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rUv 4d5d3bb092 feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 
* 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>
2025-12-01 22:30:15 -05:00
..
agentic-jujutsu feat: Add comprehensive agentic-jujutsu integration examples and tests 2025-11-22 03:12:31 +00:00
docs docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
exo-ai-2025 docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
google-cloud feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 2025-12-01 22:30:15 -05:00
graph docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
nodejs docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
onnx-embeddings feat(examples): Add ONNX-Rust embeddings example for RuVector 2025-11-29 18:11:26 -05:00
refrag-pipeline feat: Add REFRAG pipeline example demonstrating 30x RAG latency reduction 2025-11-27 20:59:23 +00:00
rust docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
scipix Plan Rust Mathpix clone for ruvector (#28) 2025-11-29 17:34:47 -05:00
spiking-network feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 2025-12-01 22:30:15 -05:00
wasm-react docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
wasm-vanilla docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
README.md docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00

README.md

RuVector Examples

Comprehensive examples demonstrating RuVector's capabilities across multiple platforms and use cases.

Directory Structure

examples/
├── rust/                 # Rust SDK examples
├── nodejs/               # Node.js SDK examples
├── graph/                # Graph database features
├── wasm-react/           # React + WebAssembly integration
├── wasm-vanilla/         # Vanilla JS + WebAssembly
├── agentic-jujutsu/      # AI agent version control
├── exo-ai-2025/          # Advanced cognitive substrate
├── refrag-pipeline/      # Document processing pipeline
└── docs/                 # Additional documentation

Quick Start by Platform

Rust

cd rust
cargo run --example basic_usage
cargo run --example advanced_features
cargo run --example agenticdb_demo

Node.js

cd nodejs
npm install
node basic_usage.js
node semantic_search.js

WebAssembly (React)

cd wasm-react
npm install
npm run dev

WebAssembly (Vanilla)

cd wasm-vanilla
# Open index.html in browser

Example Categories

Category Directory Description
Core API rust/basic_usage.rs Vector DB fundamentals
Batch Ops rust/batch_operations.rs High-throughput ingestion
RAG Pipeline rust/rag_pipeline.rs Retrieval-Augmented Generation
Advanced rust/advanced_features.rs Hypergraphs, neural hashing
AgenticDB rust/agenticdb_demo.rs AI agent memory system
GNN rust/gnn_example.rs Graph Neural Networks
Graph graph/ Cypher queries, clustering
Node.js nodejs/ JavaScript integration
WASM React wasm-react/ Modern React apps
WASM Vanilla wasm-vanilla/ Browser without framework
Agentic Jujutsu agentic-jujutsu/ Multi-agent version control
EXO-AI 2025 exo-ai-2025/ Cognitive substrate research
Refrag refrag-pipeline/ Document fragmentation

Feature Highlights

Vector Database Core

  • High-performance similarity search
  • Multiple distance metrics (Cosine, Euclidean, Dot Product)
  • Metadata filtering
  • Batch operations

Advanced Features

  • Hypergraph Index: Multi-entity relationships
  • Temporal Hypergraph: Time-aware relationships
  • Causal Memory: Cause-effect chains
  • Learned Index: ML-optimized indexing
  • Neural Hash: Locality-sensitive hashing
  • Topological Analysis: Persistent homology

AgenticDB

  • Reflexion episodes (self-critique)
  • Skill library (consolidated patterns)
  • Causal memory (hypergraph relationships)
  • Learning sessions (RL training data)
  • Vector embeddings (core storage)

EXO-AI Cognitive Substrate

  • exo-core: IIT consciousness, thermodynamics
  • exo-temporal: Causal memory coordination
  • exo-hypergraph: Topological structures
  • exo-manifold: Continuous deformation
  • exo-exotic: 10 cutting-edge experiments
  • exo-wasm: Browser deployment
  • exo-federation: Distributed consensus
  • exo-node: Native bindings
  • exo-backend-classical: Classical compute

Running Benchmarks

# Rust benchmarks
cargo bench --example advanced_features

# Refrag pipeline benchmarks
cd refrag-pipeline
cargo bench

# EXO-AI benchmarks
cd exo-ai-2025
cargo bench

Related Documentation

License

MIT OR Apache-2.0