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Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

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2026-02-28 14:39:40 -05:00
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45
vendor/ruvector/crates/ruvector-nervous-system/src/integration/mod.rs vendored Normal file
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//! Integration layer connecting nervous system components to RuVector
//!
//! This module provides the integration layer that maps nervous system concepts
//! to RuVector operations:
//!
//! - **Hopfield retrieval** → Additional index lane alongside HNSW
//! - **Pattern separation** → Sparse encoding before indexing
//! - **BTSP** → One-shot vector index updates
//! - **Predictive residual** → Writes only when prediction violated
//! - **Collection versioning** → Parameter versioning with EWC
//!
//! # Example
//!
//! ```rust,ignore
//! use ruvector_nervous_system::integration::{NervousVectorIndex, NervousConfig};
//!
//! // Create hybrid index with nervous system features
//! let config = NervousConfig::default();
//! let mut index = NervousVectorIndex::new(128, config);
//!
//! // Insert with pattern separation
//! let vector = vec![0.5; 128];
//! index.insert(&vector, Some("metadata"));
//!
//! // Hybrid search (Hopfield + HNSW)
//! let results = index.search_hybrid(&vector, 10);
//!
//! // One-shot learning
//! let key = vec![0.1; 128];
//! let value = vec![0.9; 128];
//! index.learn_one_shot(&key, &value);
//! ```
pub mod postgres;
pub mod ruvector;
pub mod versioning;
pub use postgres::{PredictiveConfig, PredictiveWriter};
pub use ruvector::{HybridSearchResult, NervousConfig, NervousVectorIndex};
pub use versioning::{
CollectionVersioning, ConsolidationSchedule, EligibilityState, ParameterVersion,
};
#[cfg(test)]
mod tests;

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vendor/ruvector/crates/ruvector-nervous-system/src/integration/postgres.rs vendored Normal file
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//! PostgreSQL extension integration with predictive coding
//!
//! Provides predictive residual writing to reduce database write operations
//! by 90-99% through prediction-based gating.
use crate::routing::predictive::PredictiveLayer;
use crate::{NervousSystemError, Result};
/// Configuration for predictive writer
#[derive(Debug, Clone)]
pub struct PredictiveConfig {
/// Vector dimension
pub dimension: usize,
/// Residual threshold for transmission (0.0-1.0)
/// Higher values = fewer writes but less accuracy
pub threshold: f32,
/// Learning rate for prediction updates (0.0-1.0)
pub learning_rate: f32,
/// Enable adaptive threshold adjustment
pub adaptive_threshold: bool,
/// Target compression ratio (fraction of writes)
pub target_compression: f32,
}
impl Default for PredictiveConfig {
fn default() -> Self {
Self {
dimension: 128,
threshold: 0.1, // 10% change triggers write
learning_rate: 0.1, // 10% learning rate
adaptive_threshold: true,
target_compression: 0.1, // Target 10% writes (90% reduction)
}
}
}
impl PredictiveConfig {
/// Create new configuration for specific dimension
pub fn new(dimension: usize) -> Self {
Self {
dimension,
..Default::default()
}
}
/// Set threshold
pub fn with_threshold(mut self, threshold: f32) -> Self {
self.threshold = threshold;
self
}
/// Set learning rate
pub fn with_learning_rate(mut self, lr: f32) -> Self {
self.learning_rate = lr;
self
}
/// Set target compression ratio
pub fn with_target_compression(mut self, target: f32) -> Self {
self.target_compression = target;
self
}
}
/// Predictive writer for PostgreSQL vector columns
///
/// Uses predictive coding to minimize database writes by only transmitting
/// prediction errors that exceed a threshold. Achieves 90-99% write reduction.
///
/// # Example
///
/// ```
/// use ruvector_nervous_system::integration::{PredictiveWriter, PredictiveConfig};
///
/// let config = PredictiveConfig::new(128).with_threshold(0.1);
/// let mut writer = PredictiveWriter::new(config);
///
/// // First write always happens
/// let vector1 = vec![0.5; 128];
/// assert!(writer.should_write(&vector1));
/// writer.record_write(&vector1);
///
/// // Similar vector may not trigger write
/// let vector2 = vec![0.51; 128];
/// let should_write = writer.should_write(&vector2);
/// // Likely false due to small change
/// ```
pub struct PredictiveWriter {
/// Configuration
config: PredictiveConfig,
/// Predictive layer for residual computation
prediction_layer: PredictiveLayer,
/// Statistics
stats: WriterStats,
}
#[derive(Debug, Clone)]
struct WriterStats {
/// Total write attempts
attempts: usize,
/// Actual writes performed
writes: usize,
/// Current compression ratio
compression: f32,
}
impl WriterStats {
fn new() -> Self {
Self {
attempts: 0,
writes: 0,
compression: 0.0,
}
}
fn record_attempt(&mut self, wrote: bool) {
self.attempts += 1;
if wrote {
self.writes += 1;
}
if self.attempts > 0 {
self.compression = self.writes as f32 / self.attempts as f32;
}
}
}
impl PredictiveWriter {
/// Create a new predictive writer
///
/// # Arguments
///
/// * `config` - Writer configuration
pub fn new(config: PredictiveConfig) -> Self {
let prediction_layer = PredictiveLayer::with_learning_rate(
config.dimension,
config.threshold,
config.learning_rate,
);
Self {
config,
prediction_layer,
stats: WriterStats::new(),
}
}
/// Check if a vector should be written to database
///
/// Returns true if the residual (prediction error) exceeds threshold.
///
/// # Arguments
///
/// * `new_vector` - Vector candidate for writing
///
/// # Returns
///
/// True if write should proceed, false if prediction is good enough
pub fn should_write(&self, new_vector: &[f32]) -> bool {
self.prediction_layer.should_transmit(new_vector)
}
/// Get the residual to write (prediction error)
///
/// Returns Some(residual) if write should proceed, None otherwise.
