37 KiB
37 KiB
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.
use ruvector_core::{HnswIndex, Distance, SearchParams};
/// Pattern index using HNSW for sub-millisecond retrieval
pub struct PatternIndex {
index: HnswIndex<f32>,
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<f32>], k: usize) -> Vec<Vec<(u64, f32)>> {
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.
use ruvector_attention::{
AttentionMechanism, MultiHeadAttention, LinearAttention,
SparseAttention, FlashAttention, KernelizedAttention
};
/// Adaptive attention selector based on query characteristics
pub struct AdaptiveAttention {
mechanisms: Vec<Box<dyn AttentionMechanism>>,
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<f32>,
history: CircularBuffer<RoutingDecision>,
}
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.
use ruvector_gnn::{GraphConv, GraphAttention, MessagePassing};
/// Knowledge graph reasoning with GNN
pub struct KnowledgeGraph {
nodes: HashMap<NodeId, NodeEmbedding>,
edges: Vec<Edge>,
gnn: GraphNeuralNetwork,
graph_index: GraphIndex,
}
#[derive(Clone)]
pub struct NodeEmbedding {
pub id: NodeId,
pub embedding: Vec<f32>,
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<f32>, 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<GnnLayer>,
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.
use ruvector_postgres::{PgVector, PgStore, VectorIndex};
/// Persistent pattern storage with PostgreSQL
pub struct PatternStore {
pool: PgPool,
vector_index: VectorIndex,
cache: LruCache<PatternId, LearnedPattern>,
}
impl PatternStore {
pub async fn new(database_url: &str) -> Result<Self, PgError> {
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<i64, PgError> {
let embedding_vec: Vec<f32> = 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<Vec<LearnedPattern>, 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<ConsolidationResult, PgError> {
// 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.
use ruvector_sparse::{SparseVector, SparseDot, SparseIndex};
/// Sparse pattern representation for efficient storage
pub struct SparsePatternStore {
index: SparseIndex,
patterns: Vec<SparsePattern>,
}
#[derive(Clone)]
pub struct SparsePattern {
pub id: u64,
pub indices: Vec<u32>,
pub values: Vec<f32>,
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<u32>, Vec<f32>) = 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<Vec<(u64, f32)>> {
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.
use exo_core::{PhiComputer, IntegratedInformation, Constellation};
/// Φ-based quality measurement for reasoning traces
pub struct PhiEvaluator {
phi_computer: PhiComputer,
history: Vec<PhiMeasurement>,
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<ReasoningStep>,
pub final_answer: Option<String>,
pub quality_score: f32,
}
pub struct ReasoningStep {
pub embedding: Vec<f32>,
pub attention_pattern: Vec<f32>,
pub activated_nodes: Vec<NodeId>,
}
Integration Points:
- Dream quality evaluation
- Reasoning coherence measurement
- Learning signal generation
2. exo-temporal (Temporal Cycles)
Purpose: Temporal pattern recognition and prediction.
use exo_temporal::{TemporalEncoder, CycleDetector, Predictor};
/// Temporal pattern learning for usage prediction
pub struct TemporalLearner {
encoder: TemporalEncoder,
cycle_detector: CycleDetector,
predictor: Predictor,
patterns: Vec<TemporalPattern>,
}
#[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<TemporalPattern> {
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<TimeWindow> {
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.
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<Complex64> = 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<CreativeSample> {
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<f64> = amplitudes.iter()
.map(|a| a.norm_sqr())
.collect();
-probs.iter()
.filter(|&&p| p > 1e-10)
.map(|&p| p * p.ln())
.sum::<f64>()
}
fn quantum_perturbation(&self, prob: f64) -> Vec<f32> {
// Generate quantum-inspired perturbation
let dim = self.state.dimension();
let mut rng = rand::thread_rng();
(0..dim)
.map(|_| {
let phase = rng.gen::<f64>() * 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<LearnedPattern>,
pub amplitudes: Vec<Complex64>,
pub entanglement_strength: f64,
}
pub struct CreativeSample {
pub base_pattern: LearnedPattern,
pub perturbation: Vec<f32>,
pub novelty_score: f64,
}
Integration Points:
- Dream creative jumps
- Novel pattern generation
- Exploration-exploitation balance
Unified Integration Layer
SONA Integration Manager
/// 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<Self, SonaError> {
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<Response, SonaError> {
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<LearningResult, SonaError> {
// 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<DeepLearningResult, SonaError> {
// 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<f32> {
// 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
/// Inter-component message types
pub enum SonaMessage {
// Learning signals
LearningSignal(LearningSignal),
PatternDiscovered(LearnedPattern),
DreamGenerated(Dream),
// Coordination
StartBackgroundLearning,
StartDeepLearning,
ConsolidateMemory,
// Queries
QueryPattern(Vec<f32>),
QueryGraph(NodeId, usize),
// Results
PatternResult(Vec<LearnedPattern>),
GraphResult(Vec<(NodeId, f32)>),
}
/// Message bus for component communication
pub struct SonaMessageBus {
sender: broadcast::Sender<SonaMessage>,
subscribers: HashMap<ComponentId, broadcast::Receiver<SonaMessage>>,
}
impl SonaMessageBus {
pub fn subscribe(&mut self, component_id: ComponentId) -> broadcast::Receiver<SonaMessage> {
self.sender.subscribe()
}
pub fn publish(&self, message: SonaMessage) {
let _ = self.sender.send(message);
}
}
Next Steps
- 06-COMPONENTS.md - This document (Complete)
- 07-IMPLEMENTATION.md - Implementation roadmap
- 08-BENCHMARKS.md - Performance targets
- 09-API-REFERENCE.md - Complete API documentation