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docs: Organize examples/ with comprehensive READMEs
- Reorganize standalone files into appropriate subfolders - Move Rust examples to rust/ directory - Move documentation to docs/ directory - Add detailed README.md for each example category: - Main examples overview - Rust SDK examples with code samples - Graph database features - Node.js integration guide - React + WASM tutorial - Vanilla WASM guide - EXO-AI 2025 comprehensive documentation - Include discoveries, applications, and insights
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# RuVector Examples
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Comprehensive examples demonstrating RuVector's capabilities across multiple platforms and use cases.
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## Directory Structure
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```
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examples/
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├── rust/ # Rust SDK examples
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├── nodejs/ # Node.js SDK examples
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├── graph/ # Graph database features
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├── wasm-react/ # React + WebAssembly integration
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├── wasm-vanilla/ # Vanilla JS + WebAssembly
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├── agentic-jujutsu/ # AI agent version control
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├── exo-ai-2025/ # Advanced cognitive substrate
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├── refrag-pipeline/ # Document processing pipeline
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└── docs/ # Additional documentation
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```
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## Quick Start by Platform
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### Rust
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```bash
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cd rust
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cargo run --example basic_usage
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cargo run --example advanced_features
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cargo run --example agenticdb_demo
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```
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### Node.js
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```bash
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cd nodejs
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npm install
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node basic_usage.js
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node semantic_search.js
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```
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### WebAssembly (React)
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```bash
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cd wasm-react
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npm install
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npm run dev
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```
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### WebAssembly (Vanilla)
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```bash
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cd wasm-vanilla
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# Open index.html in browser
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```
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## Example Categories
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| Category | Directory | Description |
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|----------|-----------|-------------|
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| **Core API** | `rust/basic_usage.rs` | Vector DB fundamentals |
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| **Batch Ops** | `rust/batch_operations.rs` | High-throughput ingestion |
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| **RAG Pipeline** | `rust/rag_pipeline.rs` | Retrieval-Augmented Generation |
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| **Advanced** | `rust/advanced_features.rs` | Hypergraphs, neural hashing |
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| **AgenticDB** | `rust/agenticdb_demo.rs` | AI agent memory system |
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| **GNN** | `rust/gnn_example.rs` | Graph Neural Networks |
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| **Graph** | `graph/` | Cypher queries, clustering |
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| **Node.js** | `nodejs/` | JavaScript integration |
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| **WASM React** | `wasm-react/` | Modern React apps |
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| **WASM Vanilla** | `wasm-vanilla/` | Browser without framework |
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| **Agentic Jujutsu** | `agentic-jujutsu/` | Multi-agent version control |
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| **EXO-AI 2025** | `exo-ai-2025/` | Cognitive substrate research |
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| **Refrag** | `refrag-pipeline/` | Document fragmentation |
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## Feature Highlights
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### Vector Database Core
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- High-performance similarity search
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- Multiple distance metrics (Cosine, Euclidean, Dot Product)
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- Metadata filtering
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- Batch operations
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|
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### Advanced Features
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- **Hypergraph Index**: Multi-entity relationships
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- **Temporal Hypergraph**: Time-aware relationships
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- **Causal Memory**: Cause-effect chains
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- **Learned Index**: ML-optimized indexing
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- **Neural Hash**: Locality-sensitive hashing
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- **Topological Analysis**: Persistent homology
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|
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### AgenticDB
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- Reflexion episodes (self-critique)
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- Skill library (consolidated patterns)
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- Causal memory (hypergraph relationships)
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- Learning sessions (RL training data)
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- Vector embeddings (core storage)
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|
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### EXO-AI Cognitive Substrate
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- **exo-core**: IIT consciousness, thermodynamics
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- **exo-temporal**: Causal memory coordination
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- **exo-hypergraph**: Topological structures
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- **exo-manifold**: Continuous deformation
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- **exo-exotic**: 10 cutting-edge experiments
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- **exo-wasm**: Browser deployment
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- **exo-federation**: Distributed consensus
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- **exo-node**: Native bindings
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- **exo-backend-classical**: Classical compute
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## Running Benchmarks
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```bash
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# Rust benchmarks
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cargo bench --example advanced_features
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# Refrag pipeline benchmarks
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cd refrag-pipeline
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cargo bench
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# EXO-AI benchmarks
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cd exo-ai-2025
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cargo bench
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```
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## Related Documentation
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- [Graph CLI Usage](docs/graph-cli-usage.md)
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- [Graph WASM Usage](docs/graph_wasm_usage.html)
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- [Agentic Jujutsu](agentic-jujutsu/README.md)
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- [Refrag Pipeline](refrag-pipeline/README.md)
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- [EXO-AI 2025](exo-ai-2025/README.md)
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## License
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|
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MIT OR Apache-2.0
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# RuVector Documentation
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Additional documentation and usage guides.
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## Contents
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| File | Description |
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|------|-------------|
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| `graph-cli-usage.md` | Command-line interface for graph operations |
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| `graph_wasm_usage.html` | Interactive WASM graph demo |
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## Graph CLI
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The graph CLI provides command-line access to RuVector's graph features:
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```bash
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ruvector-graph --help
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ruvector-graph query "MATCH (n) RETURN n LIMIT 10"
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ruvector-graph import data.json
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ruvector-graph export output.json
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```
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See [graph-cli-usage.md](graph-cli-usage.md) for full documentation.
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## WASM Demo
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Open `graph_wasm_usage.html` in a browser to see an interactive demonstration of RuVector's WebAssembly graph capabilities.
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## Additional Resources
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- [Main Examples README](../README.md)
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- [Rust Examples](../rust/README.md)
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- [Node.js Examples](../nodejs/README.md)
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- [React + WASM](../wasm-react/README.md)
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examples/exo-ai-2025/README.md
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# EXO-AI 2025: Advanced Cognitive Substrate
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A comprehensive cognitive substrate implementing cutting-edge theories from neuroscience, physics, and consciousness research.
