Configuration Validation

The validation feature in IdeaWeaver helps ensure that your configuration files are properly structured and contain all necessary components for successful model training, RAG system setup, and other operations.

Features

• YAML Validation: Validate the structure and content of your configuration files
• Schema Checking: Ensure configuration files match the expected schemas for different components
• Dependency Verification: Check for required dependencies and settings
• Comprehensive Reporting: Get detailed feedback about configuration issues

Use Cases

1. Pre-training Validation: Validate configuration files before starting model training
2. Component Integration: Verify settings for integrated components (MLflow, Weights & Biases, etc.)
3. Dependency Management: Check for required dependencies and their versions

Example Configuration

Here's an example of a valid configuration file:

# config.yml
training:
  model: microsoft/DialoGPT-medium
  dataset: ./data/training.json
  output_dir: ./models/fine-tuned
  epochs: 5
  batch_size: 8
  learning_rate: 5e-5

Validation Process

1. File Structure Check: Validates the overall structure of the YAML file
2. Schema Validation: Ensures all sections match their expected schemas
3. Dependency Check: Verifies that required dependencies are specified
4. Value Validation: Checks that values are within acceptable ranges
5. Integration Validation: Validates settings for enabled integrations

Best Practices

1. Regular Validation: Validate configuration files before starting any operation
2. Version Control: Keep validated configurations in version control
3. Environment-Specific: Use different configurations for different environments
4. Documentation: Document any special configuration requirements