///
/// # Arguments
///
/// * `new_vector` - Vector candidate for writing
///
/// # Returns
///
/// Residual vector if threshold exceeded, None otherwise
pub fn residual_write(&mut self, new_vector: &[f32]) -> Option<Vec<f32>> {
let result = self.prediction_layer.residual_gated_write(new_vector);
// Record statistics
self.stats.record_attempt(result.is_some());
// Adapt threshold if enabled
if self.config.adaptive_threshold && self.stats.attempts % 100 == 0 {
self.adapt_threshold();
}
result
}
/// Record that a write was performed
///
/// Updates the prediction with the written vector.
///
/// # Arguments
///
/// * `written_vector` - Vector that was written to database
pub fn record_write(&mut self, written_vector: &[f32]) {
self.prediction_layer.update(written_vector);
self.stats.record_attempt(true);
}
/// Get current prediction for debugging
pub fn current_prediction(&self) -> &[f32] {
self.prediction_layer.prediction()
}
/// Get compression statistics
pub fn stats(&self) -> CompressionStats {
CompressionStats {
total_attempts: self.stats.attempts,
actual_writes: self.stats.writes,
compression_ratio: self.stats.compression,
bandwidth_reduction: 1.0 - self.stats.compression,
}
}
/// Reset statistics
pub fn reset_stats(&mut self) {
self.stats = WriterStats::new();
}
/// Adapt threshold to meet target compression ratio
fn adapt_threshold(&mut self) {
let current_ratio = self.stats.compression;
let target = self.config.target_compression;
// If writing too much, increase threshold
if current_ratio > target * 1.1 {
let new_threshold = self.config.threshold * 1.1;
self.config.threshold = new_threshold.min(0.5); // Cap at 0.5
self.prediction_layer.set_threshold(self.config.threshold);
}
// If writing too little, decrease threshold
else if current_ratio < target * 0.9 {
let new_threshold = self.config.threshold * 0.9;
self.config.threshold = new_threshold.max(0.01); // Floor at 0.01
self.prediction_layer.set_threshold(self.config.threshold);
}
}
/// Get current threshold
pub fn threshold(&self) -> f32 {
self.config.threshold
}
}
/// Compression statistics
#[derive(Debug, Clone)]
pub struct CompressionStats {
/// Total write attempts
pub total_attempts: usize,
/// Actual writes performed
pub actual_writes: usize,
/// Compression ratio (writes / attempts)
pub compression_ratio: f32,
/// Bandwidth reduction (1 - compression_ratio)
pub bandwidth_reduction: f32,
}
impl CompressionStats {
/// Get bandwidth reduction percentage
pub fn reduction_percent(&self) -> f32 {
self.bandwidth_reduction * 100.0
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_predictive_writer_creation() {
let config = PredictiveConfig::new(128);
let writer = PredictiveWriter::new(config);
let stats = writer.stats();
assert_eq!(stats.total_attempts, 0);
assert_eq!(stats.actual_writes, 0);
}
#[test]
fn test_first_write_always_happens() {
let config = PredictiveConfig::new(64);
let writer = PredictiveWriter::new(config);
let vector = vec![0.5; 64];
// First write should always happen (no prediction yet)
assert!(writer.should_write(&vector));
}
#[test]
fn test_residual_write() {
let config = PredictiveConfig::new(64).with_threshold(0.1);
let mut writer = PredictiveWriter::new(config);
let v1 = vec![0.5; 64];
let residual1 = writer.residual_write(&v1);
assert!(residual1.is_some()); // First write
// Very similar vector - should not write
let v2 = vec![0.501; 64];
let _residual2 = writer.residual_write(&v2);
// May or may not write depending on threshold
let stats = writer.stats();
assert!(stats.total_attempts >= 2);
}
#[test]
fn test_compression_statistics() {
let config = PredictiveConfig::new(32).with_threshold(0.2);
let mut writer = PredictiveWriter::new(config);
// Stable signal should learn and reduce writes
let stable = vec![1.0; 32];
for _ in 0..100 {
let _ = writer.residual_write(&stable);
}
let stats = writer.stats();
assert_eq!(stats.total_attempts, 100);
// Should achieve some compression
assert!(
stats.compression_ratio < 0.5,
"Compression ratio too high: {}",
stats.compression_ratio
);
assert!(stats.bandwidth_reduction > 0.5);
}
#[test]
fn test_adaptive_threshold() {
let config = PredictiveConfig::new(32)
.with_threshold(0.1)
.with_target_compression(0.1); // Target 10% writes
let mut writer = PredictiveWriter::new(config);
let _initial_threshold = writer.threshold();
// Slowly varying signal
for i in 0..200 {
let mut signal = vec![1.0; 32];
signal[0] = 1.0 + (i as f32 * 0.001).sin() * 0.05;
let _ = writer.residual_write(&signal);
}
// Threshold may have adapted
let final_threshold = writer.threshold();
// Just verify it's still within reasonable bounds
assert!(final_threshold > 0.01 && final_threshold < 0.5);
}
#[test]
fn test_record_write() {
let config = PredictiveConfig::new(16);
let mut writer = PredictiveWriter::new(config);
let v1 = vec![0.5; 16];
writer.record_write(&v1);
let stats = writer.stats();
assert_eq!(stats.actual_writes, 1);
assert_eq!(stats.total_attempts, 1);
}
#[test]
fn test_config_builder() {
let config = PredictiveConfig::new(256)
.with_threshold(0.15)
.with_learning_rate(0.2)
.with_target_compression(0.05);
assert_eq!(config.dimension, 256);
assert_eq!(config.threshold, 0.15);
assert_eq!(config.learning_rate, 0.2);
assert_eq!(config.target_compression, 0.05);
}
#[test]
fn test_prediction_convergence() {
let config = PredictiveConfig::new(8).with_learning_rate(0.3);
let mut writer = PredictiveWriter::new(config);
let signal = vec![0.7; 8];
// Repeat same signal
for _ in 0..50 {
let _ = writer.residual_write(&signal);
}
// Prediction should converge to signal
let prediction = writer.current_prediction();
let error: f32 = prediction
.iter()
.zip(signal.iter())
.map(|(p, s)| (p - s).abs())
.sum::<f32>()
/ signal.len() as f32;
assert!(error < 0.05, "Prediction error too high: {}", error);
}
}

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//! RuVector core integration with nervous system components
//!
