# autoSMART OpenAI Configuration
# AI prediction engine settings

[openai]
# API Configuration
api_key = sk-your-openai-api-key-here
api_endpoint = https://api.openai.com/v1
model = gpt-4
max_tokens = 2048
temperature = 0.1              # Low temperature for consistent predictions

# Request limits and retry
max_requests_per_hour = 100
retry_attempts = 3
retry_delay = 5                # seconds between retries
request_timeout = 60           # seconds

[prediction]
# Prediction parameters
prediction_window_days = 30    # Predict failures within 30 days
confidence_threshold = 0.7     # Minimum confidence for alerts
historical_data_days = 90      # Use 90 days of historical data
minimum_readings = 10          # Minimum readings before prediction

# AI prompt configuration
system_prompt = "You are an expert HDD failure prediction system. Analyze SMART data and provide failure probability with reasoning."
include_context = true         # Include disk model, age, environment
include_trends = true          # Include trend analysis in prompts

[analysis]
# Analysis frequency
full_analysis_hours = 24       # Full AI analysis every 24 hours
quick_check_hours = 6          # Quick check every 6 hours
emergency_check_minutes = 30   # Emergency analysis for critical values

# Batch processing
batch_size = 10                # Analyze 10 disks per batch
batch_delay = 2                # seconds between batch requests

[features]
# Feature engineering for AI
enable_trend_analysis = true
enable_anomaly_detection = true
enable_correlation_analysis = true
enable_environmental_factors = true

# Advanced features
enable_model_specific_analysis = true  # Different analysis per HDD model
enable_failure_clustering = true       # Group similar failure patterns
enable_seasonal_adjustment = true      # Account for seasonal temperature changes