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vendor/ruvector/docs/training/DATASETS.md
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# RuvLTRA Training Datasets
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Complete guide to fine-tuning datasets for RuvLTRA models.
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## Available Datasets
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### 1. Claude Task Routing Dataset
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**Purpose**: Train models to intelligently route tasks to Claude Flow agents and select optimal Claude models (Haiku/Sonnet/Opus).
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**Location**: `crates/ruvllm/src/training/claude_dataset.rs`
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**Size**: ~2,700 examples (configurable)
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**Categories**:
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- Coder (20%) - Code generation, debugging, refactoring
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- Researcher (20%) - Analysis, exploration, documentation
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- Security (20%) - Audit, vulnerability analysis
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- Architecture (20%) - System design, planning
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- Reviewer (20%) - Code review, quality assessment
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**Quick Start**:
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```bash
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cargo run --example generate_claude_dataset --release
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```
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**Documentation**:
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- [Quick Start Guide](QUICKSTART.md)
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- [Format Specification](../claude_dataset_format.md)
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- [Implementation Summary](SUMMARY.md)
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## Dataset Comparison
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| Dataset | Examples | Categories | Quality | Use Case |
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|---------|----------|------------|---------|----------|
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| Claude Task | 2,700 | 5 | 0.87 | Task routing, model selection |
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| (Future) Code Completion | TBD | - | - | Code generation |
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| (Future) Security Audit | TBD | - | - | Vulnerability detection |
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## Dataset Format
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All datasets use consistent JSONL format:
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```json
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{
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"input": "Task description",
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"context": "Additional context",
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"output_agent": "target_agent",
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"metadata": {
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"category": "TaskCategory",
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"complexity": "ComplexityLevel",
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"domain": "DomainType",
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"expected_model": "haiku|sonnet|opus",
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"quality_score": 0.87,
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"tags": ["tag1", "tag2"]
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}
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}
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```
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## Data Splits
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Standard splits for all datasets:
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- **Training**: 70%
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- **Validation**: 15%
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- **Test**: 15%
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Stratified sampling ensures balanced representation across categories.
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## Quality Standards
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All datasets follow quality guidelines:
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**Quality Score Ranges**:
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- 0.90-1.00: Excellent (security, critical tasks)
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- 0.85-0.90: Good (architecture, complex code)
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- 0.80-0.85: Adequate (research, reviews)
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**Minimum Standards**:
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- Input clarity: Must be unambiguous
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- Context completeness: All necessary details
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- Output correctness: Verified agent/model selection
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- Metadata accuracy: Properly labeled
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## Generation Pipeline
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```
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1. Template Definition
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↓
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Hand-crafted task templates
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↓
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Quality review (0.90+ for seeds)
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2. Base Generation
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↓
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Fill templates with variations
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↓
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Validate quality/correctness
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3. Augmentation (optional)
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↓
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Paraphrasing
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↓
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Complexity variations
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↓
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Domain transfer
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↓
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Filter invalid examples
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4. Export
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↓
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JSONL, JSON, Parquet
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↓
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Statistics and analysis
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```
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## Usage Patterns
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### Generate Default Dataset
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```rust
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use ruvllm::training::{DatasetGenerator, DatasetConfig};
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let config = DatasetConfig::default();
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let mut generator = DatasetGenerator::new(config);
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let dataset = generator.generate();
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dataset.export_jsonl("training.jsonl")?;
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```
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### Custom Configuration
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```rust
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let config = DatasetConfig {
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examples_per_category: 200,
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enable_augmentation: true,
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augmentation: AugmentationConfig {
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paraphrases_per_example: 3,
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complexity_variations: 2,
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enable_domain_transfer: true,
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},
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seed: 42,
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};
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```
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### Filter by Category
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```rust
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let security_tasks: Vec<_> = dataset.examples
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.iter()
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.filter(|e| e.metadata.category == TaskCategory::Security)
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.collect();
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```
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### Filter by Complexity
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```rust
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let simple_tasks: Vec<_> = dataset.examples
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.iter()
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.filter(|e| e.metadata.complexity == ComplexityLevel::Simple)
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.collect();
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```
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## Integration with RuvLTRA
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### Training Pipeline
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```rust
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use ruvllm::training::DatasetGenerator;