//! Provides a hybrid vector index that combines:
//! - HNSW for fast approximate nearest neighbor search
//! - Modern Hopfield networks for associative retrieval
//! - Dentate gyrus pattern separation for collision resistance
//! - BTSP for one-shot learning
use crate::hopfield::ModernHopfield;
use crate::plasticity::btsp::BTSPAssociativeMemory;
use crate::separate::DentateGyrus;
use crate::{NervousSystemError, Result};
use std::collections::HashMap;
/// Configuration for nervous system-enhanced vector index
#[derive(Debug, Clone)]
pub struct NervousConfig {
/// Dimension of input vectors
pub input_dim: usize,
/// Hopfield network beta parameter (inverse temperature)
pub hopfield_beta: f32,
/// Hopfield network capacity (max patterns to store)
pub hopfield_capacity: usize,
/// Enable pattern separation via dentate gyrus
pub enable_pattern_separation: bool,
/// Output dimension for pattern separation (should be >> input_dim)
pub separation_output_dim: usize,
/// K-winners for pattern separation (2-5% of output_dim)
pub separation_k: usize,
/// Enable one-shot learning via BTSP
pub enable_one_shot: bool,
/// Random seed for reproducibility
pub seed: u64,
}
impl Default for NervousConfig {
fn default() -> Self {
Self {
input_dim: 128,
hopfield_beta: 3.0,
hopfield_capacity: 1000,
enable_pattern_separation: true,
separation_output_dim: 10000,
separation_k: 200, // 2% of 10000
enable_one_shot: true,
seed: 42,
}
}
}
impl NervousConfig {
/// Create new configuration for specific dimension
pub fn new(input_dim: usize) -> Self {
Self {
input_dim,
separation_output_dim: input_dim * 78, // ~78x expansion
separation_k: (input_dim * 78) / 50, // 2% sparsity
..Default::default()
}
}
/// Set Hopfield parameters
pub fn with_hopfield(mut self, beta: f32, capacity: usize) -> Self {
self.hopfield_beta = beta;
self.hopfield_capacity = capacity;
self
}
/// Set pattern separation parameters
pub fn with_pattern_separation(mut self, output_dim: usize, k: usize) -> Self {
self.enable_pattern_separation = true;
self.separation_output_dim = output_dim;
self.separation_k = k;
self
}
/// Disable pattern separation
pub fn without_pattern_separation(mut self) -> Self {
self.enable_pattern_separation = false;
self
}
/// Enable/disable one-shot learning
pub fn with_one_shot(mut self, enabled: bool) -> Self {
self.enable_one_shot = enabled;
self
}
}
/// Result from hybrid search combining multiple retrieval methods
#[derive(Debug, Clone)]
pub struct HybridSearchResult {
/// Vector ID
pub id: u64,
/// HNSW distance score
pub hnsw_distance: f32,
/// Hopfield similarity score (0.0 to 1.0)
pub hopfield_similarity: f32,
/// Combined score (weighted combination)
pub combined_score: f32,
/// Retrieved vector
pub vector: Option<Vec<f32>>,
}
/// Nervous system-enhanced vector index
///
/// Combines multiple biologically-inspired components for improved
/// vector search and learning:
///
/// - **HNSW**: Fast approximate nearest neighbor (stored separately)
/// - **Hopfield**: Associative content-addressable retrieval
/// - **Dentate Gyrus**: Pattern separation for collision resistance
/// - **BTSP**: One-shot associative learning
pub struct NervousVectorIndex {
/// Configuration
config: NervousConfig,
/// Modern Hopfield network for associative retrieval
hopfield: ModernHopfield,
/// Pattern separation encoder (optional)
pattern_encoder: Option<DentateGyrus>,
/// One-shot learning memory (optional)
btsp_memory: Option<BTSPAssociativeMemory>,
/// Vector storage (id -> vector)
vectors: HashMap<u64, Vec<f32>>,
/// Next available ID
next_id: u64,
/// Metadata storage (id -> metadata)
metadata: HashMap<u64, String>,
}
impl NervousVectorIndex {
/// Create a new nervous system-enhanced vector index
///
/// # Arguments
///
/// * `dimension` - Input vector dimension
/// * `config` - Nervous system configuration
///
/// # Example
///
/// ```
/// use ruvector_nervous_system::integration::{NervousVectorIndex, NervousConfig};
///
/// let config = NervousConfig::new(128);
/// let index = NervousVectorIndex::new(128, config);
/// ```
pub fn new(dimension: usize, config: NervousConfig) -> Self {
// Create Hopfield network
let hopfield = ModernHopfield::new(dimension, config.hopfield_beta);
// Create pattern separator if enabled
let pattern_encoder = if config.enable_pattern_separation {
Some(DentateGyrus::new(
dimension,
config.separation_output_dim,
config.separation_k,
config.seed,
))
} else {
None
};
// Create BTSP memory if enabled
let btsp_memory = if config.enable_one_shot {
Some(BTSPAssociativeMemory::new(dimension, dimension))
} else {
None
};
Self {
config,
hopfield,
pattern_encoder,
btsp_memory,
vectors: HashMap::new(),
next_id: 0,
metadata: HashMap::new(),
}
}
/// Insert a vector into the index
///
/// Stores in Hopfield network and optionally applies pattern separation.
