# SONA Component Integration ## Overview This document details how SONA integrates with the ruvector ecosystem and exo-ai cognitive crates to create a unified self-improving architecture. ## Integration Architecture ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ SONA Integration Layer │ ├─────────────────────────────────────────────────────────────────────────┤ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Learning │ │ Router │ │ Attention │ │ Memory │ │ │ │ Engine │ │ Engine │ │ Engine │ │ Engine │ │ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ ├─────────┼────────────────┼────────────────┼────────────────┼───────────┤ │ │ │ │ │ │ │ ┌──────▼──────────────────────────────────────────────────▼──────┐ │ │ │ ruvector Crates │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ │ │ core │ │attention│ │ gnn │ │postgres │ │ sparse │ │ │ │ │ │ (HNSW) │ │(39 mech)│ │ (GNN) │ │(persist)│ │(vectors)│ │ │ │ │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │ │ └────────────────────────────────────────────────────────────────┘ │ │ │ │ ┌────────────────────────────────────────────────────────────────┐ │ │ │ exo-ai Crates │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ │ │exo-core │ │temporal │ │ exotic │ │ memory │ │attention│ │ │ │ │ │ (IIT/Φ) │ │(cycles) │ │(quantum)│ │(dreams) │ │ (39) │ │ │ │ │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │ │ └────────────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ## ruvector Crate Integration ### 1. ruvector-core (HNSW Index) **Purpose**: High-performance approximate nearest neighbor search for pattern retrieval. ```rust use ruvector_core::{HnswIndex, Distance, SearchParams}; /// Pattern index using HNSW for sub-millisecond retrieval pub struct PatternIndex { index: HnswIndex, config: HnswConfig, metrics: IndexMetrics, } impl PatternIndex { pub fn new(dim: usize, max_patterns: usize) -> Self { Self { index: HnswIndex::new(HnswConfig { m: 16, // Connections per node ef_construction: 200, // Build quality ef_search: 50, // Search quality max_elements: max_patterns, dimension: dim, }), config: HnswConfig::default(), metrics: IndexMetrics::default(), } } /// Add pattern embedding to index pub fn add_pattern(&mut self, id: u64, embedding: &[f32]) -> Result<(), IndexError> { self.index.insert(id, embedding)?; self.metrics.total_patterns += 1; Ok(()) } /// Find k nearest patterns pub fn find_similar(&self, query: &[f32], k: usize) -> Vec<(u64, f32)> { self.index.search(query, k, SearchParams { ef: self.config.ef_search, }) } /// Batch search for multiple queries pub fn batch_search(&self, queries: &[Vec], k: usize) -> Vec> { queries.par_iter() .map(|q| self.find_similar(q, k)) .collect() } } #[derive(Default)] pub struct IndexMetrics { pub total_patterns: usize, pub avg_search_time_us: f64, pub cache_hit_rate: f32, } ``` **Integration Points**: - ReasoningBank pattern storage - Dream memory retrieval - Router context lookup ### 2. ruvector-attention (39 Mechanisms) **Purpose**: Diverse attention mechanisms for different reasoning patterns. ```rust use ruvector_attention::{ AttentionMechanism, MultiHeadAttention, LinearAttention, SparseAttention, FlashAttention, KernelizedAttention }; /// Adaptive attention selector based on query characteristics pub struct AdaptiveAttention { mechanisms: Vec>, router: AttentionRouter, performance_tracker: PerformanceTracker, } impl AdaptiveAttention { pub fn new(hidden_dim: usize, num_heads: usize) -> Self { Self { mechanisms: vec![ Box::new(MultiHeadAttention::new(hidden_dim, num_heads)), Box::new(LinearAttention::new(hidden_dim)), Box::new(SparseAttention::new(hidden_dim, 0.1)), // 10% sparsity Box::new(FlashAttention::new(hidden_dim, num_heads)), Box::new(KernelizedAttention::new(hidden_dim, "elu")), ], router: AttentionRouter::new(5), performance_tracker: PerformanceTracker::new(), } } /// Select optimal attention mechanism based on context pub fn forward(&mut self, q: &Tensor, k: &Tensor, v: &Tensor) -> Tensor { // Analyze query characteristics let features = self.analyze_query(q); // Route to best mechanism let mechanism_idx = self.router.route(&features); // Execute attention let start = Instant::now(); let output = self.mechanisms[mechanism_idx].forward(q, k, v); let elapsed = start.elapsed(); // Track performance self.performance_tracker.record(mechanism_idx, elapsed); output } fn analyze_query(&self, q: &Tensor) -> AttentionFeatures { AttentionFeatures { sequence_length: q.shape()[1], sparsity: q.sparsity_ratio(), entropy: q.attention_entropy(), locality: q.attention_locality(), } } } /// Routes queries to optimal attention mechanism pub struct AttentionRouter { weights: Vec, history: CircularBuffer, } impl AttentionRouter { pub fn route(&self, features: &AttentionFeatures) -> usize { // Decision logic based on features if features.sequence_length > 4096 { 2 // SparseAttention for long sequences } else if features.sparsity > 0.5 { 2 // SparseAttention for sparse patterns } else if features.locality > 0.8 { 3 // FlashAttention for local patterns } else { 0 // Default MultiHeadAttention } } pub fn update_from_feedback(&mut self, decision: usize, quality: f32) { self.history.push(RoutingDecision { decision, quality }); // Online learning of routing weights self.weights[decision] += 0.01 * (quality - self.weights[decision]); } } ``` **Integration Points**: - Query processing pipeline - Dream pattern recognition - Cross-memory attention ### 3. ruvector-gnn (Graph Neural Networks) **Purpose**: Graph-based reasoning over knowledge structures. ```rust use ruvector_gnn::{GraphConv, GraphAttention, MessagePassing}; /// Knowledge graph reasoning with GNN pub struct KnowledgeGraph { nodes: HashMap, edges: Vec, gnn: GraphNeuralNetwork, graph_index: GraphIndex, } #[derive(Clone)] pub struct NodeEmbedding { pub id: NodeId, pub embedding: Vec, pub node_type: NodeType, pub importance: f32, pub last_accessed: Instant, } #[derive(Clone, Copy)] pub enum NodeType { Concept, Pattern, Episode, Procedure, Dream, } impl KnowledgeGraph { pub fn new(embedding_dim: usize, hidden_dim: usize) -> Self { Self { nodes: HashMap::new(), edges: Vec::new(), gnn: GraphNeuralNetwork::new(embedding_dim, hidden_dim, 3), // 3 layers graph_index: GraphIndex::new(), } } /// Add node to knowledge graph pub fn add_node(&mut self, id: NodeId, embedding: Vec, node_type: NodeType) { let node = NodeEmbedding { id, embedding: embedding.clone(), node_type, importance: 1.0, last_accessed: Instant::now(), }; self.nodes.insert(id, node); self.graph_index.add(id, &embedding); } /// Create edge between nodes pub fn add_edge(&mut self, from: NodeId, to: NodeId, edge_type: EdgeType, weight: f32) { self.edges.push(Edge { from, to, edge_type, weight }); } /// Propagate information through graph pub fn propagate(&mut self, query: &[f32], hops: usize) -> Vec<(NodeId, f32)> { // Find seed nodes let seeds = self.graph_index.search(query, 10); // Message passing through GNN layers let mut activations = HashMap::new(); for (node_id, score) in seeds { activations.insert(node_id, score); } for _hop in 0..hops { let new_activations = self.gnn.propagate(&self.nodes, &self.edges, &activations); activations = new_activations; } // Return top activated nodes let mut results: Vec<_> = activations.into_iter().collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results.truncate(20); results } /// Learn new edge from pattern pub fn learn_edge(&mut self, pattern: &LearnedPattern) { // Extract node relationships from pattern let source_nodes = self.find_related_nodes(&pattern.centroid, pattern.cluster_size); for i in 0..source_nodes.len() { for j in i+1..source_nodes.len() { let strength = cosine_similarity( &self.nodes[&source_nodes[i]].embedding, &self.nodes[&source_nodes[j]].embedding ); if strength > 0.7 { self.add_edge( source_nodes[i], source_nodes[j], EdgeType::Pattern, strength ); } } } } } /// Graph neural network with multiple layer types pub struct GraphNeuralNetwork { layers: Vec, aggregator: Aggregator, } enum GnnLayer { GraphConv(GraphConv), GraphAttention(GraphAttention), MessagePassing(MessagePassing), } ``` **Integration Points**: - Knowledge representation - Dream creative connections - Pattern relationship discovery ### 4. ruvector-postgres (Persistence) **Purpose**: Durable storage for learned knowledge. ```rust use ruvector_postgres::{PgVector, PgStore, VectorIndex}; /// Persistent pattern storage with PostgreSQL pub struct PatternStore { pool: PgPool, vector_index: VectorIndex, cache: LruCache, } impl PatternStore { pub async fn new(database_url: &str) -> Result { let pool = PgPool::connect(database_url).await?; // Initialize schema sqlx::query(r#" CREATE TABLE IF NOT EXISTS patterns ( id BIGSERIAL PRIMARY KEY, embedding vector(256), centroid vector(256), cluster_size INTEGER, total_weight FLOAT, avg_quality FLOAT, created_at TIMESTAMP DEFAULT NOW(), last_accessed TIMESTAMP DEFAULT NOW(), access_count INTEGER DEFAULT 0, pattern_type VARCHAR(50), metadata JSONB ); CREATE INDEX IF NOT EXISTS patterns_embedding_idx ON patterns USING ivfflat (embedding vector_cosine_ops); CREATE INDEX IF NOT EXISTS patterns_centroid_idx ON patterns USING hnsw (centroid vector_cosine_ops); "#).execute(&pool).await?; Ok(Self { pool, vector_index: VectorIndex::new(256), cache: LruCache::new(NonZeroUsize::new(10000).unwrap()), }) } /// Store pattern with vector embedding pub async fn store_pattern(&mut self, pattern: &LearnedPattern) -> Result { let embedding_vec: Vec = pattern.centroid.clone(); let row = sqlx::query_scalar::<_, i64>(r#" INSERT INTO patterns (embedding, centroid, cluster_size, total_weight, avg_quality, pattern_type, metadata) VALUES ($1, $2, $3, $4, $5, $6, $7) RETURNING id "#) .bind(&embedding_vec) .bind(&embedding_vec) .bind(pattern.cluster_size as i32) .bind(pattern.total_weight) .bind(pattern.avg_quality) .bind("learned") .bind(serde_json::to_value(&pattern.metadata).unwrap()) .fetch_one(&self.pool) .await?; // Update cache self.cache.put(row, pattern.clone()); Ok(row) } /// Find similar patterns using vector similarity pub async fn find_similar(&self, embedding: &[f32], k: usize) -> Result, PgError> { let rows = sqlx::query_as::<_, PatternRow>(r#" SELECT id, embedding, centroid, cluster_size, total_weight, avg_quality, metadata FROM patterns ORDER BY embedding <=> $1 LIMIT $2 "#) .bind(embedding) .bind(k as i64) .fetch_all(&self.pool) .await?; Ok(rows.into_iter().map(|r| r.into()).collect()) } /// Consolidate patterns (merge similar, prune weak) pub async fn consolidate(&mut self) -> Result { // Find patterns to merge (similarity > 0.95) let