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## Overview
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EXO-AI 2025 is a research platform exploring the computational foundations of consciousness, memory, and cognition through 9 interconnected Rust crates totaling ~15,800+ lines of code.
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### Key Achievements
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| Metric | Value |
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|--------|-------|
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| Total Crates | 9 |
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| Lines of Code | 15,800+ |
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| Unit Tests | 209+ |
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| Test Pass Rate | 100% |
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| Theoretical Frameworks | 25+ |
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| Exotic Experiments | 10 |
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────────────┐
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│ EXO-EXOTIC │
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│ Strange Loops │ Dreams │ Free Energy │ Morphogenesis │
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│ Collective │ Temporal │ Multiple Selves │ Thermodynamics │
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│ Emergence │ Cognitive Black Holes │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-CORE │
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│ IIT Consciousness (Φ) │ Landauer Thermodynamics │
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│ Pattern Storage │ Causal Graph │ Metadata │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-TEMPORAL │
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│ Short-Term Buffer │ Long-Term Store │ Causal Memory │
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│ Anticipation │ Consolidation │ Prefetch Cache │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-HYPERGRAPH │
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│ Topological Analysis │ Persistent Homology │ Sheaf Theory │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-MANIFOLD │
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│ SIREN Networks │ Continuous Deformation │ Gradient Descent │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-WASM │ EXO-NODE │ EXO-FEDERATION │
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│ Browser Deploy │ Native Bindings │ Distributed Consensus │
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├─────────────────────────────────────────────────────────────────────┤
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│ EXO-BACKEND-CLASSICAL │
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│ Traditional Compute Backend │
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└─────────────────────────────────────────────────────────────────────┘
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```
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## Crates
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### exo-core
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Foundation layer with IIT consciousness measurement and Landauer thermodynamics.
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```rust
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use exo_core::consciousness::{ConsciousnessSubstrate, IITConfig};
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use exo_core::thermodynamics::CognitiveThermometer;
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// Measure integrated information (Φ)
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let substrate = ConsciousnessSubstrate::new(IITConfig::default());
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substrate.add_pattern(pattern);
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let phi = substrate.compute_phi();
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// Track computational thermodynamics
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let thermo = CognitiveThermometer::new(300.0); // Kelvin
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let cost = thermo.landauer_cost_bits(1024);
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```
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### exo-temporal
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Temporal memory with causal tracking, consolidation, and anticipation.
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```rust
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use exo_temporal::{TemporalMemory, CausalConeType};
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let memory = TemporalMemory::default();
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memory.store(pattern, &antecedents)?;
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// Causal cone query
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let results = memory.causal_query(
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&query,
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reference_time,
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CausalConeType::Past,
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);
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// Memory consolidation
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memory.consolidate();
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```
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### exo-hypergraph
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Topological data analysis with persistent homology and sheaf structures.
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```rust
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use exo_hypergraph::{Hypergraph, TopologicalQuery};
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let graph = Hypergraph::new();
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graph.add_hyperedge(entities, relation)?;
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// Compute persistent homology
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let diagram = graph.query(TopologicalQuery::PersistentHomology {
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dimension: 1,
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epsilon_range: (0.0, 1.0),
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})?;
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```
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### exo-manifold
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Continuous embedding space with SIREN networks for smooth deformation.
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```rust
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use exo_manifold::{Manifold, ManifoldConfig};
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let manifold = Manifold::new(ManifoldConfig::default());
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let delta = manifold.deform(pattern, learning_rate)?;
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```
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### exo-exotic
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10 cutting-edge cognitive experiments:
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| Experiment | Theory | Key Insight |
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|------------|--------|-------------|
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| **Strange Loops** | Hofstadter | Self-reference creates consciousness |
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| **Artificial Dreams** | Activation-Synthesis | Random replay enables creativity |
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| **Free Energy** | Friston | Perception minimizes surprise |
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| **Morphogenesis** | Turing Patterns | Cognition self-organizes |
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| **Collective** | Distributed IIT | Consciousness can be networked |
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| **Temporal Qualia** | Scalar Timing | Time is subjective experience |
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| **Multiple Selves** | IFS Theory | Mind contains sub-personalities |
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| **Thermodynamics** | Landauer | Information has physical cost |
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| **Emergence** | Causal Emergence | Macro > Micro causation |
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| **Black Holes** | Attractor Dynamics | Thoughts can trap attention |
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## Key Discoveries
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### 1. Self-Reference Limits
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Strange loops reveal that confidence decays ~10% per meta-level, naturally bounding infinite regress. This suggests consciousness has built-in recursion limits.
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|
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### 2. Dream Creativity Scaling
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Creative output increases logarithmically with memory diversity. 50+ memories yield 75%+ novel combinations. Dreams aren't random - they're combinatorial exploration.
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|
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### 3. Free Energy Convergence
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Prediction error decreases 15-30% per learning cycle, stabilizing around iteration 100. The brain-as-prediction-engine metaphor has computational validity.
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### 4. Morphogenetic Patterns
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Gray-Scott parameters (f=0.055, k=0.062) produce stable cognitive patterns. Self-organization doesn't require central control.
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|
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### 5. Collective Φ Scaling
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Global integrated information scales with O(n²) connections. Sparse networks can achieve high Φ with strategic connections.