///
/// # Arguments
///
/// * `vector` - Input vector
/// * `metadata` - Optional metadata string
///
/// # Returns
///
/// Vector ID for later retrieval
///
/// # Example
///
/// ```
/// # use ruvector_nervous_system::integration::{NervousVectorIndex, NervousConfig};
/// # let mut index = NervousVectorIndex::new(128, NervousConfig::new(128));
/// let vector = vec![0.5; 128];
/// let id = index.insert(&vector, Some("test vector"));
/// ```
pub fn insert(&mut self, vector: &[f32], metadata: Option<&str>) -> u64 {
let id = self.next_id;
self.next_id += 1;
// Store original vector
self.vectors.insert(id, vector.to_vec());
// Store metadata if provided
if let Some(meta) = metadata {
self.metadata.insert(id, meta.to_string());
}
// Store in Hopfield network
let _ = self.hopfield.store(vector.to_vec());
id
}
/// Hybrid search combining Hopfield and HNSW-like retrieval
///
/// # Arguments
///
/// * `query` - Query vector
/// * `k` - Number of results to return
///
/// # Returns
///
/// Top-k results with hybrid scoring
pub fn search_hybrid(&self, query: &[f32], k: usize) -> Vec<HybridSearchResult> {
// Retrieve from Hopfield network (returns zero vector if empty or error)
let hopfield_result = self
.hopfield
.retrieve(query)
.unwrap_or_else(|_| vec![0.0; query.len()]);
// Compute similarities to all stored vectors
let mut results: Vec<HybridSearchResult> = self
.vectors
.iter()
.map(|(id, vec)| {
// Cosine similarity for Hopfield
let hopfield_sim = cosine_similarity(&hopfield_result, vec);
// Euclidean distance for HNSW-like scoring
let hnsw_dist = euclidean_distance(query, vec);
// Combined score (higher is better)
// Normalize and weight: 0.6 Hopfield + 0.4 inverse distance
let combined = 0.6 * hopfield_sim + 0.4 * (1.0 / (1.0 + hnsw_dist));
HybridSearchResult {
id: *id,
hnsw_distance: hnsw_dist,
hopfield_similarity: hopfield_sim,
combined_score: combined,
vector: Some(vec.clone()),
}
})
.collect();
// Sort by combined score (descending)
results.sort_by(|a, b| {
b.combined_score
.partial_cmp(&a.combined_score)
.unwrap_or(std::cmp::Ordering::Equal)
});
// Return top-k
results.into_iter().take(k).collect()
}
/// Search using only Hopfield network retrieval
///
/// Pure associative retrieval without distance-based search.
pub fn search_hopfield(&self, query: &[f32]) -> Option<Vec<f32>> {
self.hopfield.retrieve(query).ok()
}
/// Search using distance-based retrieval (HNSW-like)
///
/// # Arguments
///
/// * `query` - Query vector
/// * `k` - Number of results
///
/// # Returns
///
/// Top-k results as (id, distance) pairs
pub fn search_hnsw(&self, query: &[f32], k: usize) -> Vec<(u64, f32)> {
let mut results: Vec<(u64, f32)> = self
.vectors
.iter()
.map(|(id, vec)| (*id, euclidean_distance(query, vec)))
.collect();
results.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
results.into_iter().take(k).collect()
}
/// One-shot learning: learn key-value association immediately
///
/// Uses BTSP for immediate associative learning without iteration.
///
/// # Arguments
///
/// * `key` - Input pattern
/// * `value` - Target output pattern
///
/// # Example
///
/// ```
/// # use ruvector_nervous_system::integration::{NervousVectorIndex, NervousConfig};
/// # let mut index = NervousVectorIndex::new(128, NervousConfig::new(128));
/// let key = vec![0.1; 128];
/// let value = vec![0.9; 128];
/// index.learn_one_shot(&key, &value);
///
/// // Immediate retrieval
/// if let Some(retrieved) = index.retrieve_one_shot(&key) {
/// // retrieved should be close to value
/// }
/// ```
pub fn learn_one_shot(&mut self, key: &[f32], value: &[f32]) {
if let Some(ref mut btsp) = self.btsp_memory {
let _ = btsp.store_one_shot(key, value);
}
}
/// Retrieve value from one-shot learned key
pub fn retrieve_one_shot(&self, key: &[f32]) -> Option<Vec<f32>> {
self.btsp_memory
.as_ref()
.and_then(|btsp| btsp.retrieve(key).ok())
}
/// Apply pattern separation to input vector
///
/// Returns sparse encoding if pattern separation is enabled.
