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Dragan Spiridonov
6b249c034f
Implements 23 tasks across 4 phases per docs/implementation/ruvector-integration-plan.md: Phase 1 — Foundation (make what we have faster): - Native HNSW backend via @ruvector/router (150x search speedup, ADR-081) - Metadata filtering layer with composable AND/OR/NOT/FIELD expressions - Temporal tensor compression (Int8Array quantization, 4x memory reduction) - Deterministic golden-ratio dithering for cross-platform reproducibility - 15 feature flags (all default false) with CLI profiles Phase 2 — Intelligence (make agents smarter): - Neural TinyDancer router with REINFORCE policy gradient (ADR-082) - SONA three-loop enhancement (MicroLoRA 0.18μs/adapt via WASM) - Cross-domain transfer learning with Thompson Sampling (ADR-084) - Regret tracking with log-log regression growth classification Phase 3 — Safety (make agents trustworthy): - Sheaf-gated test validation via prime-radiant CohomologyEngine - Coherence-gated agent actions (3-filter pipeline, ADR-083) - Witness chain audit trail with SHA-256 hash linking - HNSW health monitoring with spectral analysis Phase 4 — Differentiation (make AQE unique): - Cognitive container export/import (RVF v2) - DAG attention scheduler for dependency-aware test ordering - CNN visual regression with spatial pooling embeddings - Behavior tree orchestration (Sequence/Selector/Parallel) - Reasoning QEC with majority-vote consensus - Browser QE dashboard scaffolding Also: remove obsolete v2-to-v3-migration test (source already removed), add ADR-081 through ADR-085, research docs, benchmarks, and integration test suites for all 4 phases. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2.9 KiB
2.9 KiB
RuVector Graph, GNN & DAG Crates Research
Crates Covered
- ruvector-graph - Full graph database (Cypher, hypergraphs, HNSW, RAFT, federation)
- ruvector-graph-wasm / ruvector-graph-node - Browser/Node.js bindings
- ruvector-gnn - GNN on HNSW topology with EWC++ and 5-level compression
- ruvector-gnn-node / ruvector-gnn-wasm - Bindings
- ruvector-graph-transformer - 8 specialized modules with proof-gated mutation
- ruvector-graph-transformer-node / ruvector-graph-transformer-wasm - Bindings
- ruvector-dag - Self-learning query optimizer with 7 DAG attention mechanisms
- ruvector-dag-wasm - 58KB minimal WASM DAG
Key Findings
ruvector-graph
- Full Cypher query parser built with
nom(lexer, parser, optimizer) - Native hyperedge support (one edge connects N nodes)
- HNSW vector search on every node via SimSIMD
- ACID transactions, RAFT consensus, sharding, gRPC federation
- Roaring bitmap indexes for microsecond-scale label filtering
- Graph Neural Engine and RAG engine built in
ruvector-gnn
- GNN message passing directly on HNSW graph topology (index IS the graph)
- 5-level tensor compression: None/Half/PQ8/PQ4/Binary (by access frequency)
- EWC++ for preventing catastrophic forgetting
- Replay buffer with reservoir sampling
- Memory-mapped weight storage for models larger than RAM
ruvector-graph-transformer (8 Modules)
- Proof-Gated Mutation: Every mutation requires formal proof. 82-byte attestation receipts.
- Sublinear Attention: O(n log n) via LSH-bucket, PPR-sampled, spectral sparsification
- Verified Training: Delta-apply rollback with BLAKE3 certificates
- Physics-Informed: Hamiltonian graph networks, symplectic leapfrog, gauge equivariance
- Biological: Spiking attention, Hebbian/STDP learning, dendritic branching
- Self-Organizing: Morphogenetic fields, L-system graph growth
- Manifold: Product manifold S^n x H^m x R^k, Riemannian Adam, geodesic message passing
- Temporal-Causal: Causal masking, Granger causality extraction
- Economic: Nash equilibrium attention, Shapley attribution
ruvector-dag
- 7 DAG attention mechanisms with dynamic policy-driven selection
- SONA engine with MicroLoRA adaptation in <100us per query
- O(n^0.12) subpolynomial MinCut for bottleneck detection
- Self-healing: Z-score anomaly detection, predictive intervention
- QuDAG: Quantum-resistant distributed pattern learning with ML-KEM/ML-DSA
- Convergence demo: 847ms -> 156ms (81.6% improvement) over 10 runs
AQE Integration Opportunities
- GNN on HNSW for +12.4% better pattern recognition (already referenced in AQE)
- Proof-gated mutations for tamper-evident quality gate decisions
- DAG self-learning for AQE's autonomous query optimization
- Granger causality to identify causal relationships between test failures
- Nash equilibrium attention for multi-agent resource allocation
- 58KB DAG WASM for AQE's Agent Booster (Tier 1)