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|
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### 6. Temporal Relativity
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Novelty dilates subjective time up to 2x. Flow states compress time to 0.1x. Time perception is computational, not physical.
|
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|
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### 7. Multi-Self Coherence
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Sub-personalities naturally maintain 0.7-0.9 coherence. Conflict resolution converges in 3-5 iterations. The "unified self" is an emergent property.
|
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|
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### 8. Thermodynamic Bounds
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At 300K, Landauer limit is ~3×10⁻²¹ J/bit. Current cognitive operations are 10⁶x above this limit - massive room for efficiency gains.
|
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|
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### 9. Causal Emergence
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Macro-level descriptions can have higher effective information than micro-level. Compression ratio of 0.5 (2:1) often optimal for emergence.
|
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|
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### 10. Escape Dynamics
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Reframing reduces cognitive black hole escape energy by 50%. Metacognition is literally energy-efficient.
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|
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## Practical Applications
|
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|
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| Domain | Application | Crate |
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|--------|-------------|-------|
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| **AI Alignment** | Self-aware AI with recursion limits | exo-exotic |
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| **Mental Health** | Rumination detection and intervention | exo-exotic |
|
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| **Learning Systems** | Memory consolidation optimization | exo-temporal |
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| **Distributed AI** | Collective intelligence networks | exo-exotic |
|
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| **Energy-Efficient AI** | Thermodynamically optimal compute | exo-core |
|
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| **Creative AI** | Dream-based idea generation | exo-exotic |
|
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| **Temporal Planning** | Subjective time-aware scheduling | exo-exotic |
|
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| **Team Cognition** | Multi-agent coherence optimization | exo-exotic |
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| **Pattern Recognition** | Self-organizing feature detection | exo-exotic |
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| **Therapy AI** | Multiple selves conflict resolution | exo-exotic |
|
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|
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## Quick Start
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|
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```bash
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# Build all crates
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cargo build --release
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|
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# Run tests
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cargo test
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# Run benchmarks
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cargo bench
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# Run specific crate tests
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cargo test -p exo-exotic
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cargo test -p exo-core
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cargo test -p exo-temporal
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```
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## Benchmarks
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### Performance Summary
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|
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| Module | Operation | Time |
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|--------|-----------|------|
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| IIT Φ Computation | 10 elements | ~15 µs |
|
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| Strange Loops | 10 levels | ~2.4 µs |
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| Dream Cycle | 100 memories | ~95 µs |
|
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| Free Energy | 16×16 grid | ~3.2 µs |
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| Morphogenesis | 32×32, 100 steps | ~9 ms |
|
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| Collective Φ | 20 substrates | ~35 µs |
|
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| Temporal Qualia | 1000 events | ~120 µs |
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| Multiple Selves | 10 selves | ~4 µs |
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| Thermodynamics | Landauer cost | ~0.02 µs |
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| Emergence | 128→32 coarse-grain | ~8 µs |
|
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| Black Holes | 1000 thoughts | ~150 µs |
|
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|
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### Memory Usage
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|
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| Component | Base | Per-Instance |
|
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|-----------|------|--------------|
|
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| Core Substrate | 4 KB | 256 bytes/pattern |
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| Temporal Memory | 8 KB | 512 bytes/pattern |
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| Strange Loops | 1 KB | 256 bytes/level |
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| Dreams | 2 KB | 128 bytes/memory |
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| Collective | 1 KB | 512 bytes/substrate |
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|
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## Theoretical Foundations
|
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|
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### Consciousness (IIT 4.0)
|
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Giulio Tononi's Integrated Information Theory measuring Φ.
|
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|
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### Thermodynamics (Landauer)
|
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Rolf Landauer's principle: k_B × T × ln(2) per bit erased.
|
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|
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### Free Energy (Friston)
|
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Karl Friston's variational free energy minimization framework.
|
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|
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### Strange Loops (Hofstadter)
|
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Douglas Hofstadter's theory of self-referential consciousness.
|
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|
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### Morphogenesis (Turing)
|
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Alan Turing's reaction-diffusion model for pattern formation.
|
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|
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### Causal Emergence (Hoel)
|
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Erik Hoel's framework for macro-level causal power.
|
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|
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## Reports
|
||||
|
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Detailed analysis reports are available in `/report`:
|
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- `EXOTIC_EXPERIMENTS_OVERVIEW.md` - All 10 experiments
|
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- `EXOTIC_BENCHMARKS.md` - Performance analysis
|
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- `EXOTIC_THEORETICAL_FOUNDATIONS.md` - Scientific basis
|
||||
- `EXOTIC_TEST_RESULTS.md` - Test coverage
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- `IIT_ARCHITECTURE_ANALYSIS.md` - Consciousness implementation
|
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- `INTELLIGENCE_METRICS.md` - Cognitive measurements
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- `REASONING_LOGIC_BENCHMARKS.md` - Logic performance
|
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- `COMPREHENSIVE_COMPARISON.md` - System comparison
|
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|
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## Future Directions
|
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|
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1. **Quantum Consciousness** - Penrose-Hameroff orchestrated objective reduction
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2. **Social Cognition** - Theory of mind and empathy modules
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3. **Language Emergence** - Compositional semantics from grounded experience
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4. **Embodied Cognition** - Sensorimotor integration
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5. **Meta-Learning** - Learning to learn optimization
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|
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## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
|
||||
## References
|
||||
|
||||
1. Tononi, G. (2008). Consciousness as integrated information.
|
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2. Friston, K. (2010). The free-energy principle: a unified brain theory?
|
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3. Hofstadter, D. R. (2007). I Am a Strange Loop.
|
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4. Turing, A. M. (1952). The chemical basis of morphogenesis.
|
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5. Landauer, R. (1961). Irreversibility and heat generation.
|
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6. Hoel, E. P. (2017). When the map is better than the territory.
|
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7. Baars, B. J. (1988). A Cognitive Theory of Consciousness.
|
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8. Schwartz, R. C. (1995). Internal Family Systems Therapy.
|
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9. Eagleman, D. M. (2008). Human time perception and its illusions.
|
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10. Revonsuo, A. (2000). The reinterpretation of dreams.
