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Fixed policy sign flip (Mode A): risk_score = R - 30*D (was R + 30*D) Distractors now reduce effective range, making Mode A conservative under distractors. This is the defensible control arm: a rational fixed agent should be more cautious when distractors are present. Mode C must learn to outperform this baseline. EarlyCommitPenalty wired into bandit reward: SkipModeStats now tracks early_commit_penalty_sum per arm. reward() includes robustness_penalty = 0.2 * avg_penalty. This means Mode C can actually learn to avoid early wrong commits in distractor-heavy contexts. Previously the penalty was only printed, not optimized. Context buckets expanded to 18: 3 range (small/medium/large) × 3 distractor (clean/some/heavy) × 2 noise (clean/noisy) = 18 buckets. Previous: 4 range × 2 distractor = 8 (too coarse for bandit). Noise flag now flows through AdaptiveSolver.noisy_hint. New ablation assertion: c_penalty_better_than_b: Mode C EarlyCommitPenalty must be ≤90% of Mode B penalty. Proves robustness improvement is explicit, not just noise_accuracy-based. Acceptance test noise plumbing: solver.noisy_hint set to true for noisy puzzles in both training and holdout evaluation. Context buckets now correctly distinguish clean vs noisy conditions. 81 tests passing (61 lib + 20 integration). https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G |
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| agentic-jujutsu | ||
| apify | ||
| app-clip | ||
| benchmarks | ||
| data | ||
| delta-behavior | ||
| dna | ||
| docs | ||
| edge | ||
| edge-full/pkg | ||
| edge-net | ||
| exo-ai-2025 | ||
| google-cloud | ||
| graph | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| neural-trader | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| OSpipe | ||
| prime-radiant | ||
| pwa-loader | ||
| refrag-pipeline | ||
| rust | ||
| ruvLLM | ||
| rvf | ||
| scipix | ||
| spiking-network | ||
| subpolynomial-time | ||
| ultra-low-latency-sim | ||
| vibecast-7sense | ||
| vwm-viewer | ||
| 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