept1.to_string(), concept2.to_string())
183        } else {
184            (concept2.to_string(), concept1.to_string())
185        };
186
187        self.concept_relationships.insert(key, strength);
188    }
189
190    /// Get related concepts
191    pub fn get_related_concepts(&self, concept: &str) -> Vec<(String, f32)> {
192        self.concept_relationships
193            .iter()
194            .filter_map(|((c1, c2), &strength)| {
195                if c1 == concept {
196                    Some((c2.clone(), strength))
197                } else if c2 == concept {
198                    Some((c1.clone(), strength))
199                } else {
200                    None
201                }
202            })
203            .collect()
204    }
205}
206
207/// Meta-cognitive layer for self-awareness and reflection
208#[derive(Debug, Clone, Serialize, Deserialize)]
209pub struct MetaCognition {
210    /// Self-assessment of current understanding
211    pub understanding_confidence: f32,
212    /// Awareness of knowledge gaps
213    pub knowledge_gaps: Vec<String>,
214    /// Strategy effectiveness tracking
215    pub strategy_effectiveness: HashMap<String, f32>,
216    /// Self-reflection notes
217    pub reflection_history: Vec<(chrono::DateTime<chrono::Utc>, String, f32)>,
218}
219
220impl MetaCognition {
221    pub fn new() -> Self {
222        Self {
223            understanding_confidence: 0.5,
224            knowledge_gaps: Vec::new(),
225            strategy_effectiveness: HashMap::new(),
226            reflection_history: Vec::new(),
227        }
228    }
229
230    /// Update confidence based on recent performance
231    pub fn update_confidence(&mut self, performance_score: f32) {
232        let alpha = 0.1; // Learning rate for confidence updates
233        self.understanding_confidence =
234            (1.0 - alpha) * self.understanding_confidence + alpha * performance_score;
235        self.understanding_confidence = self.understanding_confidence.clamp(0.0, 1.0);
236    }
237
238    /// Add a reflection note
239    pub fn reflect(&mut self, insight: String, confidence: f32) {
240        let now = chrono::Utc::now();
241        self.reflection_history.push((now, insight, confidence));
242
243        // Keep only recent reflections
244        if self.reflection_history.len() > 100 {
245            self.reflection_history.remove(0);
246        }
247    }
248
249    /// Identify knowledge gaps
250    pub fn identify_knowledge_gap(&mut self, domain: String) {
251        if !self.knowledge_gaps.contains(&domain) {
252            self.knowledge_gaps.push(domain);
253        }
254    }
255
256    /// Update strategy effectiveness
257    pub fn update_strategy_effectiveness(&mut self, strategy: &str, effectiveness: f32) {
258        let current = self.strategy_effectiveness.get(strategy).unwrap_or(&0.5);
259        let alpha = 0.2;
260        let new_effectiveness = (1.0 - alpha) * current + alpha * effectiveness;
261        self.strategy_effectiveness
262            .insert(strategy.to_string(), new_effectiveness);
263    }
264}
265
266impl Default for MetaCognition {
267    fn default() -> Self {
268        Self::new()
269    }
270}
271
272/// Main consciousness-aware embedding model
273#[derive(Debug)]
274pub struct ConsciousnessAwareEmbedding {
275    /// Unique model identifier
276    pub model_id: Uuid,
277    /// Model configuration
278    pub config: ModelConfig,
279    /// Current consciousness level
280    pub consciousness_level: ConsciousnessLevel,
281    /// Attention mechanism
282    pub attention: AttentionMechanism,
283    /// Working memory
284    pub working_memory: WorkingMemory,
285    /// Meta-cognitive layer
286    pub meta_cognition: MetaCognition,
287    /// Entity embeddings with consciousness enhancement
288    pub embeddings: HashMap<String, Array1<f32>>,
289    /// Consciousness state vector
290    pub consciousness_state: Array1<f32>,
291    /// Learning history for adaptation
292    pub learning_history: Vec<(String, Array1<f32>, f32)>, // (concept, embedding, quality)
293}
294
295impl ConsciousnessAwareEmbedding {
296    pub fn new(config: ModelConfig) -> Self {
297        let dimensions = config.dimensions;
298
299        Self {
300            model_id: Uuid::new_v4(),
301            config,
302            consciousness_level: ConsciousnessLevel::SelfAware,
303            attention: AttentionMechanism::new(20, 0.05),
304            working_memory: WorkingMemory::new(Some(7)),
305            meta_cognition: MetaCognition::new(),