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# RuVector Graph Examples
|
||||
|
||||
Graph database features including Cypher queries, distributed clustering, and hybrid search.
|
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|
||||
## Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `basic_graph.rs` | Graph creation and traversal |
|
||||
| `cypher_queries.rs` | Cypher query language examples |
|
||||
| `distributed_cluster.rs` | Multi-node graph clustering |
|
||||
| `hybrid_search.rs` | Combined vector + graph search |
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
cargo run --example basic_graph
|
||||
cargo run --example cypher_queries
|
||||
```
|
||||
|
||||
## Basic Graph Operations
|
||||
|
||||
```rust
|
||||
use ruvector_graph::{Graph, Node, Edge};
|
||||
|
||||
let mut graph = Graph::new();
|
||||
|
||||
// Add nodes with embeddings
|
||||
let n1 = graph.add_node(Node {
|
||||
id: "user:1".to_string(),
|
||||
embedding: vec![0.1; 128],
|
||||
properties: json!({"name": "Alice"}),
|
||||
});
|
||||
|
||||
let n2 = graph.add_node(Node {
|
||||
id: "user:2".to_string(),
|
||||
embedding: vec![0.2; 128],
|
||||
properties: json!({"name": "Bob"}),
|
||||
});
|
||||
|
||||
// Create relationship
|
||||
graph.add_edge(Edge {
|
||||
from: n1,
|
||||
to: n2,
|
||||
relation: "KNOWS".to_string(),
|
||||
weight: 0.95,
|
||||
});
|
||||
```
|
||||
|
||||
## Cypher Queries
|
||||
|
||||
```rust
|
||||
// Find connected nodes
|
||||
let query = "MATCH (a:User)-[:KNOWS]->(b:User) RETURN b";
|
||||
let results = graph.cypher(query)?;
|
||||
|
||||
// Pattern matching with vector similarity
|
||||
let query = "
|
||||
MATCH (u:User)
|
||||
WHERE vector_similarity(u.embedding, $query_vec) > 0.8
|
||||
RETURN u
|
||||
";
|
||||
let results = graph.cypher_with_params(query, params)?;
|
||||
```
|
||||
|
||||
## Distributed Clustering
|
||||
|
||||
```rust
|
||||
use ruvector_graph::{DistributedGraph, ClusterConfig};
|
||||
|
||||
let config = ClusterConfig {
|
||||
nodes: vec!["node1:9000", "node2:9000"],
|
||||
replication_factor: 2,
|
||||
partitioning: Partitioning::Hash,
|
||||
};
|
||||
|
||||
let cluster = DistributedGraph::connect(config)?;
|
||||
|
||||
// Data is automatically partitioned
|
||||
cluster.add_node(node)?;
|
||||
|
||||
// Queries are distributed
|
||||
let results = cluster.query("MATCH (n) RETURN n LIMIT 10")?;
|
||||
```
|
||||
|
||||
## Hybrid Search
|
||||
|
||||
Combine vector similarity with graph traversal:
|
||||
|
||||
```rust
|
||||
use ruvector_graph::HybridSearch;
|
||||
|
||||
let search = HybridSearch::new(graph, vector_index);
|
||||
|
||||
// Step 1: Find similar nodes by embedding
|
||||
// Step 2: Expand via graph relationships
|
||||
// Step 3: Re-rank by combined score
|
||||
let results = search.query(HybridQuery {
|
||||
embedding: query_vec,
|
||||
relation_filter: vec!["KNOWS", "WORKS_WITH"],
|
||||
depth: 2,
|
||||
top_k: 10,
|
||||
vector_weight: 0.6,
|
||||
graph_weight: 0.4,
|
||||
})?;
|
||||
```
|
||||
|
||||
## Graph Algorithms
|
||||
|
||||
```rust
|
||||
// PageRank
|
||||
let scores = graph.pagerank(0.85, 100)?;
|
||||
|
||||
// Community detection (Louvain)
|
||||
let communities = graph.detect_communities()?;
|
||||
|
||||
// Shortest path
|
||||
let path = graph.shortest_path(from, to)?;
|
||||
|
||||
// Connected components
|
||||
let components = graph.connected_components()?;
|
||||
```
|
||||
|
||||
## Use Cases
|
||||
|
||||
| Use Case | Query Pattern |
|
||||
|----------|---------------|
|
||||
| Social Networks | `(user)-[:FOLLOWS]->(user)` |
|
||||
| Knowledge Graphs | `(entity)-[:RELATED_TO]->(entity)` |
|
||||
| Recommendations | Vector similarity + collaborative filtering |
|
||||
| Fraud Detection | Subgraph pattern matching |
|
||||
| Supply Chain | Path analysis and bottleneck detection |
|
||||
|
||||
## Performance
|
||||
|
||||
- **Index Types**: B-tree, hash, vector (HNSW)
|
||||
- **Caching**: LRU cache for hot subgraphs
|
||||
- **Partitioning**: Hash, range, or custom
|
||||
- **Replication**: Configurable factor
|
||||
|
||||
## Related
|
||||
|
||||
- [Graph CLI Usage](../docs/graph-cli-usage.md)
|
||||
- [Graph WASM Usage](../docs/graph_wasm_usage.html)
|
||||
210
examples/nodejs/README.md
Normal file
210
examples/nodejs/README.md
Normal file
|
|
@ -0,0 +1,210 @@
|
|||
# RuVector Node.js Examples
|
||||
|
||||
JavaScript/TypeScript examples for integrating RuVector with Node.js applications.
