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https://github.com/ruvnet/RuVector.git
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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
28 lines
971 B
JSON
28 lines
971 B
JSON
{
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"simple_scan": {
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"nodes": [
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{"id": 0, "type": "SeqScan", "table": "users", "cost": 100.0},
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{"id": 1, "type": "Filter", "predicate": "id > 0", "cost": 10.0},
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{"id": 2, "type": "Result", "cost": 1.0}
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],
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"edges": [[0, 1], [1, 2]]
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},
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"vector_search": {
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"nodes": [
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{"id": 0, "type": "HnswScan", "index": "vec_idx", "ef_search": 64, "cost": 50.0},
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{"id": 1, "type": "Limit", "count": 10, "cost": 1.0},
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{"id": 2, "type": "Result", "cost": 1.0}
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],
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"edges": [[0, 1], [1, 2]]
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},
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"join_query": {
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"nodes": [
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{"id": 0, "type": "SeqScan", "table": "orders", "cost": 500.0},
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{"id": 1, "type": "IndexScan", "index": "products_pkey", "cost": 100.0},
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{"id": 2, "type": "HashJoin", "key": "product_id", "cost": 200.0},
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{"id": 3, "type": "Sort", "keys": ["created_at"], "cost": 50.0},
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{"id": 4, "type": "Result", "cost": 1.0}
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],
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"edges": [[0, 2], [1, 2], [2, 3], [3, 4]]
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}
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}
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