230 lines
7.8 KiB
Markdown
230 lines
7.8 KiB
Markdown
# AgentDB Simulation Documentation
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**Version**: 2.0.0
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**Last Updated**: 2025-11-30
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Welcome to the comprehensive documentation for AgentDB's latent space simulation system. This suite enables you to benchmark, validate, and optimize vector database configurations using real-world scenarios.
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---
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## 📚 Quick Navigation
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### 🚀 Getting Started
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- **[Quick Start Guide](guides/QUICK-START.md)** - Get up and running in 5 minutes
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- **[CLI Reference](guides/CLI-REFERENCE.md)** - Complete command-line documentation
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- **[Interactive Wizard Guide](guides/WIZARD-GUIDE.md)** - Using the wizard interface
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### 🔧 Advanced Usage
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- **[Custom Simulations](guides/CUSTOM-SIMULATIONS.md)** - Build custom scenarios from components
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- **[Troubleshooting](guides/TROUBLESHOOTING.md)** - Common issues and solutions
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### 🏗️ Architecture & Implementation
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- **[Simulation Architecture](architecture/SIMULATION-ARCHITECTURE.md)** - TypeScript implementation details
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- **[Optimization Strategy](architecture/OPTIMIZATION-STRATEGY.md)** - Performance tuning guide
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- **[CLI Integration Plan](CLI-INTEGRATION-PLAN.md)** - Development roadmap
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### 📊 Research & Results
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- **[Latent Space Reports](reports/latent-space/README.md)** - Executive summary of findings
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- **[Master Synthesis](reports/latent-space/MASTER-SYNTHESIS.md)** - Cross-simulation analysis
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- **Individual Reports**: 8 detailed simulation results
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---
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## 🎯 What's New in v2.0
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### Headline Features
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- **8.2x Speedup**: RuVector achieves 61μs search latency (vs 498μs baseline)
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- **97.9% Self-Healing**: Autonomous adaptation prevents performance degradation
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- **29.4% Neural Boost**: Full neural pipeline enhancement validated
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- **Interactive CLI**: Wizard-driven simulation creation
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- **Custom Builder**: Compose simulations from discovered optimal components
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### Key Optimizations Discovered
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| Component | Optimal Value | Impact |
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|-----------|---------------|--------|
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| **Backend** | RuVector | 8.2x speedup |
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| **Attention Heads** | 8 heads | +12.4% recall |
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| **Search Strategy** | Beam-5 + Dynamic-k | 96.8% recall, -18.4% latency |
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| **Clustering** | Louvain | Q=0.758 modularity |
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| **Self-Healing** | MPC | 97.9% uptime |
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| **Neural Pipeline** | Full stack | +29.4% improvement |
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---
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## 📖 Documentation Structure
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```
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docs/
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├── README.md (this file) # Documentation index
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├── CLI-INTEGRATION-PLAN.md # Implementation roadmap
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├── guides/ # User guides
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│ ├── README.md # Scenario overview
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│ ├── QUICK-START.md # 5-minute guide
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│ ├── CUSTOM-SIMULATIONS.md # Component reference
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│ ├── WIZARD-GUIDE.md # Interactive wizard
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│ ├── CLI-REFERENCE.md # Complete CLI docs
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│ └── TROUBLESHOOTING.md # Common issues
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├── architecture/ # Technical docs
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│ ├── SIMULATION-ARCHITECTURE.md # TypeScript design
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│ └── OPTIMIZATION-STRATEGY.md # Performance tuning
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└── reports/ # Simulation results
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└── latent-space/ # 8 simulation reports
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├── README.md # Executive summary
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├── MASTER-SYNTHESIS.md # Cross-analysis
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└── [8 individual reports].md
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```
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---
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## 🚀 Quick Start (TL;DR)
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```bash
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# Install AgentDB
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npm install -g agentdb
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# Run interactive wizard
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agentdb simulate --wizard
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# Run validated scenario
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agentdb simulate hnsw --iterations 3
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# Build custom simulation
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agentdb simulate --custom \
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--backend ruvector \
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--attention-heads 8 \
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--search beam 5 \
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--cluster louvain \
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--self-healing mpc
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# View past results
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agentdb simulate --report latest
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```
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**👉 [See detailed quick start guide →](guides/QUICK-START.md)**
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---
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## 🎓 Learning Path
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### 1️⃣ Beginners
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Start here if you're new to vector databases or AgentDB:
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1. Read [Quick Start Guide](guides/QUICK-START.md)
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2. Run your first simulation with `agentdb simulate --wizard`
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3. Explore [Latent Space Reports](reports/latent-space/README.md) to understand findings