merge_candidates = sqlx::query(r#" SELECT p1.id as id1, p2.id as id2, 1 - (p1.centroid <=> p2.centroid) as similarity FROM patterns p1 JOIN patterns p2 ON p1.id < p2.id WHERE 1 - (p1.centroid <=> p2.centroid) > 0.95 LIMIT 100 "#).fetch_all(&self.pool).await?; // Prune weak patterns (low quality, low access) let pruned = sqlx::query(r#" DELETE FROM patterns WHERE avg_quality < 0.3 AND access_count < 5 AND created_at < NOW() - INTERVAL '7 days' RETURNING id "#).fetch_all(&self.pool).await?; Ok(ConsolidationResult { merged: merge_candidates.len(), pruned: pruned.len(), }) } } ``` **Integration Points**: - Long-term pattern persistence - Dream memory storage - Knowledge graph persistence ### 5. ruvector-sparse (Sparse Vectors) **Purpose**: Efficient sparse vector operations for pattern matching. ```rust use ruvector_sparse::{SparseVector, SparseDot, SparseIndex}; /// Sparse pattern representation for efficient storage pub struct SparsePatternStore { index: SparseIndex, patterns: Vec, } #[derive(Clone)] pub struct SparsePattern { pub id: u64, pub indices: Vec, pub values: Vec, pub nnz: usize, // Non-zero count pub metadata: PatternMetadata, } impl SparsePatternStore { pub fn new(dim: usize) -> Self { Self { index: SparseIndex::new(dim), patterns: Vec::new(), } } /// Convert dense pattern to sparse representation pub fn add_pattern(&mut self, dense: &[f32], threshold: f32) -> u64 { let (indices, values): (Vec, Vec) = dense.iter() .enumerate() .filter(|(_, &v)| v.abs() > threshold) .map(|(i, &v)| (i as u32, v)) .unzip(); let id = self.patterns.len() as u64; let pattern = SparsePattern { id, nnz: indices.len(), indices, values, metadata: PatternMetadata::default(), }; self.index.insert(id, &pattern.indices, &pattern.values); self.patterns.push(pattern); id } /// Fast sparse dot product search pub fn search(&self, query_indices: &[u32], query_values: &[f32], k: usize) -> Vec<(u64, f32)> { self.index.search_sparse(query_indices, query_values, k) } /// Batch sparse search with SIMD acceleration #[cfg(target_arch = "x86_64")] pub fn batch_search_simd(&self, queries: &[SparseVector], k: usize) -> Vec> { use std::arch::x86_64::*; queries.par_iter() .map(|q| { // SIMD-accelerated sparse dot products let mut scores = Vec::with_capacity(self.patterns.len()); for pattern in &self.patterns { let score = unsafe { sparse_dot_simd(&q.indices, &q.values, &pattern.indices, &pattern.values) }; scores.push((pattern.id, score)); } // Top-k selection scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); scores.truncate(k); scores }) .collect() } } /// SIMD-accelerated sparse dot product #[cfg(target_arch = "x86_64")] #[target_feature(enable = "avx2")] unsafe fn sparse_dot_simd( idx1: &[u32], val1: &[f32], idx2: &[u32], val2: &[f32] ) -> f32 { let mut i = 0; let mut j = 0; let mut sum = _mm256_setzero_ps(); // Merge-join with SIMD accumulation while i + 8 <= idx1.len() && j + 8 <= idx2.len() { let idx1_vec = _mm256_loadu_si256(idx1[i..].as_ptr() as *const __m256i); let idx2_vec = _mm256_loadu_si256(idx2[j..].as_ptr() as *const __m256i); // Compare and accumulate matching indices // ... SIMD comparison logic ... i += 8; j += 8; } // Reduce SIMD accumulator let mut result = [0.0f32; 8]; _mm256_storeu_ps(result.as_mut_ptr(), sum); result.iter().sum() } ``` **Integration Points**: - Pattern compression - Fast similarity search - Memory-efficient storage --- ## exo-ai Crate Integration ### 1. exo-core (IIT/Φ Measurement) **Purpose**: Integrated Information Theory for consciousness metrics. ```rust