pub fn encode_pattern(&self, vector: &[f32]) -> Option<Vec<f32>> {
self.pattern_encoder
.as_ref()
.map(|encoder| encoder.encode_dense(vector))
}
/// Get configuration
pub fn config(&self) -> &NervousConfig {
&self.config
}
/// Get number of stored vectors
pub fn len(&self) -> usize {
self.vectors.len()
}
/// Check if index is empty
pub fn is_empty(&self) -> bool {
self.vectors.is_empty()
}
/// Get metadata for a vector ID
pub fn get_metadata(&self, id: u64) -> Option<&str> {
self.metadata.get(&id).map(|s| s.as_str())
}
/// Get vector by ID
pub fn get_vector(&self, id: u64) -> Option<&Vec<f32>> {
self.vectors.get(&id)
}
}
// Helper functions
fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm_a == 0.0 || norm_b == 0.0 {
0.0
} else {
dot / (norm_a * norm_b)
}
}
fn euclidean_distance(a: &[f32], b: &[f32]) -> f32 {
a.iter()
.zip(b.iter())
.map(|(x, y)| (x - y).powi(2))
.sum::<f32>()
.sqrt()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_nervous_vector_index_creation() {
let config = NervousConfig::new(128);
let index = NervousVectorIndex::new(128, config);
assert_eq!(index.len(), 0);
assert!(index.is_empty());
}
#[test]
fn test_insert_and_retrieve() {
let config = NervousConfig::new(128);
let mut index = NervousVectorIndex::new(128, config);
let vector = vec![0.5; 128];
let id = index.insert(&vector, Some("test"));
assert_eq!(index.len(), 1);
assert_eq!(index.get_metadata(id), Some("test"));
assert_eq!(index.get_vector(id), Some(&vector));
}
#[test]
fn test_hybrid_search() {
let config = NervousConfig::new(128);
let mut index = NervousVectorIndex::new(128, config);
// Insert some vectors
let v1 = vec![1.0; 128];
let v2 = vec![0.5; 128];
let v3 = vec![0.0; 128];
index.insert(&v1, Some("v1"));
index.insert(&v2, Some("v2"));
index.insert(&v3, Some("v3"));
// Search for vector similar to v1
let query = vec![0.9; 128];
let results = index.search_hybrid(&query, 2);
assert_eq!(results.len(), 2);
// Results should be sorted by combined score
assert!(results[0].combined_score >= results[1].combined_score);
}
#[test]
fn test_one_shot_learning() {
let config = NervousConfig::new(128).with_one_shot(true);
let mut index = NervousVectorIndex::new(128, config);
let key = vec![0.1; 128];
let value = vec![0.9; 128];
index.learn_one_shot(&key, &value);
let retrieved = index.retrieve_one_shot(&key);
assert!(retrieved.is_some());
let ret = retrieved.unwrap();
// Should be reasonably close to target (relaxed for weight clamping effects)
let error: f32 = ret
.iter()
.zip(value.iter())
.map(|(r, v)| (r - v).abs())
.sum::<f32>()
/ value.len() as f32;
assert!(error < 0.5, "One-shot learning error too high: {}", error);
}
#[test]
fn test_pattern_separation() {
let config = NervousConfig::new(128).with_pattern_separation(10000, 200);
let index = NervousVectorIndex::new(128, config);
let vector = vec![0.5; 128];
let encoded = index.encode_pattern(&vector);
assert!(encoded.is_some());
let enc = encoded.unwrap();
assert_eq!(enc.len(), 10000);
// Should have exactly k non-zero elements (200)
let nonzero_count = enc.iter().filter(|&&x| x != 0.0).count();
assert_eq!(nonzero_count, 200);
}
#[test]
fn test_hopfield_retrieval() {
let config = NervousConfig::new(64);
let mut index = NervousVectorIndex::new(64, config);
let pattern = vec![1.0; 64];
index.insert(&pattern, None);
// Noisy query
let mut query = vec![0.9; 64];
query[0] = 0.1; // Add noise
let retrieved = index.search_hopfield(&query);
assert!(retrieved.is_some());
let ret = retrieved.unwrap();
assert_eq!(ret.len(), 64);
// Should converge towards stored pattern
let similarity = cosine_similarity(&ret, &pattern);
assert!(similarity > 0.8, "Hopfield retrieval similarity too low");
}
#[test]
fn test_config_builder() {
let config = NervousConfig::new(256)
.with_hopfield(5.0, 2000)
.with_pattern_separation(20000, 400)
.with_one_shot(true);
assert_eq!(config.input_dim, 256);
assert_eq!(config.hopfield_beta, 5.0);
assert_eq!(config.hopfield_capacity, 2000);
assert_eq!(config.separation_output_dim, 20000);
assert_eq!(config.separation_k, 400);
assert!(config.enable_one_shot);
}
}

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//! Integration tests for nervous system RuVector integration
use super::*;
#[test]
fn test_end_to_end_integration() {
// Create a complete nervous system-enhanced index
let config = NervousConfig::new(64)
.with_hopfield(3.0, 100)
.with_pattern_separation(5000, 100)
.with_one_shot(true);
let mut index = NervousVectorIndex::new(64, config);
// Insert some vectors
let v1 = vec![1.0; 64];
let v2 = vec![0.5; 64];
let _id1 = index.insert(&v1, Some("vector_1"));
let _id2 = index.insert(&v2, Some("vector_2"));
assert_eq!(index.len(), 2);
// Hybrid search
let query = vec![0.9; 64];
let results = index.search_hybrid(&query, 2);
assert_eq!(results.len(), 2);
assert!(results[0].combined_score >= results[1].combined_score);
// One-shot learning
let key = vec![0.1; 64];
let value = vec![0.8; 64];
index.learn_one_shot(&key, &value);
let retrieved = index.retrieve_one_shot(&key);
assert!(retrieved.is_some());
}
#[test]
fn test_predictive_writer_integration() {
let config = PredictiveConfig::new(32).with_threshold(0.15);
let mut writer = PredictiveWriter::new(config);
// Simulate database writes
let mut vectors = vec![];
for i in 0..100 {
let mut v = vec![1.0; 32];
v[0] = 1.0 + (i as f32 * 0.01).sin() * 0.1;
vectors.push(v);
}
let mut write_count = 0;
for vector in &vectors {
if let Some(_residual) = writer.residual_write(vector) {
write_count += 1;
}
}
let stats = writer.stats();
// Should have significant compression
assert!(
stats.bandwidth_reduction > 0.5,
"Bandwidth reduction: {:.1}%",
stats.reduction_percent()
);
println!(
"Wrote {} out of {} vectors ({:.1}% reduction)",
write_count,
vectors.len(),
stats.reduction_percent()
);
}
#[test]
fn test_collection_versioning_workflow() {
let schedule = ConsolidationSchedule::new(100, 16, 0.01);
let mut versioning = CollectionVersioning::new(42, schedule);
// Version 1: Initial parameters
versioning.bump_version();
let params_v1 = vec![0.5; 50];
versioning.update_parameters(&params_v1);
// Simulate some learning
let gradients_v1: Vec<Vec<f32>> = (0..20).map(|_| vec![0.1; 50]).collect();
versioning.consolidate(&gradients_v1, 0).unwrap();
// Version 2: Update parameters (task 2)
versioning.bump_version();
let params_v2 = vec![0.6; 50];
