Error Handling

Handle failures gracefully with retries, fallbacks, and proper logging.

Common Errors

Error	Cause	Solution
`RateLimitError`	Too many requests	Implement exponential backoff
`InvalidRequestError`	Bad parameters or prompt	Validate input, check token limits
`AuthenticationError`	Invalid API key	Verify key configuration
`ServiceUnavailable`	Provider outage	Retry with backoff, use fallback
`ContextLengthExceeded`	Prompt too long	Reduce prompt size, use larger model

Retry Strategy

Configure automatic retries for transient failures.

Python

provider.write({
    'max_retries': 3,
    'retry_delay': 5,           # Initial delay in seconds
    'retry_backoff': 2,         # Exponential backoff multiplier
    'retry_max_delay': 60,      # Maximum delay between retries
    'retry_on': ['rate_limit', 'server_error'],  # Error types to retry
})

Logging

Enable comprehensive logging for debugging and monitoring.

Python

import logging

_logger = logging.getLogger(__name__)

def process_task(self, task):
    try:
        _logger.info(f"Processing task {task.id} for project {self.id}")
        result = self._call_ai_provider(task)
        _logger.info(f"Task {task.id} completed successfully")
        return result

    except RateLimitError as e:
        _logger.warning(f"Rate limit hit for task {task.id}: {e}")
        raise

    except Exception as e:
        _logger.error(f"Task {task.id} failed: {e}", exc_info=True)
        task.write({
            'ai_state': 'failed',
            'ai_error': str(e),
        })
        raise

User Communication

Provide clear, actionable error messages to users.

User-Friendly Messages

Don't expose technical details to end users. Transform API errors into helpful guidance:

Python

USER_MESSAGES = {
    'rate_limit': "Our AI service is busy. Your request has been queued and will process shortly.",
    'context_length': "Your project description is too long. Please shorten it and try again.",
    'authentication': "There's a configuration issue. Please contact support.",
    'server_error': "The AI service is temporarily unavailable. We'll retry automatically.",
}

def get_user_message(self, error_type):
    return USER_MESSAGES.get(error_type, "An unexpected error occurred. Please try again.")