use exo_core::{PhiComputer, IntegratedInformation, Constellation}; /// Φ-based quality measurement for reasoning traces pub struct PhiEvaluator { phi_computer: PhiComputer, history: Vec, threshold: f64, } #[derive(Clone)] pub struct PhiMeasurement { pub phi_value: f64, pub main_complex: Constellation, pub timestamp: Instant, pub context: String, } impl PhiEvaluator { pub fn new(threshold: f64) -> Self { Self { phi_computer: PhiComputer::new(), history: Vec::new(), threshold, } } /// Measure integrated information of reasoning trace pub fn measure_phi(&mut self, trace: &ReasoningTrace) -> PhiMeasurement { // Build state transition matrix from trace let tpm = self.build_tpm(trace); // Compute Φ using IIT 3.0 let result = self.phi_computer.compute_phi(&tpm); let measurement = PhiMeasurement { phi_value: result.phi, main_complex: result.main_complex, timestamp: Instant::now(), context: trace.query.clone(), }; self.history.push(measurement.clone()); measurement } /// Check if reasoning meets integration threshold pub fn is_integrated(&self, measurement: &PhiMeasurement) -> bool { measurement.phi_value >= self.threshold } fn build_tpm(&self, trace: &ReasoningTrace) -> TransitionMatrix { let n = trace.steps.len(); let mut tpm = TransitionMatrix::zeros(n, n); for i in 0..n-1 { let from_state = &trace.steps[i]; let to_state = &trace.steps[i+1]; // Compute transition probability based on embedding similarity let similarity = cosine_similarity(&from_state.embedding, &to_state.embedding); tpm[(i, i+1)] = similarity; } tpm } /// Evaluate dream quality using Φ pub fn evaluate_dream(&mut self, dream: &Dream) -> f64 { let trace = ReasoningTrace { query: "dream".to_string(), steps: dream.path.iter() .map(|node| ReasoningStep { embedding: node.embedding.clone(), ..Default::default() }) .collect(), ..Default::default() }; self.measure_phi(&trace).phi_value } } /// Reasoning trace for Φ analysis pub struct ReasoningTrace { pub query: String, pub steps: Vec, pub final_answer: Option, pub quality_score: f32, } pub struct ReasoningStep { pub embedding: Vec, pub attention_pattern: Vec, pub activated_nodes: Vec, } ``` **Integration Points**: - Dream quality evaluation - Reasoning coherence measurement - Learning signal generation ### 2. exo-temporal (Temporal Cycles) **Purpose**: Temporal pattern recognition and prediction. ```rust use exo_temporal::{TemporalEncoder, CycleDetector, Predictor}; /// Temporal pattern learning for usage prediction pub struct TemporalLearner { encoder: TemporalEncoder, cycle_detector: CycleDetector, predictor: Predictor, patterns: Vec, } #[derive(Clone)] pub struct TemporalPattern { pub id: u64, pub period: Duration, pub phase: f32, pub amplitude: f32, pub pattern_type: TemporalPatternType, } #[derive(Clone, Copy)] pub enum TemporalPatternType { Daily, Weekly, Bursty, Seasonal, Custom, } impl TemporalLearner { pub fn new(encoding_dim: usize) -> Self { Self { encoder: TemporalEncoder::new(encoding_dim), cycle_detector: CycleDetector::new(), predictor: Predictor::new(encoding_dim, 64), patterns: Vec::new(), } } /// Record event with timestamp pub fn record_event(&mut self, event: &Event, timestamp: Instant) { let encoding = self.encoder.encode(timestamp, event); self.cycle_detector.add_observation(encoding, timestamp); } /// Detect temporal patterns pub fn detect_patterns(&mut self) -> Vec { let cycles = self.cycle_detector.find_cycles(); self.patterns = cycles.into_iter() .enumerate() .map(|(i, cycle)| TemporalPattern { id: i as u64, period: cycle.period, phase: cycle.phase, amplitude: cycle.amplitude, pattern_type: self.classify_cycle(&cycle), }) .collect(); self.patterns.clone() } fn classify_cycle(&self, cycle: &Cycle) -> TemporalPatternType { let hours = cycle.period.as_secs_f64() / 3600.0; if (23.0..25.0).contains(&hours) { TemporalPatternType::Daily } else if (166.0..170.0).contains(&hours) { TemporalPatternType::Weekly } else if hours < 1.0 { TemporalPatternType::Bursty } else { TemporalPatternType::Custom } } /// Predict optimal times for background learning pub fn predict_learning_windows(&self) -> Vec { let mut windows = Vec::new(); for pattern in &self.patterns { if matches!(pattern.pattern_type, TemporalPatternType::Daily | TemporalPatternType::Weekly) { // Find low-activity periods let low_activity_phase = pattern.phase + std::f32::consts::PI; // Opposite phase windows.push(TimeWindow { start: self.phase_to_time(low_activity_phase, pattern.period), duration: Duration::from_secs(3600), // 1 hour window priority: pattern.amplitude, }); } } windows.sort_by(|a, b| b.priority.partial_cmp(&a.priority).unwrap()); windows } fn phase_to_time(&self, phase: f32, period: Duration) -> Instant { let period_secs = period.as_secs_f32(); let offset = (phase / (2.0 * std::f32::consts::PI)) * period_secs; Instant::now() + Duration::from_secs_f32(offset) } } pub struct TimeWindow { pub start: Instant, pub duration: Duration, pub priority: f32, } ``` **Integration Points**: - Learning schedule optimization - Usage pattern prediction - Adaptive resource allocation ### 3. exo-exotic (Quantum-Inspired) **Purpose**: Quantum-inspired optimization for creative exploration. ```rust use exo_exotic::{QuantumState, SuperpositionSampler, EntanglementGraph}; /// Quantum-inspired creative exploration pub struct QuantumExplorer { state: QuantumState, sampler: SuperpositionSampler, entanglement: EntanglementGraph, } impl QuantumExplorer { pub fn new(dim: usize) -> Self { Self { state: QuantumState::new(dim), sampler: SuperpositionSampler::new(), entanglement: EntanglementGraph::new(), } } /// Create superposition of pattern states pub fn create_superposition(&mut self, patterns: &[LearnedPattern]) -> Superposition { let amplitudes: Vec = patterns.iter() .map(|p| { let magnitude = (p.avg_quality as f64).sqrt(); let phase = p.total_weight as f64 * 0.1; Complex64::from_polar(magnitude, phase) }) .collect(); self.state.set_amplitudes(&litudes); Superposition { patterns: patterns.to_vec(), amplitudes: amplitudes.clone(), entanglement_strength: self.measure_entanglement(&litudes), } } /// Sample from superposition for creative exploration pub fn sample_creative(&self, superposition: &Superposition, n_samples: usize) -> Vec { self.sampler.sample(&superposition.amplitudes, n_samples) .into_iter() .enumerate() .map(|(i, prob)| { let pattern_idx = self.probability_to_index(prob, superposition.patterns.len()); CreativeSample { base_pattern: superposition.patterns[pattern_idx].clone(), perturbation: self.quantum_perturbation(prob), novelty_score: 1.0 - prob, // Lower probability = more novel } }) .collect() } fn measure_entanglement(&self, amplitudes: &[Complex64]) -> f64 { // Compute von Neumann entropy as entanglement measure let probs: Vec = amplitudes.iter() .map(|a| a.norm_sqr()) .collect(); -probs.iter() .filter(|&&p| p > 1e-10) .map(|&p| p * p.ln()) .sum::() } fn quantum_perturbation(&self, prob: f64) -> Vec { // Generate quantum-inspired perturbation let dim = self.state.dimension(); let mut rng = rand::thread_rng(); (0..dim) .map(|_| { let phase = rng.gen::() * 