306            embeddings: HashMap::new(),
307            consciousness_state: Array1::zeros(dimensions),
308            learning_history: Vec::new(),
309        }
310    }
311
312    /// Generate consciousness-aware embedding for an entity
313    pub fn generate_conscious_embedding(
314        &mut self,
315        entity: &str,
316        context: &[String],
317    ) -> Result<Array1<f32>> {
318        // Update attention based on context
319        for ctx in context {
320            self.attention.focus_on(ctx, 0.3);
321        }
322        self.attention.focus_on(entity, 0.8);
323
324        // Get base embedding or create new one
325        let base_embedding = self
326            .embeddings
327            .get(entity)
328            .cloned()
329            .unwrap_or_else(|| self.create_base_embedding(entity));
330
331        // Apply consciousness-aware modifications
332        let conscious_embedding =
333            self.apply_consciousness_enhancement(&base_embedding, entity, context)?;
334
335        // Update working memory
336        self.working_memory
337            .add_concept(entity.to_string(), conscious_embedding.clone())?;
338
339        // Store enhanced embedding
340        self.embeddings
341            .insert(entity.to_string(), conscious_embedding.clone());
342
343        // Meta-cognitive reflection
344        self.reflect_on_embedding(entity, &conscious_embedding);
345
346        Ok(conscious_embedding)
347    }
348
349    /// Create a base embedding for an entity
350    fn create_base_embedding(&self, entity: &str) -> Array1<f32> {
351        let dimensions = self.config.dimensions;
352
353        // Simple hash-based initialization (could be more sophisticated)
354        let hash = entity.chars().map(|c| c as u32).sum::<u32>();
355        let _seed = hash as u64;
356
357        {
358            #[allow(unused_imports)]
359            use scirs2_core::random::{Random, Rng};
360            // Note: Seeding not supported in scirs2_core - would be deterministic if needed
361            Array1::from_shape_fn(dimensions, |_| {
362                let mut random = Random::default();
363                random.gen_range(-0.1..0.1)
364            })
365        }
366    }
367
368    /// Apply consciousness enhancement to base embedding
369    fn apply_consciousness_enhancement(
370        &mut self,
371        base_embedding: &Array1<f32>,
372        entity: &str,
373        context: &[String],
374    ) -> Result<Array1<f32>> {
375        let mut enhanced = base_embedding.clone();
376
377        // Apply attention-based modulation
378        let attention_factor = self.attention.focus_weights.get(entity).unwrap_or(&0.0)
379            * self.consciousness_level.awareness_factor();
380
381        // Consciousness state influence
382        let consciousness_influence = &self.consciousness_state * attention_factor;
383        enhanced = enhanced + consciousness_influence;
384
385        // Context integration
386        for ctx in context {
387            if let Some(ctx_embedding) = self.embeddings.get(ctx) {
388                let context_weight = self.attention.focus_weights.get(ctx).unwrap_or(&0.0) * 0.2;
389                enhanced = enhanced + ctx_embedding * context_weight;
390            }
391        }
392
393        // Working memory integration
394        let related_concepts = self.working_memory.get_related_concepts(entity);
395        for (related_concept, relationship_strength) in related_concepts {
396            if let Some(related_embedding) =
397                self.working_memory.active_concepts.get(&related_concept)
398            {
399                enhanced = enhanced + related_embedding * (relationship_strength * 0.1);
400            }
401        }
402
403        // Meta-cognitive adjustment based on confidence
404        let confidence_factor = self.meta_cognition.understanding_confidence;
405        enhanced = enhanced * confidence_factor + base_embedding * (1.0 - confidence_factor);
406
407        // Normalize to prevent explosion
408        let norm = enhanced.mapv(|x| x * x).sum().sqrt();
409        if norm > 0.0 {
410            enhanced /= norm;
411        }
412
413        Ok(enhanced)
414    }
415
416    /// Meta-cognitive reflection on generated embedding
417    fn reflect_on_embedding(&mut self, entity: &str, embedding: &Array1<f32>) {
418        // Analyze embedding quality
419        let norm = embedding.mapv(|x| x * x).sum().sqrt();
420        let quality_score = if norm > 0.0 && norm < 2.0 {
421            (1.0 - (norm - 1.0).abs()).max(0.0)