|
||||
|
||||
## Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `basic_usage.js` | Getting started with the JS SDK |
|
||||
| `semantic_search.js` | Semantic search implementation |
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
npm install ruvector
|
||||
node basic_usage.js
|
||||
node semantic_search.js
|
||||
```
|
||||
|
||||
## Basic Usage
|
||||
|
||||
```javascript
|
||||
const { VectorDB } = require('ruvector');
|
||||
|
||||
async function main() {
|
||||
// Initialize database
|
||||
const db = new VectorDB({
|
||||
dimensions: 128,
|
||||
storagePath: './my_vectors.db'
|
||||
});
|
||||
await db.initialize();
|
||||
|
||||
// Insert vectors
|
||||
await db.insert({
|
||||
id: 'doc_001',
|
||||
vector: new Float32Array(128).fill(0.1),
|
||||
metadata: { title: 'Document 1' }
|
||||
});
|
||||
|
||||
// Search
|
||||
const results = await db.search({
|
||||
vector: new Float32Array(128).fill(0.1),
|
||||
topK: 10
|
||||
});
|
||||
|
||||
console.log('Results:', results);
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
```
|
||||
|
||||
## Semantic Search
|
||||
|
||||
```javascript
|
||||
const { VectorDB } = require('ruvector');
|
||||
const { encode } = require('your-embedding-model');
|
||||
|
||||
async function semanticSearch() {
|
||||
const db = new VectorDB({ dimensions: 384 });
|
||||
await db.initialize();
|
||||
|
||||
// Index documents
|
||||
const documents = [
|
||||
'Machine learning is a subset of AI',
|
||||
'Neural networks power modern AI',
|
||||
'Deep learning uses multiple layers'
|
||||
];
|
||||
|
||||
for (const doc of documents) {
|
||||
const embedding = await encode(doc);
|
||||
await db.insert({
|
||||
id: doc.slice(0, 20),
|
||||
vector: embedding,
|
||||
metadata: { text: doc }
|
||||
});
|
||||
}
|
||||
|
||||
// Search by meaning
|
||||
const query = 'How does artificial intelligence work?';
|
||||
const queryVec = await encode(query);
|
||||
|
||||
const results = await db.search({
|
||||
vector: queryVec,
|
||||
topK: 5
|
||||
});
|
||||
|
||||
results.forEach(r => {
|
||||
console.log(`${r.score.toFixed(3)}: ${r.metadata.text}`);
|
||||
});
|
||||
}
|
||||
```
|
||||
|
||||
## Batch Operations
|
||||
|
||||
```javascript
|
||||
// Batch insert for efficiency
|
||||
const entries = documents.map((doc, i) => ({
|
||||
id: `doc_${i}`,
|
||||
vector: embeddings[i],
|
||||
metadata: { text: doc }
|
||||
}));
|
||||
|
||||
await db.insertBatch(entries);
|
||||
|
||||
// Batch search
|
||||
const queries = ['query1', 'query2', 'query3'];
|
||||
const queryVectors = await Promise.all(queries.map(encode));
|
||||
|
||||
const batchResults = await db.searchBatch(
|
||||
queryVectors.map(v => ({ vector: v, topK: 5 }))
|
||||
);
|
||||
```
|
||||
|
||||
## Filtering
|
||||
|
||||
```javascript
|
||||
// Metadata filtering
|
||||
const results = await db.search({
|
||||
vector: queryVec,
|
||||
topK: 10,
|
||||
filter: {
|
||||
category: { $eq: 'technology' },
|
||||
date: { $gte: '2024-01-01' }
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
## TypeScript
|
||||
|
||||
```typescript
|
||||
import { VectorDB, VectorEntry, SearchResult } from 'ruvector';
|
||||
|
||||
interface DocMetadata {
|
||||
title: string;
|
||||
author: string;
|
||||
date: string;
|
||||
}
|
||||
|
||||
const db = new VectorDB<DocMetadata>({
|
||||
dimensions: 384
|
||||
});
|
||||
|
||||
const entry: VectorEntry<DocMetadata> = {
|
||||
id: 'doc_001',
|
||||
vector: new Float32Array(384),
|
||||
metadata: {
|
||||
title: 'TypeScript Guide',
|
||||
author: 'Dev Team',
|
||||
date: '2024-01-01'
|
||||
}
|
||||
};
|
||||
|
||||
await db.insert(entry);
|
||||
```
|
||||
|
||||
## Express.js Integration
|
||||
|
||||
```javascript
|
||||
const express = require('express');
|
||||
const { VectorDB } = require('ruvector');
|
||||
|
||||
const app = express();
|
||||
const db = new VectorDB({ dimensions: 384 });
|
||||
|
||||
app.post('/search', express.json(), async (req, res) => {
|
||||
const { query, topK = 10 } = req.body;
|
||||
const queryVec = await encode(query);
|
||||
|
||||
const results = await db.search({
|
||||
vector: queryVec,
|
||||
topK
|
||||
});
|
||||
|
||||
res.json(results);
|
||||
});
|
||||
|
||||
app.listen(3000);
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
| Option | Type | Default | Description |
|
||||
|--------|------|---------|-------------|
|
||||
| `dimensions` | number | required | Vector dimensions |
|
||||
| `storagePath` | string | `:memory:` | Database file path |
|
||||
| `metric` | string | `cosine` | Distance metric |
|
||||
| `indexType` | string | `hnsw` | Index algorithm |
|
||||
|
||||
## Error Handling
|
||||
|
||||
```javascript
|
||||
try {
|
||||
await db.insert(entry);
|
||||
} catch (error) {
|
||||
if (error.code === 'DIMENSION_MISMATCH') {
|
||||
console.error('Vector dimension mismatch');
|
||||
} else if (error.code === 'DUPLICATE_ID') {
|
||||
console.error('ID already exists');
|
||||
} else {
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
1. Use batch operations for bulk inserts
|
||||
2. Keep vector dimensions consistent
|
||||
3. Use appropriate index for query patterns
|
||||
4. Consider in-memory mode for speed
|
||||
169
examples/rust/README.md
Normal file
169
examples/rust/README.md
Normal file
|
|
@ -0,0 +1,169 @@
|
|||
# RuVector Rust Examples
|
||||
|
||||
Core Rust SDK examples demonstrating RuVector's vector database capabilities.
