docs/my-website/docs/exception_mapping.md

Exception Mapping

LiteLLM maps exceptions across all providers to their OpenAI counterparts.

• Rate Limit Errors
• Invalid Request Errors
• Authentication Errors
• Timeout Errors openai.APITimeoutError
• ServiceUnavailableError
• APIError
• APIConnectionError

Base case we return APIConnectionError

All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.

For all cases, the exception returned inherits from the original OpenAI Exception but contains 3 additional attributes:

• status_code - the http status code of the exception
• message - the error message
• llm_provider - the provider raising the exception

Usage

import litellm
import openai

try:
    response = litellm.completion(
                model="gpt-4",
                messages=[
                    {
                        "role": "user",
                        "content": "hello, write a 20 pageg essay"
                    }
                ],
                timeout=0.01, # this will raise a timeout exception
            )
except openai.APITimeoutError as e:
    print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e)
    print(type(e))
    pass

Usage - Catching Streaming Exceptions

import litellm
try:
    response = litellm.completion(
        model="gpt-3.5-turbo",
        messages=[
            {
                "role": "user",
                "content": "hello, write a 20 pg essay"
            }
        ],
        timeout=0.0001, # this will raise an exception
        stream=True,
    )
    for chunk in response:
        print(chunk)
except openai.APITimeoutError as e:
    print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e)
    print(type(e))
    pass
except Exception as e:
    print(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}")

Details

To see how it's implemented - check out the code

Create an issue or make a PR if you want to improve the exception mapping.

Note For OpenAI and Azure we return the original exception (since they're of the OpenAI Error type). But we add the 'llm_provider' attribute to them. See code

Custom mapping list

Base case - we return the original exception.

	ContextWindowExceededError	AuthenticationError	InvalidRequestError	RateLimitError	ServiceUnavailableError
Anthropic	✅	✅	✅	✅
OpenAI	✅	✅	✅	✅	✅
Replicate	✅	✅	✅	✅	✅
Cohere	✅	✅	✅	✅	✅
Huggingface	✅	✅	✅	✅
Openrouter	✅	✅	✅	✅
AI21	✅	✅	✅	✅
VertexAI			✅
Bedrock			✅
Sagemaker			✅
TogetherAI	✅	✅	✅	✅
AlephAlpha	✅	✅	✅	✅	✅

For a deeper understanding of these exceptions, you can check out this implementation for additional insights.

The ContextWindowExceededError is a sub-class of InvalidRequestError. It was introduced to provide more granularity for exception-handling scenarios. Please refer to this issue to learn more.

Contributions to improve exception mapping are welcome