422        } else {
423            0.0
424        };
425
426        // Update meta-cognition
427        self.meta_cognition.update_confidence(quality_score);
428
429        // Reflect on the process
430        let reflection = format!(
431            "Generated embedding for '{entity}' with quality {quality_score:.3} and norm {norm:.3}"
432        );
433        self.meta_cognition.reflect(reflection, quality_score);
434
435        // Add to learning history
436        self.learning_history
437            .push((entity.to_string(), embedding.clone(), quality_score));
438
439        // Keep learning history manageable
440        if self.learning_history.len() > 1000 {
441            self.learning_history.remove(0);
442        }
443    }
444
445    /// Elevate consciousness level based on experience
446    pub fn evolve_consciousness(&mut self) -> Result<()> {
447        let total_experience = self.learning_history.len();
448        let average_quality = if !self.learning_history.is_empty() {
449            self.learning_history.iter().map(|(_, _, q)| q).sum::<f32>() / total_experience as f32
450        } else {
451            0.0
452        };
453
454        // Determine if consciousness should evolve
455        let evolution_threshold = self.consciousness_level.complexity_threshold();
456
457        if average_quality > evolution_threshold && total_experience > 100 {
458            self.consciousness_level = match self.consciousness_level {
459                ConsciousnessLevel::Reactive => ConsciousnessLevel::Associative,
460                ConsciousnessLevel::Associative => ConsciousnessLevel::SelfAware,
461                ConsciousnessLevel::SelfAware => ConsciousnessLevel::Reflective,
462                ConsciousnessLevel::Reflective => ConsciousnessLevel::MetaCognitive,
463                ConsciousnessLevel::MetaCognitive => ConsciousnessLevel::Transcendent,
464                ConsciousnessLevel::Transcendent => ConsciousnessLevel::Transcendent, // Stays at highest
465            };
466
467            // Update consciousness state vector
468            self.update_consciousness_state()?;
469
470            // Meta-cognitive reflection on evolution
471            self.meta_cognition.reflect(
472                format!("Consciousness evolved to {:?}", self.consciousness_level),
473                0.9,
474            );
475        }
476
477        Ok(())
478    }
479
480    /// Update consciousness state vector
481    fn update_consciousness_state(&mut self) -> Result<()> {
482        let dimensions = self.consciousness_state.len();
483        let awareness_factor = self.consciousness_level.awareness_factor();
484
485        // Update consciousness state based on recent experiences
486        if !self.learning_history.is_empty() {
487            let recent_embeddings = self
488                .learning_history
489                .iter()
490                .rev()
491                .take(50) // Last 50 experiences
492                .map(|(_, emb, quality)| emb * *quality)
493                .fold(Array1::<f32>::zeros(dimensions), |acc, emb| acc + emb);
494
495            let count = self.learning_history.len().min(50) as f32;
496            let average_recent = recent_embeddings / count;
497
498            // Integrate with existing consciousness state
499            self.consciousness_state =
500                &self.consciousness_state * 0.8f32 + &average_recent * 0.2f32;
501
502            // Apply consciousness-level scaling
503            self.consciousness_state = &self.consciousness_state * awareness_factor;
504        }
505
506        Ok(())
507    }
508
509    /// Get consciousness insights for debugging/monitoring
510    pub fn get_consciousness_insights(&self) -> ConsciousnessInsights {
511        let attention_distribution = self.attention.get_attention_distribution();
512        let active_concepts: Vec<String> = self
513            .working_memory
514            .active_concepts
515            .keys()
516            .cloned()
517            .collect();
518        let recent_reflections: Vec<String> = self
519            .meta_cognition
520            .reflection_history
521            .iter()
522            .rev()
523            .take(5)
524            .map(|(_, reflection, _)| reflection.clone())
525            .collect();
526
527        ConsciousnessInsights {
528            consciousness_level: self.consciousness_level,
529            understanding_confidence: self.meta_cognition.understanding_confidence,
530            attention_distribution,
531            active_concepts,