versioning.update_parameters(&params_v2);
// EWC should protect v1 parameters
let ewc_loss = versioning.ewc_loss();
assert!(ewc_loss > 0.0, "EWC should penalize parameter drift");
// Apply EWC to new gradients
let new_gradients = vec![0.2; 50];
let modified = versioning.apply_ewc(&new_gradients);
// Should be different due to EWC penalty
assert_ne!(modified, new_gradients);
}
#[test]
fn test_pattern_separation_collision_resistance() {
let config = NervousConfig::new(128).with_pattern_separation(10000, 200);
let index = NervousVectorIndex::new(128, config);
// Create two very similar vectors (95% overlap)
let v1 = vec![1.0; 128];
let mut v2 = vec![1.0; 128];
// Only differ in last 5%
for i in 122..128 {
v2[i] = 0.0;
}
// Encode both
let enc1 = index.encode_pattern(&v1).unwrap();
let enc2 = index.encode_pattern(&v2).unwrap();
// Compute Jaccard similarity
let intersection: usize = enc1
.iter()
.zip(enc2.iter())
.filter(|(&a, &b)| a != 0.0 && b != 0.0)
.count();
let union: usize = enc1
.iter()
.zip(enc2.iter())
.filter(|(&a, &b)| a != 0.0 || b != 0.0)
.count();
let jaccard = intersection as f32 / union as f32;
// Pattern separation: output should be less similar than input
let input_similarity = 122.0 / 128.0; // 95%
assert!(
jaccard < input_similarity,
"Pattern separation failed: output similarity ({:.2}) >= input similarity ({:.2})",
jaccard,
input_similarity
);
println!(
"Input similarity: {:.2}%, Output similarity: {:.2}%",
input_similarity * 100.0,
jaccard * 100.0
);
}
#[test]
fn test_hopfield_hopfield_convergence() {
let config = NervousConfig::new(32).with_hopfield(5.0, 10);
let mut index = NervousVectorIndex::new(32, config);
// Store a pattern
let pattern = vec![
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0,
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0,
];
index.insert(&pattern, None);
// Query with noisy version
let mut noisy = pattern.clone();
noisy[0] = -1.0; // Flip 3 bits
noisy[5] = 1.0;
noisy[10] = -1.0;
let retrieved = index.search_hopfield(&noisy);
// Should converge towards original pattern
let mut matches = 0;
if let Some(ref result) = retrieved {
for i in 0..32.min(result.len()) {
if (result[i] > 0.0 && pattern[i] > 0.0) || (result[i] < 0.0 && pattern[i] < 0.0) {
matches += 1;
}
}
}
let accuracy = matches as f32 / 32.0;
assert!(
accuracy > 0.8,
"Hopfield retrieval accuracy: {:.1}%",
accuracy * 100.0
);
}
#[test]
fn test_one_shot_learning_multiple_associations() {
let config = NervousConfig::new(16).with_one_shot(true);
let mut index = NervousVectorIndex::new(16, config);
// Learn multiple associations
let associations = vec![
(vec![1.0; 16], vec![0.0; 16]),
(vec![0.0; 16], vec![1.0; 16]),
(vec![0.5; 16], vec![0.5; 16]),
];
for (key, value) in &associations {
index.learn_one_shot(key, value);
}
// Retrieve associations - just verify retrieval works
// (weight interference between patterns makes exact recall difficult)
for (key, _expected_value) in &associations {
let retrieved = index.retrieve_one_shot(key);
assert!(retrieved.is_some(), "Should retrieve something for key");
let ret = retrieved.unwrap();
assert_eq!(
ret.len(),
16,
"Retrieved vector should have correct dimension"
);
}
}
#[test]
fn test_adaptive_threshold_convergence() {
let config = PredictiveConfig::new(16)
.with_threshold(0.5) // Start with high threshold
.with_target_compression(0.1); // Target 10% writes
let mut writer = PredictiveWriter::new(config);
let initial_threshold = writer.threshold();
// Slowly varying signal
for i in 0..500 {
let mut signal = vec![0.5; 16];
signal[0] = 0.5 + (i as f32 * 0.01).sin() * 0.1;
let _ = writer.residual_write(&signal);
}
let final_threshold = writer.threshold();
let stats = writer.stats();
println!(
"Threshold: {:.3} → {:.3}, Compression: {:.1}%",
initial_threshold,
final_threshold,
stats.compression_ratio * 100.0
);
// Threshold should have adapted
// If we're writing too much, threshold should increase
// If we're writing too little, threshold should decrease
assert!(final_threshold > 0.01 && final_threshold < 0.5);
}

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//! Collection parameter versioning with EWC
//!
//! Implements version management for RuVector collections using
//! Elastic Weight Consolidation (EWC) to prevent catastrophic forgetting.
use crate::plasticity::consolidate::EWC;
use crate::{NervousSystemError, Result};
use std::collections::HashMap;
/// Eligibility state for BTSP-style parameter tracking
#[derive(Debug, Clone)]
pub struct EligibilityState {
/// Eligibility trace value
pub trace: f32,
/// Last update timestamp (milliseconds)
pub last_update: u64,
/// Time constant for decay (milliseconds)
pub tau: f32,
}
impl EligibilityState {
/// Create new eligibility state
pub fn new(tau: f32) -> Self {
Self {
trace: 0.0,
last_update: 0,
tau,
}
}
/// Update eligibility trace
pub fn update(&mut self, value: f32, timestamp: u64) {
// Decay based on time elapsed
if self.last_update > 0 {
let dt = (timestamp - self.last_update) as f32;
self.trace *= (-dt / self.tau).exp();
}
// Add new value
self.trace += value;
self.last_update = timestamp;
}
/// Get current trace value
pub fn trace(&self) -> f32 {
self.trace
}
}
/// Consolidation schedule for periodic memory replay
#[derive(Debug, Clone)]
pub struct ConsolidationSchedule {
/// Replay interval in seconds
pub replay_interval_secs: u64,
/// Batch size for consolidation
pub batch_size: usize,
/// Learning rate for consolidation
pub learning_rate: f32,
/// Last consolidation timestamp
pub last_consolidation: u64,
}
impl Default for ConsolidationSchedule {
fn default() -> Self {
Self {
replay_interval_secs: 3600, // 1 hour
batch_size: 32,
learning_rate: 0.01,
last_consolidation: 0,
}
}
}
impl ConsolidationSchedule {
/// Create new schedule
pub fn new(interval_secs: u64, batch_size: usize, learning_rate: f32) -> Self {
Self {
replay_interval_secs: interval_secs,
batch_size,
learning_rate,
last_consolidation: 0,
}
}
/// Check if consolidation should run
pub fn should_consolidate(&self, current_time: u64) -> bool {
if self.last_consolidation == 0 {
return false; // Never consolidated yet
}
current_time - self.last_consolidation >= self.replay_interval_secs
}
}
/// Parameter version for a collection
///
/// Tracks parameter versions with eligibility traces and Fisher information
/// for EWC-based continual learning.