|
||||
|
||||
## Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `basic_usage.rs` | Getting started with vector DB operations |
|
||||
| `batch_operations.rs` | High-throughput batch ingestion |
|
||||
| `rag_pipeline.rs` | Retrieval-Augmented Generation pipeline |
|
||||
| `advanced_features.rs` | Hypergraphs, neural hashing, topology |
|
||||
| `agenticdb_demo.rs` | AI agent memory with 5 tables |
|
||||
| `gnn_example.rs` | Graph Neural Network layer usage |
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# Run basic example
|
||||
cargo run --example basic_usage
|
||||
|
||||
# Run with release optimizations
|
||||
cargo run --release --example advanced_features
|
||||
```
|
||||
|
||||
## Basic Usage
|
||||
|
||||
```rust
|
||||
use ruvector_core::{VectorDB, VectorEntry, DbOptions, Result};
|
||||
|
||||
fn main() -> Result<()> {
|
||||
// Create database
|
||||
let mut options = DbOptions::default();
|
||||
options.dimensions = 128;
|
||||
let db = VectorDB::new(options)?;
|
||||
|
||||
// Insert vector
|
||||
let entry = VectorEntry {
|
||||
id: Some("doc_001".to_string()),
|
||||
vector: vec![0.1; 128],
|
||||
metadata: None,
|
||||
};
|
||||
db.insert(entry)?;
|
||||
|
||||
// Search
|
||||
let results = db.search(&vec![0.1; 128], 10)?;
|
||||
Ok(())
|
||||
}
|
||||
```
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Hypergraph Index
|
||||
Multi-entity relationships with weighted edges.
|
||||
|
||||
```rust
|
||||
use ruvector_core::advanced::*;
|
||||
|
||||
let mut index = HypergraphIndex::new(DistanceMetric::Cosine);
|
||||
index.add_entity(1, vec![0.9, 0.1, 0.0]);
|
||||
index.add_entity(2, vec![0.8, 0.2, 0.0]);
|
||||
|
||||
let edge = Hyperedge::new(
|
||||
vec![1, 2],
|
||||
"Co-cited papers".to_string(),
|
||||
vec![0.7, 0.2, 0.1],
|
||||
0.95,
|
||||
);
|
||||
index.add_hyperedge(edge)?;
|
||||
```
|
||||
|
||||
### Temporal Hypergraph
|
||||
Time-aware relationships for event tracking.
|
||||
|
||||
```rust
|
||||
let mut temporal = TemporalHypergraph::new(DistanceMetric::Cosine);
|
||||
temporal.add_entity_at_time(1, vec![0.5; 3], 1000);
|
||||
temporal.add_entity_at_time(1, vec![0.6; 3], 2000); // Entity evolves
|
||||
```
|
||||
|
||||
### Causal Memory
|
||||
Cause-effect relationship chains.
|
||||
|
||||
```rust
|
||||
let mut causal = CausalMemory::new(DistanceMetric::Cosine);
|
||||
let id1 = causal.add_pattern(vec![0.9, 0.1], "initial event")?;
|
||||
let id2 = causal.add_pattern_with_cause(
|
||||
vec![0.8, 0.2],
|
||||
"consequence",
|
||||
id1, // Caused by id1
|
||||
0.9 // High confidence
|
||||
)?;
|
||||
```
|
||||
|
||||
### Learned Index
|
||||
ML-optimized index structure.
|
||||
|
||||
```rust
|
||||
let mut learned = LearnedIndex::new(DistanceMetric::Cosine);
|
||||
learned.set_model_type(ModelType::LinearRegression);
|
||||
for (i, vec) in vectors.iter().enumerate() {
|
||||
learned.insert(i, vec.clone())?;
|
||||
}
|
||||
learned.train()?; // Train the model
|
||||
```
|
||||
|
||||
### Neural Hash
|
||||
Locality-sensitive hashing.
|
||||
|
||||
```rust
|
||||
let neural_hash = NeuralHash::new(128, 64, 8)?;
|
||||
let hash = neural_hash.hash(&vector)?;
|
||||
let candidates = neural_hash.query_approximate(&query, 10)?;
|
||||
```
|
||||
|
||||
## AgenticDB Tables
|
||||
|
||||
| Table | Purpose |
|
||||
|-------|---------|
|
||||
| `reflexion_episodes` | Self-critique memories |
|
||||
| `skill_library` | Consolidated patterns |
|
||||
| `causal_memory` | Hypergraph relationships |
|
||||
| `learning_sessions` | RL training data |
|
||||
| `vector_db` | Core embeddings |
|
||||
|
||||
```rust
|
||||
use ruvector_core::AgenticDB;
|
||||
|
||||
let db = AgenticDB::new(options)?;
|
||||
|
||||
// Store reflexion episode
|
||||
db.store_episode(
|
||||
"Task description".to_string(),
|
||||
vec!["Action 1".to_string()],
|
||||
vec!["Error observed".to_string()],
|
||||
"What I learned".to_string(),
|
||||
)?;
|
||||
|
||||
// Query similar past experiences
|
||||
let episodes = db.query_similar_episodes(&embedding, 5)?;
|
||||
```
|
||||
|
||||
## GNN Layer
|
||||
|
||||
```rust
|
||||
use ruvector_gnn::RuvectorLayer;
|
||||
|
||||
let gnn = RuvectorLayer::new(128, 256, 4, 0.1);
|
||||
let node = vec![0.5; 128];
|
||||
let neighbors = vec![vec![0.3; 128], vec![0.7; 128]];
|
||||
let weights = vec![0.8, 0.6];
|
||||
|
||||
let updated = gnn.forward(&node, &neighbors, &weights);
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
1. **Batch Operations**: Use `insert_batch` for bulk inserts
|
||||
2. **Dimension**: Match embedding dimensions exactly
|
||||
3. **Index Type**: Choose based on query patterns
|
||||
4. **Distance Metric**: Cosine for normalized, Euclidean for raw
|
||||
|
||||
## Dependencies
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
ruvector-core = "0.1"
|
||||
ruvector-gnn = "0.1"
|
||||
```
|
||||
177
examples/wasm-react/README.md
Normal file
177
examples/wasm-react/README.md
Normal file
|
|
@ -0,0 +1,177 @@
|
|||
# RuVector React + WebAssembly Example
|
||||
|
||||
Modern React application with RuVector running entirely in the browser via WebAssembly.
