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#28 (null): hub_modules ∈ {0, 1, 2, 3, 4, 6, 8} at N=1024/40-modules. Peak stays at hub=3 → 0.516. hub ∈ [0, 2] cluster at 0.487–0.488; hub ≥ 4 collapses to 0.37–0.43. Narrow non-monotonic peak, not a smooth ridge. The "smaller hub wins" pattern from N=512 does NOT generalise to N=1024 — 2nd ADR-level case of "hypothesis from small-N extrapolates wrong at large N" (1st was item 22 on fixed γ). #29: fine num_modules ∈ {20, 25, 30, 35, 40, 50, 60, 80} at N=1024/ hub=3. New N=1024 peak: 0.531 @ modules=30 (density 34.1), γ=3.0 (70 communities vs 30 truth). Secondary peak at modules=80/γ=2.5 scores 0.515 — multi-modal landscape confirmed. Finding: at N=1024 the optimal density is 34.1 neurons/module, not 25.6. At N=512 it's 25.6. The 4-D landscape (N × density × γ × hub) does not factorize. AC-3a gap at N=1024 now 1.41× (down from 1.47×). Best-across-scales remains 0.599 @ (N=512, modules=20, hub=1, γ=4.0) — 1.25× gap. - tests/leiden_cpm.rs: leiden_cpm_hub_fraction_sweep_at_n1024, leiden_cpm_module_count_sweep_at_n1024_hub3 - ADR-154 §17 rows 28, 29 + heading 27 → 29 Co-Authored-By: claude-flow <ruv@ruv.net> |
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| apify | ||
| app-clip | ||
| benchmarks | ||
| boundary-discovery | ||
| brain-boundary-discovery | ||
| climate-consciousness | ||
| cmb-boundary-discovery | ||
| cmb-consciousness | ||
| connectome-fly | ||
| data | ||
| decompiler-dashboard | ||
| delta-behavior | ||
| dna | ||
| docs | ||
| dragnes | ||
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| ecosystem-consciousness | ||
| edge | ||
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| edge-net | ||
| exo-ai-2025 | ||
| frb-boundary-discovery | ||
| gene-consciousness | ||
| google-cloud | ||
| graph | ||
| gw-consciousness | ||
| health-boundary-discovery | ||
| infrastructure-boundary-discovery | ||
| market-boundary-discovery | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| music-boundary-discovery | ||
| neural-trader | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| OSpipe | ||
| pandemic-boundary-discovery | ||
| prime-radiant | ||
| pwa-loader | ||
| quantum-consciousness | ||
| real-eeg-analysis | ||
| real-eeg-multi-seizure | ||
| refrag-pipeline | ||
| robotics | ||
| rust | ||
| ruvLLM | ||
| rvf | ||
| rvf-desktop | ||
| rvf-kernel-optimized | ||
| scipix | ||
| seizure-clinical-report | ||
| seizure-therapeutic-sim | ||
| seti-boundary-discovery | ||
| seti-exotic-signals | ||
| spiking-network | ||
| subpolynomial-time | ||
| temporal-attractor-discovery | ||
| train-discoveries | ||
| ultra-low-latency-sim | ||
| verified-applications | ||
| vibecast-7sense | ||
| void-boundary-discovery | ||
| vwm-viewer | ||
| wasm/ios | ||
| wasm-react | ||
| wasm-vanilla | ||
| weather-boundary-discovery | ||
| bounded_instance_demo.rs | ||
| README.md | ||
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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