2.0 * std::f64::consts::PI; let amplitude = (1.0 - prob).sqrt(); (amplitude * phase.cos()) as f32 * 0.1 }) .collect() } fn probability_to_index(&self, prob: f64, n: usize) -> usize { ((prob * n as f64) as usize).min(n - 1) } } pub struct Superposition { pub patterns: Vec, pub amplitudes: Vec, pub entanglement_strength: f64, } pub struct CreativeSample { pub base_pattern: LearnedPattern, pub perturbation: Vec, pub novelty_score: f64, } ``` **Integration Points**: - Dream creative jumps - Novel pattern generation - Exploration-exploitation balance --- ## Unified Integration Layer ### SONA Integration Manager ```rust /// Central integration manager for all SONA components pub struct SonaIntegration { // ruvector components pub pattern_index: PatternIndex, pub attention: AdaptiveAttention, pub knowledge_graph: KnowledgeGraph, pub pattern_store: PatternStore, pub sparse_store: SparsePatternStore, // exo-ai components pub phi_evaluator: PhiEvaluator, pub temporal_learner: TemporalLearner, pub quantum_explorer: QuantumExplorer, // Core SONA components pub lora_engine: LoraEngine, pub reasoning_bank: ReasoningBank, pub dream_engine: DreamEngine, pub ewc: EwcPlusPlus, // Coordination pub loop_coordinator: LoopCoordinator, pub metrics: IntegrationMetrics, } impl SonaIntegration { pub async fn new(config: SonaConfig) -> Result { Ok(Self { pattern_index: PatternIndex::new(config.embedding_dim, config.max_patterns), attention: AdaptiveAttention::new(config.hidden_dim, config.num_heads), knowledge_graph: KnowledgeGraph::new(config.embedding_dim, config.hidden_dim), pattern_store: PatternStore::new(&config.database_url).await?, sparse_store: SparsePatternStore::new(config.embedding_dim), phi_evaluator: PhiEvaluator::new(config.phi_threshold), temporal_learner: TemporalLearner::new(config.temporal_dim), quantum_explorer: QuantumExplorer::new(config.embedding_dim), lora_engine: LoraEngine::new(config.lora_config), reasoning_bank: ReasoningBank::new(config.pattern_config), dream_engine: DreamEngine::new(config.dream_config), ewc: EwcPlusPlus::new(config.ewc_config), loop_coordinator: LoopCoordinator::new(), metrics: IntegrationMetrics::default(), }) } /// Process query through unified pipeline pub async fn process(&mut self, query: &str, context: &Context) -> Result { let start = Instant::now(); // 1. Record temporal event self.temporal_learner.record_event(&Event::Query(query.to_string()), Instant::now()); // 2. Embed query let query_embedding = self.embed_query(query); // 3. Find similar patterns (parallel) let (similar_patterns, graph_context, sparse_matches) = tokio::join!( self.pattern_index.find_similar(&query_embedding, 10), self.knowledge_graph.propagate(&query_embedding, 3), async { self.sparse_store.search(&[], &[], 5) } // Sparse backup ); // 4. Apply adaptive attention let attended = self.attention.forward(&query_embedding, &context, &similar_patterns); // 5. Generate response with LoRA let response = self.lora_engine.forward(&attended); // 6. Record trajectory let trajectory = QueryTrajectory { query: query.to_string(), steps: vec![/* reasoning steps */], response: response.clone(), quality: self.evaluate_quality(&response), }; // 7. Signal learning (async) let signal = LearningSignal::from_trajectory(&trajectory); self.loop_coordinator.signal_learning(signal); self.metrics.queries_processed += 1; self.metrics.avg_latency_ms = (self.metrics.avg_latency_ms * 0.99) + (start.elapsed().as_millis() as f64 * 0.01); Ok(Response { text: response.text, confidence: response.confidence, patterns_used: similar_patterns.len(), }) } /// Run background learning cycle pub async fn background_learn(&mut self) -> Result { // Check if good time for learning let windows = self.temporal_learner.predict_learning_windows(); // Extract patterns from reasoning bank let patterns = self.reasoning_bank.extract_patterns(); // Evaluate patterns with Φ for pattern in &patterns { let trace = pattern.to_reasoning_trace(); let phi = self.phi_evaluator.measure_phi(&trace); if self.phi_evaluator.is_integrated(&phi) { // High-quality pattern - persist self.pattern_store.store_pattern(pattern).await?; self.knowledge_graph.learn_edge(pattern); } } // Update LoRA with EWC++ let gradients = self.lora_engine.compute_gradients(&patterns); let safe_gradients = self.ewc.apply_constraints(&gradients); self.lora_engine.apply_update(&safe_gradients); // Consolidate storage self.pattern_store.consolidate().await?; Ok(LearningResult { patterns_learned: patterns.len(), patterns_persisted: patterns.iter().filter(|p| p.avg_quality > 0.7).count(), }) } /// Run deep learning cycle (weekly) pub async fn deep_learn(&mut self) -> Result { // Generate dreams let dreams = self.dream_engine.generate_dreams(50); // Evaluate with quantum exploration let quantum_samples: Vec<_> = dreams.iter() .filter_map(|dream| { let patterns = dream.to_patterns(); if patterns.len() >= 2 { let superposition = self.quantum_explorer.create_superposition(&patterns); Some(self.quantum_explorer.sample_creative(&superposition, 3)) } else { None } }) .flatten() .collect(); // Evaluate dreams with Φ let mut integrated_dreams = Vec::new(); for dream in &dreams { let phi = self.phi_evaluator.evaluate_dream(dream); if phi > self.phi_evaluator.threshold { integrated_dreams.push((dream.clone(), phi)); } } // Integrate high-quality dreams for (dream, _phi) in &integrated_dreams { self.dream_engine.integrate_dream(dream); } // Update temporal patterns self.temporal_learner.detect_patterns(); // Full EWC++ consolidation self.ewc.consolidate_all_tasks(); Ok(DeepLearningResult { dreams_generated: dreams.len(), dreams_integrated: integrated_dreams.len(), quantum_samples: quantum_samples.len(), }) } fn embed_query(&self, query: &str) -> Vec { // Query embedding implementation vec![0.0; 256] // Placeholder } fn evaluate_quality(&self, response: &ResponseData) -> f32 { response.confidence } } #[derive(Default)] pub struct IntegrationMetrics { pub queries_processed: u64, pub patterns_learned: u64, pub dreams_integrated: u64, pub avg_latency_ms: f64, pub avg_phi: f64, } ``` --- ## Component Communication Protocol ```rust /// Inter-component message types pub enum SonaMessage { // Learning signals LearningSignal(LearningSignal), PatternDiscovered(LearnedPattern), DreamGenerated(Dream), // Coordination StartBackgroundLearning, StartDeepLearning, ConsolidateMemory, // Queries QueryPattern(Vec), QueryGraph(NodeId, usize), // Results PatternResult(Vec), GraphResult(Vec<(NodeId, f32)>), } /// Message bus for component communication pub struct SonaMessageBus { sender: broadcast::Sender, subscribers: HashMap>, } impl SonaMessageBus { pub fn subscribe(&mut self, component_id: ComponentId) -> broadcast::Receiver { self.sender.subscribe() } pub fn publish(&self, message: SonaMessage) { let _ = self.sender.send(message); } } ``` --- ## Next Steps 1. **06-COMPONENTS.md** - This document (Complete) 2. **07-IMPLEMENTATION.md** - Implementation roadmap 3. **08-BENCHMARKS.md** - Performance targets 4. **09-API-REFERENCE.md** - Complete API documentation