mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-25 15:03:46 +00:00
61a0ff897b
## Learning Module (src/learning/mod.rs) - ReasoningBank for pattern storage with similarity lookup and pruning - TrajectoryTracker ring buffer for task execution tracking - Spike-driven attention for 87x energy efficiency (based on Yao et al.) - Multi-head attention for distributed task routing - NetworkLearning unified interface for edge nodes ## RAC Module (src/rac/mod.rs) - Adversarial Coherence Thesis Implements the 12 axioms for browser-scale adversarial truth maintenance: 1. Connectivity is not truth 2. Everything is an event 3. No destructive edits (deprecation only) 4. Every claim is scoped 5. Semantics drift is expected 6. Disagreement is signal 7. Authority is scoped, not global 8. Witnesses matter 9. Quarantine is mandatory 10. All decisions are replayable 11. Equivocation is detectable 12. Local learning is allowed Core components: - Append-only Merkle event log for tamper-evident history - CoherenceEngine for conflict detection and resolution - QuarantineManager for contested claims - Authority policy and verifier traits - Decision traces for audit and replay ## Integration - Learning and RAC integrated into EdgeNetNode - 28 tests pass (13 new tests for learning/RAC) References: - FLP Impossibility (MIT CSAIL) - PBFT Byzantine Fault Tolerance - CRDTs (Lip6) - RFC 6962 Certificate Transparency 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> |
||
|---|---|---|
| .. | ||
| agentic-jujutsu | ||
| apify | ||
| docs | ||
| edge | ||
| edge-full/pkg | ||
| edge-net | ||
| exo-ai-2025 | ||
| google-cloud | ||
| graph | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| refrag-pipeline | ||
| rust | ||
| ruvLLM | ||
| scipix | ||
| spiking-network | ||
| subpolynomial-time | ||
| ultra-low-latency-sim | ||
| wasm/ios | ||
| wasm-react | ||
| wasm-vanilla | ||
| bounded_instance_demo.rs | ||
| 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