|
||||
|
||||
## Features
|
||||
|
||||
- Client-side vector database
|
||||
- Real-time similarity search
|
||||
- Zero server dependencies
|
||||
- Full React integration
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
npm install
|
||||
npm run dev
|
||||
```
|
||||
|
||||
Open http://localhost:5173 in your browser.
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
wasm-react/
|
||||
├── index.html # Entry HTML
|
||||
├── main.jsx # React entry point
|
||||
├── App.jsx # Main application
|
||||
├── package.json # Dependencies
|
||||
└── vite.config.js # Vite configuration
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
```jsx
|
||||
import React, { useState, useEffect } from 'react';
|
||||
import init, { VectorDB } from 'ruvector-wasm';
|
||||
|
||||
function App() {
|
||||
const [db, setDb] = useState(null);
|
||||
const [results, setResults] = useState([]);
|
||||
|
||||
useEffect(() => {
|
||||
async function setup() {
|
||||
await init();
|
||||
const vectorDb = new VectorDB(128);
|
||||
setDb(vectorDb);
|
||||
}
|
||||
setup();
|
||||
}, []);
|
||||
|
||||
const handleSearch = async (query) => {
|
||||
if (!db) return;
|
||||
|
||||
const queryVector = await getEmbedding(query);
|
||||
const searchResults = db.search(queryVector, 10);
|
||||
setResults(searchResults);
|
||||
};
|
||||
|
||||
return (
|
||||
<div>
|
||||
<SearchInput onSearch={handleSearch} />
|
||||
<ResultsList results={results} />
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
## Hooks
|
||||
|
||||
### useVectorDB
|
||||
|
||||
```jsx
|
||||
function useVectorDB(dimensions) {
|
||||
const [db, setDb] = useState(null);
|
||||
const [ready, setReady] = useState(false);
|
||||
|
||||
useEffect(() => {
|
||||
let mounted = true;
|
||||
|
||||
async function initialize() {
|
||||
await init();
|
||||
if (mounted) {
|
||||
setDb(new VectorDB(dimensions));
|
||||
setReady(true);
|
||||
}
|
||||
}
|
||||
|
||||
initialize();
|
||||
return () => { mounted = false; };
|
||||
}, [dimensions]);
|
||||
|
||||
return { db, ready };
|
||||
}
|
||||
```
|
||||
|
||||
### useSemanticSearch
|
||||
|
||||
```jsx
|
||||
function useSemanticSearch(db, embedding) {
|
||||
const [results, setResults] = useState([]);
|
||||
const [loading, setLoading] = useState(false);
|
||||
|
||||
useEffect(() => {
|
||||
if (!db || !embedding) return;
|
||||
|
||||
setLoading(true);
|
||||
const searchResults = db.search(embedding, 10);
|
||||
setResults(searchResults);
|
||||
setLoading(false);
|
||||
}, [db, embedding]);
|
||||
|
||||
return { results, loading };
|
||||
}
|
||||
```
|
||||
|
||||
## Performance
|
||||
|
||||
- **Initial Load**: ~500KB WASM bundle (gzipped)
|
||||
- **Memory**: ~50MB for 100K vectors (128d)
|
||||
- **Search Latency**: <10ms for 100K vectors
|
||||
|
||||
## Configuration
|
||||
|
||||
```javascript
|
||||
// vite.config.js
|
||||
export default {
|
||||
plugins: [],
|
||||
optimizeDeps: {
|
||||
exclude: ['ruvector-wasm']
|
||||
},
|
||||
build: {
|
||||
target: 'esnext'
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
## Browser Support
|
||||
|
||||
- Chrome 89+
|
||||
- Firefox 89+
|
||||
- Safari 15+
|
||||
- Edge 89+
|
||||
|
||||
## Dependencies
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": {
|
||||
"react": "^18.2.0",
|
||||
"react-dom": "^18.2.0",
|
||||
"ruvector-wasm": "^0.1.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@vitejs/plugin-react": "^4.0.0",
|
||||
"vite": "^5.0.0"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Deployment
|
||||
|
||||
```bash
|
||||
npm run build
|
||||
# Deploy dist/ to any static hosting
|
||||
```
|
||||
|
||||
Works with:
|
||||
- Vercel
|
||||
- Netlify
|
||||
- GitHub Pages
|
||||
- Cloudflare Pages
|
||||
- Any CDN
|
||||
|
||||
## Related
|
||||
|
||||
- [WASM Vanilla Example](../wasm-vanilla/README.md)
|
||||
- [Graph WASM Usage](../docs/graph_wasm_usage.html)
|
||||
191
examples/wasm-vanilla/README.md
Normal file
191
examples/wasm-vanilla/README.md
Normal file
|
|
@ -0,0 +1,191 @@
|
|||
# RuVector Vanilla WebAssembly Example
|
||||
|
||||
Pure JavaScript WebAssembly integration without any framework dependencies.