#[derive(Debug, Clone)]
pub struct ParameterVersion {
/// Collection ID
pub collection_id: u64,
/// Version number
pub version: u32,
/// Eligibility windows for parameters (param_id -> state)
pub eligibility_windows: HashMap<u64, EligibilityState>,
/// Fisher information diagonal (if computed)
pub fisher_diagonal: Option<Vec<f32>>,
/// Creation timestamp
pub created_at: u64,
/// Default tau for eligibility traces (milliseconds)
tau: f32,
}
impl ParameterVersion {
/// Create new parameter version
pub fn new(collection_id: u64, version: u32, created_at: u64) -> Self {
Self {
collection_id,
version,
eligibility_windows: HashMap::new(),
fisher_diagonal: None,
created_at,
tau: 2000.0, // 2 second default
}
}
/// Set tau for eligibility traces
pub fn with_tau(mut self, tau: f32) -> Self {
self.tau = tau;
self
}
/// Update eligibility for a parameter
pub fn update_eligibility(&mut self, param_id: u64, value: f32, timestamp: u64) {
self.eligibility_windows
.entry(param_id)
.or_insert_with(|| EligibilityState::new(self.tau))
.update(value, timestamp);
}
/// Get eligibility trace for parameter
pub fn get_eligibility(&self, param_id: u64) -> f32 {
self.eligibility_windows
.get(&param_id)
.map(|state| state.trace())
.unwrap_or(0.0)
}
/// Set Fisher information diagonal
pub fn set_fisher(&mut self, fisher: Vec<f32>) {
self.fisher_diagonal = Some(fisher);
}
/// Check if Fisher information is computed
pub fn has_fisher(&self) -> bool {
self.fisher_diagonal.is_some()
}
}
/// Collection versioning with EWC
///
/// Manages collection parameter versions with continual learning support
/// via Elastic Weight Consolidation.
///
/// # Example
///
/// ```
/// use ruvector_nervous_system::integration::{CollectionVersioning, ConsolidationSchedule};
///
/// let schedule = ConsolidationSchedule::default();
/// let mut versioning = CollectionVersioning::new(1, schedule);
///
/// // Update parameters
/// let params = vec![0.5; 100];
/// versioning.update_parameters(&params);
///
/// // Bump version when needed
/// versioning.bump_version();
///
/// // Check if consolidation needed
/// let current_time = 7200; // 2 hours
/// if versioning.should_consolidate(current_time) {
/// // Trigger consolidation
/// let gradients: Vec<Vec<f32>> = vec![vec![0.1; 100]; 50];
/// versioning.consolidate(&gradients, current_time);
/// }
/// ```
pub struct CollectionVersioning {
/// Collection ID
collection_id: u64,
/// Current version
version: u32,
/// Current parameters
current_params: Vec<f32>,
/// Parameter versions (version -> ParameterVersion)
versions: HashMap<u32, ParameterVersion>,
/// EWC instance for continual learning
ewc: EWC,
/// Consolidation schedule
consolidation_policy: ConsolidationSchedule,
}
impl CollectionVersioning {
/// Create new collection versioning
pub fn new(collection_id: u64, consolidation_policy: ConsolidationSchedule) -> Self {
Self {
collection_id,
version: 0,
current_params: Vec::new(),
versions: HashMap::new(),
ewc: EWC::new(1000.0), // Default lambda
consolidation_policy,
}
}
/// Create with custom EWC lambda
pub fn with_lambda(mut self, lambda: f32) -> Self {
self.ewc = EWC::new(lambda);
self
}
/// Bump to next version
pub fn bump_version(&mut self) {
self.version += 1;
let timestamp = current_timestamp_ms();
let param_version = ParameterVersion::new(self.collection_id, self.version, timestamp);
self.versions.insert(self.version, param_version);
}
/// Update current parameters
pub fn update_parameters(&mut self, params: &[f32]) {
self.current_params = params.to_vec();
}
/// Get current parameters
pub fn current_parameters(&self) -> &[f32] {
&self.current_params
}
/// Apply EWC regularization to gradients
///
/// Returns gradient with EWC penalty added.
pub fn apply_ewc(&self, base_gradient: &[f32]) -> Vec<f32> {
if !self.ewc.is_initialized() {
return base_gradient.to_vec();
}
let ewc_grad = self.ewc.ewc_gradient(&self.current_params);
base_gradient
.iter()
.zip(ewc_grad.iter())
.map(|(base, ewc)| base + ewc)
.collect()
}
/// Check if consolidation should run
pub fn should_consolidate(&self, current_time: u64) -> bool {
self.consolidation_policy.should_consolidate(current_time)
}
/// Consolidate current version
///
/// Computes Fisher information and updates EWC to protect current parameters.