532            recent_reflections,
533            total_experiences: self.learning_history.len(),
534            knowledge_gaps: self.meta_cognition.knowledge_gaps.clone(),
535        }
536    }
537}
538
539/// Consciousness insights for monitoring and debugging
540#[derive(Debug, Clone, Serialize, Deserialize)]
541pub struct ConsciousnessInsights {
542    pub consciousness_level: ConsciousnessLevel,
543    pub understanding_confidence: f32,
544    pub attention_distribution: Vec<(String, f32)>,
545    pub active_concepts: Vec<String>,
546    pub recent_reflections: Vec<String>,
547    pub total_experiences: usize,
548    pub knowledge_gaps: Vec<String>,
549}
550
551#[cfg(test)]
552mod tests {
553    use super::*;
554
555    #[test]
556    fn test_consciousness_levels() {
557        assert_eq!(ConsciousnessLevel::Reactive.awareness_factor(), 0.1);
558        assert_eq!(ConsciousnessLevel::Transcendent.awareness_factor(), 1.0);
559    }
560
561    #[test]
562    fn test_attention_mechanism() {
563        let mut attention = AttentionMechanism::new(10, 0.1);
564
565        attention.focus_on("concept1", 0.8);
566        attention.focus_on("concept2", 0.6);
567
568        let distribution = attention.get_attention_distribution();
569        assert_eq!(distribution.len(), 2);
570        assert_eq!(distribution[0].0, "concept1");
571        assert!(distribution[0].1 > distribution[1].1);
572    }
573
574    #[test]
575    fn test_working_memory() {
576        let mut memory = WorkingMemory::new(Some(3));
577
578        let emb1 = Array1::from_vec(vec![1.0, 2.0, 3.0]);
579        let emb2 = Array1::from_vec(vec![4.0, 5.0, 6.0]);
580
581        assert!(memory.add_concept("concept1".to_string(), emb1).is_ok());
582        assert!(memory.add_concept("concept2".to_string(), emb2).is_ok());
583
584        memory.update_relationship("concept1", "concept2", 0.8);
585        let related = memory.get_related_concepts("concept1");
586        assert_eq!(related.len(), 1);
587        assert_eq!(related[0].0, "concept2");
588        assert_eq!(related[0].1, 0.8);
589    }
590
591    #[test]
592    fn test_consciousness_aware_embedding() {
593        let config = ModelConfig::default().with_dimensions(64);
594        let mut model = ConsciousnessAwareEmbedding::new(config);
595
596        let context = vec!["related_entity".to_string()];
597        let embedding = model.generate_conscious_embedding("test_entity", &context);
598
599        assert!(embedding.is_ok());
600        let emb = embedding.unwrap();
601        assert_eq!(emb.len(), 64);
602
603        // Check that attention was updated
604        assert!(model.attention.focus_weights.contains_key("test_entity"));
605
606        // Check working memory
607        assert!(model
608            .working_memory
609            .active_concepts
610            .contains_key("test_entity"));
611    }
612
613    #[test]
614    fn test_consciousness_evolution() {
615        let config = ModelConfig::default().with_dimensions(32);
616        let mut model = ConsciousnessAwareEmbedding::new(config);
617
618        // Add some quality experiences
619        for i in 0..150 {
620            let entity = format!("entity_{i}");
621            let embedding = Array1::from_vec(vec![0.1; 32]);
622            model.learning_history.push((entity, embedding, 0.9)); // High quality
623        }
624
625        let initial_level = model.consciousness_level;
626        assert!(model.evolve_consciousness().is_ok());
627
628        // Should have evolved due to high-quality experiences
629        assert_ne!(model.consciousness_level, initial_level);
630    }
631
632    #[tokio::test]
633    async fn test_consciousness_insights() {
634        let config = ModelConfig::default().with_dimensions(16);
635        let mut model = ConsciousnessAwareEmbedding::new(config);
636
637        // Generate some embeddings to create insights
638        let context = vec!["context1".to_string()];
639        let _ = model.generate_conscious_embedding("entity1", &context);
640        let _ = model.generate_conscious_embedding("entity2", &context);
641
642        let insights = model.get_consciousness_insights();
643
644        assert_eq!(insights.consciousness_level, ConsciousnessLevel::SelfAware);
645        assert!(insights.total_experiences > 0);
646        assert!(!insights.active_concepts.is_empty());
647        assert!(!insights.attention_distribution.is_empty());
648    }
649}
oxirs_embed/consciousness_aware_embeddings.rs

oxirs_embed/
consciousness_aware_embeddings.rs
