Error Handling in ClientAI¶
ClientAI provides a robust error handling system that unifies exceptions across different AI providers. This guide covers how to handle potential errors when using ClientAI.
Table of Contents¶
Exception Hierarchy¶
ClientAI uses a custom exception hierarchy to provide consistent error handling across different AI providers:
from clientai.exceptions import (
ClientAIError,
AuthenticationError,
RateLimitError,
InvalidRequestError,
ModelError,
TimeoutError,
APIError
)
ClientAIError
: Base exception class for all ClientAI errors.AuthenticationError
: Raised when there's an authentication problem with the AI provider.RateLimitError
: Raised when the AI provider's rate limit is exceeded.InvalidRequestError
: Raised when the request to the AI provider is invalid.ModelError
: Raised when there's an issue with the specified model.TimeoutError
: Raised when a request to the AI provider times out.APIError
: Raised when there's an API-related error from the AI provider.
Handling Errors¶
Here's how to handle potential errors when using ClientAI:
from clientai import ClientAI
from clientai.exceptions import (
ClientAIError,
AuthenticationError,
RateLimitError,
InvalidRequestError,
ModelError,
TimeoutError,
APIError
)
client = ClientAI('openai', api_key="your-openai-api-key")
try:
response = client.generate_text("Tell me a joke", model="gpt-3.5-turbo")
print(f"Generated text: {response}")
except AuthenticationError as e:
print(f"Authentication error: {e}")
except RateLimitError as e:
print(f"Rate limit exceeded: {e}")
except InvalidRequestError as e:
print(f"Invalid request: {e}")
except ModelError as e:
print(f"Model error: {e}")
except TimeoutError as e:
print(f"Request timed out: {e}")
except APIError as e:
print(f"API error: {e}")
except ClientAIError as e:
print(f"An unexpected ClientAI error occurred: {e}")
Provider-Specific Error Mapping¶
ClientAI maps provider-specific errors to its custom exception hierarchy. For example:
OpenAI¶
def _map_exception_to_clientai_error(self, e: Exception) -> None:
error_message = str(e)
status_code = getattr(e, 'status_code', None)
if isinstance(e, OpenAIAuthenticationError) or "incorrect api key" in error_message.lower():
raise AuthenticationError(error_message, status_code, original_error=e)
elif status_code == 429 or "rate limit" in error_message.lower():
raise RateLimitError(error_message, status_code, original_error=e)
elif status_code == 404 or "not found" in error_message.lower():
raise ModelError(error_message, status_code, original_error=e)
elif status_code == 400 or "invalid" in error_message.lower():
raise InvalidRequestError(error_message, status_code, original_error=e)
elif status_code == 408 or "timeout" in error_message.lower():
raise TimeoutError(error_message, status_code, original_error=e)
elif status_code and status_code >= 500:
raise APIError(error_message, status_code, original_error=e)
raise ClientAIError(error_message, status_code, original_error=e)
Replicate¶
def _map_exception_to_clientai_error(self, e: Exception, status_code: int = None) -> ClientAIError:
error_message = str(e)
status_code = status_code or getattr(e, 'status_code', None)
if "authentication" in error_message.lower() or "unauthorized" in error_message.lower():
return AuthenticationError(error_message, status_code, original_error=e)
elif "rate limit" in error_message.lower():
return RateLimitError(error_message, status_code, original_error=e)
elif "not found" in error_message.lower():
return ModelError(error_message, status_code, original_error=e)
elif "invalid" in error_message.lower():
return InvalidRequestError(error_message, status_code, original_error=e)
elif "timeout" in error_message.lower() or status_code == 408:
return TimeoutError(error_message, status_code, original_error=e)
elif status_code == 400:
return InvalidRequestError(error_message, status_code, original_error=e)
else:
return APIError(error_message, status_code, original_error=e)
Best Practices¶
-
Specific Exception Handling: Catch specific exceptions when you need to handle them differently.
-
Logging: Log errors for debugging and monitoring purposes.
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
try:
response = client.generate_text("Tell me a joke", model="gpt-3.5-turbo")
except ClientAIError as e:
logger.error(f"An error occurred: {e}", exc_info=True)
- Retry Logic: Implement retry logic for transient errors like rate limiting.
import time
from clientai.exceptions import RateLimitError
def retry_generate(prompt, model, max_retries=3, delay=1):
for attempt in range(max_retries):
try:
return client.generate_text(prompt, model=model)
except RateLimitError as e:
if attempt == max_retries - 1:
raise
wait_time = e.retry_after if hasattr(e, 'retry_after') else delay * (2 ** attempt)
logger.warning(f"Rate limit reached. Waiting for {wait_time} seconds...")
time.sleep(wait_time)
- Graceful Degradation: Implement fallback options when errors occur.
def generate_with_fallback(prompt, primary_client, fallback_client):
try:
return primary_client.generate_text(prompt, model="gpt-3.5-turbo")
except ClientAIError as e:
logger.warning(f"Primary client failed: {e}. Falling back to secondary client.")
return fallback_client.generate_text(prompt, model="llama-2-70b-chat")
By following these practices and utilizing ClientAI's unified error handling system, you can create more robust and maintainable applications that gracefully handle errors across different AI providers.