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>
122 lines
2.8 KiB
TOML
122 lines
2.8 KiB
TOML
[workspace]
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exclude = ["crates/micro-hnsw-wasm"]
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members = [
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"crates/ruvector-core",
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"crates/ruvector-node",
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"crates/ruvector-wasm",
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"crates/ruvector-cli",
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"crates/ruvector-bench",
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"crates/ruvector-metrics",
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"crates/ruvector-filter",
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"crates/ruvector-router-core",
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"crates/ruvector-router-cli",
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"crates/ruvector-router-ffi",
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"crates/ruvector-router-wasm",
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"crates/ruvector-server",
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"crates/ruvector-snapshot",
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"crates/ruvector-tiny-dancer-core",
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"crates/ruvector-tiny-dancer-wasm",
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"crates/ruvector-tiny-dancer-node",
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"crates/ruvector-collections",
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"crates/ruvector-cluster",
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"crates/ruvector-raft",
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"crates/ruvector-replication",
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"crates/ruvector-graph",
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"crates/ruvector-graph-node",
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"crates/ruvector-graph-wasm",
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"crates/ruvector-gnn",
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"crates/ruvector-gnn-node",
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"crates/ruvector-gnn-wasm",
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"crates/ruvector-attention",
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"crates/ruvector-attention-wasm",
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"crates/ruvector-attention-node",
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"examples/refrag-pipeline",
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"examples/scipix",
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"examples/google-cloud",
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]
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resolver = "2"
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[workspace.package]
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version = "0.1.19"
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edition = "2021"
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rust-version = "1.77"
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license = "MIT"
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authors = ["Ruvector Team"]
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repository = "https://github.com/ruvnet/ruvector"
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[workspace.dependencies]
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# Core functionality
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redb = "2.1"
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memmap2 = "0.9"
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hnsw_rs = "0.3"
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simsimd = "5.9"
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rayon = "1.10"
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crossbeam = "0.8"
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# Serialization
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rkyv = "0.8"
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bincode = { version = "2.0.0-rc.3", features = ["serde"] }
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# Node.js bindings
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napi = { version = "2.16", default-features = false, features = ["napi9", "async", "tokio_rt"] }
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napi-derive = "2.16"
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# WASM
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wasm-bindgen = "0.2"
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wasm-bindgen-futures = "0.4"
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js-sys = "0.3"
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web-sys = { version = "0.3", features = ["Worker", "MessagePort", "console"] }
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getrandom = { version = "0.3", features = ["wasm_js"] }
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# Async runtime
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tokio = { version = "1.41", features = ["rt-multi-thread", "sync", "macros"] }
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futures = "0.3"
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# Error handling and utilities
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thiserror = "2.0"
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anyhow = "1.0"
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter"] }
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# Math and numerics
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ndarray = "0.16"
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rand = "0.8"
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rand_distr = "0.4"
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# Time and UUID
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chrono = "0.4"
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uuid = { version = "1.11", features = ["v4", "serde", "js"] }
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# CLI
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clap = { version = "4.5", features = ["derive", "cargo"] }
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indicatif = "0.17"
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console = "0.15"
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# Testing and benchmarking
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criterion = { version = "0.5", features = ["html_reports"] }
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proptest = "1.5"
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mockall = "0.13"
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# Performance
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dashmap = "6.1"
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parking_lot = "0.12"
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once_cell = "1.20"
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[profile.release]
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opt-level = 3
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lto = "fat"
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codegen-units = 1
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strip = true
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panic = "abort"
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[profile.bench]
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inherits = "release"
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debug = true
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[profile.dev]
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opt-level = 0
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debug = true
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[profile.test]
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