mirror of
https://github.com/ruvnet/RuVector.git
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* docs: Add comprehensive GNN v2 implementation plans Add 22 detailed planning documents for 19 advanced GNN features: Tier 1 (Immediate - 3-6 months): - GNN-Guided HNSW Routing (+25% QPS) - Incremental Graph Learning/ATLAS (10-100x faster updates) - Neuro-Symbolic Query Execution (hybrid neural + logical) Tier 2 (Medium-Term - 6-12 months): - Hyperbolic Embeddings (Poincaré ball model) - Degree-Aware Adaptive Precision (2-4x memory reduction) - Continuous-Time Dynamic GNN (concept drift detection) Tier 3 (Research - 12+ months): - Graph Condensation (10-100x smaller graphs) - Native Sparse Attention (8-15x GPU speedup) - Quantum-Inspired Attention (long-range dependencies) Novel Innovations (10 experimental features): - Gravitational Embedding Fields, Causal Attention Networks - Topology-Aware Gradient Routing, Embedding Crystallization - Semantic Holography, Entangled Subspace Attention - Predictive Prefetch Attention, Morphological Attention - Adversarial Robustness Layer, Consensus Attention Includes comprehensive regression prevention strategy with: - Feature flag system for safe rollout - Performance baseline (186 tests + 6 search_v2 tests) - Automated rollback mechanisms Related to #38 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(micro-hnsw-wasm): Add neuromorphic HNSW v2.3 with SNN integration ## New Crate: micro-hnsw-wasm v2.3.0 - Published to crates.io: https://crates.io/crates/micro-hnsw-wasm - 11.8KB WASM binary with 58 exported functions - Neuromorphic vector search combining HNSW + Spiking Neural Networks ### Core Features - HNSW graph-based approximate nearest neighbor search - Multi-distance metrics: L2, Cosine, Dot product - GNN extensions: typed nodes, edge weights, neighbor aggregation - Multi-core sharding: 256 cores × 32 vectors = 8K total ### Spiking Neural Network (SNN) - LIF (Leaky Integrate-and-Fire) neurons with membrane dynamics - STDP (Spike-Timing Dependent Plasticity) learning - Spike propagation through graph topology - HNSW→SNN bridge for similarity-driven neural activation ### Novel Neuromorphic Features (v2.3) - Spike-Timing Vector Encoding (rate-to-time conversion) - Homeostatic Plasticity (self-stabilizing thresholds) - Oscillatory Resonance (40Hz gamma synchronization) - Winner-Take-All Circuits (competitive selection) - Dendritic Computation (nonlinear branch integration) - Temporal Pattern Recognition (spike history matching) - Combined Neuromorphic Search pipeline ### Performance Optimizations - 5.5x faster SNN tick (2,726ns → 499ns) - 18% faster STDP learning - Pre-computed reciprocal constants - Division elimination in hot paths ### Documentation & Organization - Reorganized docs into subdirectories (gnn/, implementation/, publishing/, status/) - Added comprehensive README with badges, SEO, citations - Added benchmark.js and test_wasm.js test suites - Added DEEP_REVIEW.md with performance analysis - Added Verilog RTL for ASIC synthesis 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
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62 lines
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1.5 KiB
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{
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"name": "@ruvector/gnn",
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"version": "0.1.19",
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"description": "Graph Neural Network capabilities for Ruvector - Node.js bindings",
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"main": "index.js",
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"types": "index.d.ts",
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"napi": {
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"name": "ruvector-gnn",
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"triples": {
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"defaults": true,
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"additional": [
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"x86_64-unknown-linux-musl",
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"aarch64-unknown-linux-gnu",
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"aarch64-unknown-linux-musl",
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"aarch64-apple-darwin",
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"x86_64-pc-windows-msvc"
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]
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}
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},
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"scripts": {
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"artifacts": "napi artifacts",
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"build": "napi build --platform --release",
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"build:debug": "napi build --platform",
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"prepublishOnly": "napi prepublish -t npm",
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"test": "node --test test/*.test.js",
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"version": "napi version"
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},
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"keywords": [
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"ruvector",
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"gnn",
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"graph-neural-network",
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"machine-learning",
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"vector-database",
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"hnsw",
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"napi-rs"
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],
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"author": "Ruvector Team",
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"license": "MIT",
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"repository": {
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"type": "git",
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"url": "https://github.com/ruvnet/ruvector"
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},
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"devDependencies": {
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"@napi-rs/cli": "^2.16.0"
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},
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"engines": {
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"node": ">= 10"
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},
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"publishConfig": {
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"registry": "https://registry.npmjs.org/",
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"access": "public"
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},
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"optionalDependencies": {
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"@ruvector/gnn-win32-x64-msvc": "0.1.19",
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"@ruvector/gnn-darwin-x64": "0.1.19",
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"@ruvector/gnn-linux-x64-gnu": "0.1.19",
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"@ruvector/gnn-linux-x64-musl": "0.1.19",
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"@ruvector/gnn-linux-arm64-gnu": "0.1.19",
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"@ruvector/gnn-linux-arm64-musl": "0.1.19",
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"@ruvector/gnn-darwin-arm64": "0.1.19"
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}
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} |