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### 2️⃣ Developers
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For those building with AgentDB:
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1. Review [Custom Simulations Guide](guides/CUSTOM-SIMULATIONS.md)
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2. Understand [Optimization Strategy](architecture/OPTIMIZATION-STRATEGY.md)
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3. Check [CLI Reference](guides/CLI-REFERENCE.md) for all options
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4. Read [Simulation Architecture](architecture/SIMULATION-ARCHITECTURE.md) for extension points
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### 3️⃣ Researchers
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For performance optimization and research:
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1. Study [Master Synthesis Report](reports/latent-space/MASTER-SYNTHESIS.md)
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2. Review all [8 individual simulation reports](reports/latent-space/)
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3. Read [Optimization Strategy](architecture/OPTIMIZATION-STRATEGY.md)
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4. Explore custom component combinations in [Custom Simulations](guides/CUSTOM-SIMULATIONS.md)
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---
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## 📊 Key Findings Summary
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### Performance Benchmarks (100K vectors, 384d)
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- **Latency**: 61μs (8.2x faster than hnswlib baseline)
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- **Recall@10**: 96.8% (beam-5 search)
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- **Memory**: 151MB (-18% with GNN edges)
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- **QPS**: 12,182 (vs 2,007 baseline)
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### Long-Term Stability (30-day simulation)
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- **Static database**: +95.3% latency degradation ⚠️
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- **Self-organizing**: +2.1% degradation ✅
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- **Prevention rate**: 97.9% of performance loss avoided
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### Neural Enhancements
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- **GNN Attention (8-head)**: +12.4% recall, +5.5% latency
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- **RL Navigation**: -13.6% latency, +4.2% recall
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- **Full Neural Stack**: +29.4% overall improvement
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**👉 [See complete analysis →](reports/latent-space/MASTER-SYNTHESIS.md)**
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---
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## 🛠️ CLI Commands Overview
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```bash
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# Scenario Execution
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agentdb simulate hnsw # HNSW graph topology
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agentdb simulate attention # Multi-head attention
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agentdb simulate clustering # Community detection
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agentdb simulate traversal # Search optimization
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agentdb simulate hypergraph # Multi-agent collaboration
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agentdb simulate self-organizing # Autonomous adaptation
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agentdb simulate neural # Neural augmentation
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agentdb simulate quantum # Theoretical analysis
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# Interactive Modes
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agentdb simulate --wizard # Step-by-step builder
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agentdb simulate --custom # Component composer
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# Reporting
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agentdb simulate --list # List scenarios
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agentdb simulate --report [id] # View results
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```
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**👉 [See complete CLI reference →](guides/CLI-REFERENCE.md)**
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---
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## 🤝 Contributing
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We welcome contributions to:
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- Add new simulation scenarios
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- Improve optimization algorithms
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- Extend neural components
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- Enhance documentation
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### Adding Custom Scenarios
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See [Simulation Architecture](architecture/SIMULATION-ARCHITECTURE.md) for extension points and examples.
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### Reporting Issues
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- Check [Troubleshooting Guide](guides/TROUBLESHOOTING.md) first
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- Open issues on GitHub with reproduction steps
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- Include CLI version and configuration
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---
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## 📞 Support & Resources
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### Documentation
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- **This site**: Complete documentation suite
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- **CLI Help**: `agentdb simulate --help`
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- **Scenario Help**: `agentdb simulate [scenario] --help`
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### Community
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- **GitHub**: [ruvnet/agentic-flow](https://github.com/ruvnet/agentic-flow)
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- **Issues**: [Report bugs](https://github.com/ruvnet/agentic-flow/issues)
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- **Discussions**: [Ask questions](https://github.com/ruvnet/agentic-flow/discussions)
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### Citation
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If you use AgentDB simulations in research, please cite:
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```bibtex
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@software{agentdb2025,
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title = {AgentDB: Production-Ready Vector Database with Neural Enhancements},
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author = {RuvNet},
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year = {2025},
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version = {2.0.0},
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url = {https://github.com/ruvnet/agentic-flow}
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}
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```
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---
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## 📜 License
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MIT License - See project root for details.
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---
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**Ready to explore?** Start with the **[Quick Start Guide →](guides/QUICK-START.md)**
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