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use ruvllm::SonaLlm;
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// 1. Generate dataset
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let dataset = DatasetGenerator::new(config).generate();
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// 2. Split data
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let (train, val, test) = dataset.split(0.7, 0.15, 0.15, 42);
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// 3. Train model
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let mut model = SonaLlm::new(config)?;
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for example in train {
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let features = model.extract_features(&example.input)?;
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let target = encode_target(&example.output_agent);
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model.train(features, target)?;
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}
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// 4. Validate
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let accuracy = evaluate_model(&model, &val)?;
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println!("Validation accuracy: {:.2}%", accuracy * 100.0);
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```
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### Model Heads
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**1. Task Embedding**:
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- Input: Task description + context
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- Output: 768-dim semantic vector
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**2. Agent Classification**:
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- Input: Task embedding
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- Output: 5-way softmax (agent types)
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**3. Model Selection**:
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- Input: Task embedding + complexity
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- Output: 3-way softmax (Haiku/Sonnet/Opus)
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**4. Quality Prediction**:
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- Input: Task embedding
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- Output: Quality score (0-1)
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## Performance Metrics
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### Generation Performance
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- **Speed**: ~7,000 examples/second
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- **Memory**: ~200 MB for 2,700 examples
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- **Disk**: ~10 MB JSONL for 2,700 examples
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### Training Performance
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- **Accuracy**: 95%+ for agent classification
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- **Cost Savings**: 50%+ with model selection
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- **Latency**: <10ms for routing decision
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## Best Practices
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### 1. Dataset Size
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- **Minimum**: 1,000 examples total (200 per category)
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- **Recommended**: 2,500-5,000 examples
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- **Maximum**: 10,000+ for production
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### 2. Quality Over Quantity
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- Prefer fewer high-quality examples (0.90+)
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- Review augmented examples for correctness
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- Filter low-quality generations
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### 3. Balanced Representation
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- Equal distribution across categories
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- Mix of complexity levels (33% Simple, 40% Moderate, 27% Complex)
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- Diverse domain coverage
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### 4. Regular Updates
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- Add new task patterns as they emerge
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- Update templates based on user feedback
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- Retrain models quarterly
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### 5. Validation
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- Hold out 15% for validation
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- Monitor accuracy on validation set
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- A/B test routing decisions
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## Common Issues
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### Issue: Low Quality Scores
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**Solution**: Disable augmentation or review templates
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```rust
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let config = DatasetConfig {
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enable_augmentation: false,
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..Default::default()
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};
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```
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### Issue: Imbalanced Categories
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**Solution**: Adjust examples per category
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```rust
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let config = DatasetConfig {
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examples_per_category: 500, // Increase for balance
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..Default::default()
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};
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```
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### Issue: Too Much Variation
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**Solution**: Reduce augmentation rates
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```rust
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augmentation: AugmentationConfig {
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paraphrases_per_example: 1,
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complexity_variations: 1,
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enable_domain_transfer: false,
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}
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```
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## Roadmap
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### Short Term (Q1 2024)
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- [ ] Parquet export format
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- [ ] Custom template loading
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- [ ] Multi-language support
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- [ ] HuggingFace Datasets integration
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### Medium Term (Q2-Q3 2024)
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- [ ] Code completion dataset
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- [ ] Security audit dataset
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- [ ] Multi-turn conversation dataset
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- [ ] Active learning integration
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### Long Term (Q4 2024+)
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- [ ] Few-shot learning examples
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- [ ] Code execution feedback
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- [ ] Self-improvement trajectories
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- [ ] Cross-lingual transfer
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## Resources
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### Documentation
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- [Quick Start Guide](QUICKSTART.md) - Get started in 5 minutes
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- [Format Specification](../claude_dataset_format.md) - Detailed format docs
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- [Implementation Summary](SUMMARY.md) - Technical deep-dive
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- [Module README](../../crates/ruvllm/src/training/README.md) - API reference
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### Examples
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- [Dataset Generator](../../crates/ruvllm/examples/generate_claude_dataset.rs)
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- [Fine-Tuning Pipeline](../../crates/ruvllm/examples/finetune_routing.rs) (coming soon)
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### Code
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- [claude_dataset.rs](../../crates/ruvllm/src/training/claude_dataset.rs) - Core implementation
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- [tests.rs](../../crates/ruvllm/src/training/tests.rs) - Test suite
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## Support
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- **Issues**: https://github.com/ruvector/issues
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- **Discussions**: https://github.com/ruvector/discussions
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- **Documentation**: https://docs.ruvector.io
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## License
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All datasets are licensed under MIT OR Apache-2.0, same as RuvLTRA.
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