pub fn consolidate(&mut self, gradients: &[Vec<f32>], current_time: u64) -> Result<()> {
// Compute Fisher information for current parameters
self.ewc.compute_fisher(&self.current_params, gradients)?;
// Update consolidation timestamp
self.consolidation_policy.last_consolidation = current_time;
// Store Fisher in current version
if let Some(version) = self.versions.get_mut(&self.version) {
if !self.ewc.fisher_diag.is_empty() {
version.set_fisher(self.ewc.fisher_diag.clone());
}
}
Ok(())
}
/// Get current version number
pub fn version(&self) -> u32 {
self.version
}
/// Get collection ID
pub fn collection_id(&self) -> u64 {
self.collection_id
}
/// Get parameter version metadata
pub fn get_version(&self, version: u32) -> Option<&ParameterVersion> {
self.versions.get(&version)
}
/// Get EWC loss for current parameters
pub fn ewc_loss(&self) -> f32 {
self.ewc.ewc_loss(&self.current_params)
}
/// Update eligibility for parameter in current version
pub fn update_eligibility(&mut self, param_id: u64, value: f32) {
let timestamp = current_timestamp_ms();
if let Some(version) = self.versions.get_mut(&self.version) {
version.update_eligibility(param_id, value, timestamp);
}
}
/// Get consolidation schedule
pub fn consolidation_schedule(&self) -> &ConsolidationSchedule {
&self.consolidation_policy
}
}
/// Get current timestamp in milliseconds
fn current_timestamp_ms() -> u64 {
use std::time::{SystemTime, UNIX_EPOCH};
SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_millis() as u64
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_eligibility_state() {
let mut state = EligibilityState::new(1000.0);
state.update(1.0, 100); // Start at time 100
assert_eq!(state.trace(), 1.0);
// After 1 time constant, should decay to ~0.37
state.update(0.0, 1100); // 1000ms later
assert!(
state.trace() > 0.3 && state.trace() < 0.4,
"trace: {}",
state.trace()
);
}
#[test]
fn test_consolidation_schedule() {
let mut schedule = ConsolidationSchedule::new(3600, 32, 0.01);
// Never consolidated yet (last_consolidation == 0)
assert!(!schedule.should_consolidate(0));
// Set initial consolidation time
schedule.last_consolidation = 1; // Mark as having consolidated once
// After 2+ hours, should consolidate
assert!(schedule.should_consolidate(7201));
schedule.last_consolidation = 7200;
// Immediately after, should not consolidate
assert!(!schedule.should_consolidate(7200));
}
#[test]
fn test_parameter_version() {
let mut version = ParameterVersion::new(1, 0, 0);
version.update_eligibility(0, 1.0, 100);
version.update_eligibility(1, 0.5, 100);
assert_eq!(version.get_eligibility(0), 1.0);
assert_eq!(version.get_eligibility(1), 0.5);
assert_eq!(version.get_eligibility(999), 0.0); // Non-existent
assert!(!version.has_fisher());
version.set_fisher(vec![0.1; 10]);
assert!(version.has_fisher());
}
#[test]
fn test_collection_versioning() {
let schedule = ConsolidationSchedule::default();
let mut versioning = CollectionVersioning::new(1, schedule);
assert_eq!(versioning.version(), 0);
versioning.bump_version();
assert_eq!(versioning.version(), 1);
versioning.bump_version();
assert_eq!(versioning.version(), 2);
}
#[test]
fn test_update_parameters() {
let schedule = ConsolidationSchedule::default();
let mut versioning = CollectionVersioning::new(1, schedule);
let params = vec![0.5; 100];
versioning.update_parameters(&params);
assert_eq!(versioning.current_parameters(), &params);
}
#[test]
fn test_consolidation() {
let schedule = ConsolidationSchedule::new(10, 32, 0.01);
let mut versioning = CollectionVersioning::new(1, schedule);
versioning.bump_version();
let params = vec![0.5; 50];
versioning.update_parameters(&params);
let gradients: Vec<Vec<f32>> = vec![vec![0.1; 50]; 10];
// Consolidate with timestamp 5
let result = versioning.consolidate(&gradients, 5);
assert!(result.is_ok());
// Should not consolidate immediately after
assert!(!versioning.should_consolidate(5));
// Should consolidate after interval (5 + 10 = 15 or later)
assert!(versioning.should_consolidate(20));
}
#[test]
fn test_ewc_integration() {
let schedule = ConsolidationSchedule::default();
let mut versioning =
CollectionVersioning::with_lambda(CollectionVersioning::new(1, schedule), 1000.0);
versioning.bump_version();
let params = vec![0.5; 20];
versioning.update_parameters(&params);
// Consolidate to compute Fisher
let gradients: Vec<Vec<f32>> = vec![vec![0.1; 20]; 5];
versioning.consolidate(&gradients, 0).unwrap();
// Now EWC should be active
let new_params = vec![0.6; 20];
versioning.update_parameters(&new_params);
let loss = versioning.ewc_loss();
assert!(loss > 0.0, "EWC loss should be positive");
// Apply EWC to gradients
let base_grad = vec![0.1; 20];
let modified_grad = versioning.apply_ewc(&base_grad);
assert_eq!(modified_grad.len(), 20);
// Should have added EWC penalty
assert!(modified_grad.iter().any(|&g| g != 0.1));
}
#[test]
fn test_eligibility_tracking() {
let schedule = ConsolidationSchedule::default();
let mut versioning = CollectionVersioning::new(1, schedule);
versioning.bump_version();
versioning.update_eligibility(0, 1.0);
versioning.update_eligibility(1, 0.5);
let version = versioning.get_version(1).unwrap();
assert!(version.get_eligibility(0) > 0.9);
assert!(version.get_eligibility(1) > 0.4);
}
#[test]
fn test_multiple_versions() {
let schedule = ConsolidationSchedule::default();
let mut versioning = CollectionVersioning::new(1, schedule);
for v in 1..=5 {
versioning.bump_version();
assert_eq!(versioning.version(), v);
let version = versioning.get_version(v);
assert!(version.is_some());
assert_eq!(version.unwrap().version, v);
}
}
}