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bc4e63d4d4
Complete implementation of the Neural DAG Learning system combining RuVector vector database with QuDAG quantum-resistant consensus. Core Features: - QueryDag structure with HashMap-based adjacency and cycle detection - 18+ operator types (SeqScan, HnswScan, HashJoin, NestedLoop, etc.) - Topological, DFS, and BFS traversal iterators - JSON/binary serialization Attention Mechanisms (7 total): - Basic: Topological, CausalCone, CriticalPath, MinCutGated - Advanced: HierarchicalLorentz, ParallelBranch, TemporalBTSP - UCB bandit selector for automatic mechanism selection - LRU attention cache with 10k entry default SONA (Self-Optimizing Neural Architecture): - MicroLoRA adaptation (<100μs, rank-2) - TrajectoryBuffer with lock-free ArrayQueue (10k capacity) - ReasoningBank with K-means++ clustering - EWC++ for catastrophic forgetting prevention (λ=5000) MinCut Optimization: - O(n^0.12) subpolynomial amortized updates - Local k-cut approximation for sublinear bottleneck detection - Criticality-based flow computation - Redundancy analysis and repair suggestions Self-Healing System: - Z-score anomaly detection with adaptive thresholds - Index health monitoring (HNSW/IVFFlat metrics) - Learning drift detection with ADWIN algorithm - Repair strategies: reindex, parameter tuning, learning reset QuDAG Integration: - ML-KEM-768 quantum-resistant encryption - ML-DSA-65 quantum-resistant signatures - Differential privacy (Laplace/Gaussian mechanisms) - rUv token staking, rewards (5% APY), governance (67% threshold) PostgreSQL Extension: - GUC variables for configuration - Planner/executor hooks for query interception - Background worker for continuous learning - 50+ SQL functions for all features Testing: - 46+ integration tests across all modules - 11 benchmark groups for performance validation - Test fixtures and data generators - Mock QuDAG client for isolated testing Documentation: - Comprehensive README with architecture overview - 5 example programs demonstrating all features - Implementation notes for attention mechanisms Total: ~12,000+ lines of new Rust code
77 lines
2.3 KiB
Rust
77 lines
2.3 KiB
Rust
//! Basic usage example for Neural DAG Learning
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use ruvector_dag::dag::{QueryDag, OperatorNode, OperatorType};
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fn main() {
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println!("=== Neural DAG Learning - Basic Usage ===\n");
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// Create a new DAG
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let mut dag = QueryDag::new();
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// Add nodes representing query operators
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println!("Building query DAG...");
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let scan = dag.add_node(OperatorNode::seq_scan(0, "users"));
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println!(" Added SeqScan on 'users' (id: {})", scan);
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let filter = dag.add_node(OperatorNode::filter(1, "age > 18"));
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println!(" Added Filter 'age > 18' (id: {})", filter);
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let sort = dag.add_node(OperatorNode::sort(2, vec!["name".to_string()]));
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println!(" Added Sort by 'name' (id: {})", sort);
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let limit = dag.add_node(OperatorNode::limit(3, 10));
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println!(" Added Limit 10 (id: {})", limit);
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let result = dag.add_node(OperatorNode::new(4, OperatorType::Result));
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println!(" Added Result (id: {})", result);
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// Connect nodes
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dag.add_edge(scan, filter).unwrap();
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dag.add_edge(filter, sort).unwrap();
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dag.add_edge(sort, limit).unwrap();
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dag.add_edge(limit, result).unwrap();
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println!("\nDAG Statistics:");
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println!(" Nodes: {}", dag.node_count());
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println!(" Edges: {}", dag.edge_count());
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// Compute topological order
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let order = dag.topological_sort().unwrap();
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println!("\nTopological Order: {:?}", order);
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// Compute depths
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let depths = dag.compute_depths();
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println!("\nNode Depths:");
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for (id, depth) in &depths {
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println!(" Node {}: depth {}", id, depth);
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}
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// Get children
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println!("\nNode Children:");
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for node_id in 0..5 {
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let children = dag.get_children(node_id);
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println!(" Node {}: {:?}", node_id, children);
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}
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// Serialize to JSON
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let json = dag.to_json().unwrap();
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println!("\nJSON (first 200 chars):\n{}", &json[..json.len().min(200)]);
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// Demonstrate iterators
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println!("\nDFS Traversal:");
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for (i, node_id) in dag.dfs_iter(scan).enumerate() {
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if i < 10 {
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println!(" Visit: {}", node_id);
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}
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}
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println!("\nBFS Traversal:");
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for (i, node_id) in dag.bfs_iter(scan).enumerate() {
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if i < 10 {
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println!(" Visit: {}", node_id);
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}
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}
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println!("\n=== Example Complete ===");
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}
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