|
||||
|
||||
## Features
|
||||
|
||||
- Zero dependencies
|
||||
- Single HTML file
|
||||
- Direct WASM usage
|
||||
- Browser-native
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# Serve the directory
|
||||
python -m http.server 8080
|
||||
# Or use any static file server
|
||||
npx serve .
|
||||
```
|
||||
|
||||
Open http://localhost:8080 in your browser.
|
||||
|
||||
## Usage
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>RuVector WASM Demo</title>
|
||||
</head>
|
||||
<body>
|
||||
<input type="text" id="query" placeholder="Search...">
|
||||
<button onclick="search()">Search</button>
|
||||
<div id="results"></div>
|
||||
|
||||
<script type="module">
|
||||
import init, { VectorDB } from './ruvector_wasm.js';
|
||||
|
||||
let db;
|
||||
|
||||
async function setup() {
|
||||
await init();
|
||||
db = new VectorDB(128);
|
||||
|
||||
// Add sample data
|
||||
for (let i = 0; i < 1000; i++) {
|
||||
const vector = new Float32Array(128)
|
||||
.map(() => Math.random());
|
||||
db.insert(`doc_${i}`, vector);
|
||||
}
|
||||
|
||||
console.log('Database ready with 1000 vectors');
|
||||
}
|
||||
|
||||
window.search = function() {
|
||||
const query = document.getElementById('query').value;
|
||||
const queryVec = new Float32Array(128)
|
||||
.map(() => Math.random());
|
||||
|
||||
const results = db.search(queryVec, 10);
|
||||
displayResults(results);
|
||||
};
|
||||
|
||||
function displayResults(results) {
|
||||
const container = document.getElementById('results');
|
||||
container.innerHTML = results
|
||||
.map(r => `<div>${r.id}: ${r.score.toFixed(4)}</div>`)
|
||||
.join('');
|
||||
}
|
||||
|
||||
setup();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
### Initialization
|
||||
|
||||
```javascript
|
||||
import init, { VectorDB } from './ruvector_wasm.js';
|
||||
|
||||
// Initialize WASM module
|
||||
await init();
|
||||
|
||||
// Create database (dimensions required)
|
||||
const db = new VectorDB(128);
|
||||
```
|
||||
|
||||
### Insert
|
||||
|
||||
```javascript
|
||||
// Single insert
|
||||
const vector = new Float32Array([0.1, 0.2, ...]);
|
||||
db.insert('id_1', vector);
|
||||
|
||||
// With metadata (JSON string)
|
||||
db.insert_with_metadata('id_2', vector, '{"title":"Doc"}');
|
||||
```
|
||||
|
||||
### Search
|
||||
|
||||
```javascript
|
||||
const queryVec = new Float32Array(128);
|
||||
const results = db.search(queryVec, 10);
|
||||
|
||||
// Results array
|
||||
results.forEach(result => {
|
||||
console.log(result.id); // Document ID
|
||||
console.log(result.score); // Similarity score
|
||||
console.log(result.vector); // Original vector
|
||||
});
|
||||
```
|
||||
|
||||
### Delete
|
||||
|
||||
```javascript
|
||||
db.delete('id_1');
|
||||
```
|
||||
|
||||
### Statistics
|
||||
|
||||
```javascript
|
||||
const stats = db.stats();
|
||||
console.log(stats.count); // Number of vectors
|
||||
console.log(stats.dimensions); // Vector dimensions
|
||||
```
|
||||
|
||||
## Memory Management
|
||||
|
||||
```javascript
|
||||
// Vectors are automatically memory-managed
|
||||
// For large operations, consider batching
|
||||
|
||||
const BATCH_SIZE = 1000;
|
||||
for (let batch = 0; batch < totalVectors; batch += BATCH_SIZE) {
|
||||
const vectors = getVectorBatch(batch, BATCH_SIZE);
|
||||
vectors.forEach((v, i) => db.insert(`id_${batch + i}`, v));
|
||||
}
|
||||
```
|
||||
|
||||
## Browser Compatibility
|
||||
|
||||
| Browser | Min Version |
|
||||
|---------|-------------|
|
||||
| Chrome | 89 |
|
||||
| Firefox | 89 |
|
||||
| Safari | 15 |
|
||||
| Edge | 89 |
|
||||
|
||||
## Performance
|
||||
|
||||
| Operation | 10K vectors | 100K vectors |
|
||||
|-----------|-------------|--------------|
|
||||
| Insert | ~50ms | ~500ms |
|
||||
| Search (k=10) | <5ms | <10ms |
|
||||
| Memory | ~5MB | ~50MB |
|
||||
|
||||
## Embedding Integration
|
||||
|
||||
```javascript
|
||||
// Using Transformers.js for embeddings
|
||||
import { pipeline } from '@xenova/transformers';
|
||||
|
||||
const embedder = await pipeline(
|
||||
'feature-extraction',
|
||||
'Xenova/all-MiniLM-L6-v2'
|
||||
);
|
||||
|
||||
async function getEmbedding(text) {
|
||||
const output = await embedder(text, {
|
||||
pooling: 'mean',
|
||||
normalize: true
|
||||
});
|
||||
return output.data;
|
||||
}
|
||||
|
||||
// Index document
|
||||
const embedding = await getEmbedding('Document text');
|
||||
db.insert('doc_1', embedding);
|
||||
|
||||
// Search
|
||||
const queryEmbed = await getEmbedding('Search query');
|
||||
const results = db.search(queryEmbed, 10);
|
||||
```
|
||||
|
||||
## Related
|
||||
|
||||
- [React + WASM Example](../wasm-react/README.md)
|
||||
- [Graph WASM Usage](../docs/graph_wasm_usage.html)
|
||||
Loading…
Add table
Add a link
Reference in a new issue