Error and Remediation - Guardrails AI

LLM Error Handling and Retries

Guardrails currently performs automatic retries with exponential backoff when any of the following errors occur when calling the LLM:

openai.error.APIConnectionError
openai.error.APIError
openai.error.TryAgain
openai.error.Timeout
openai.error.RateLimitError
openai.error.ServiceUnavailableError
An incorrect structure was returned from the LLM

Guardrails will continue to retry with backoff until a max wait time between requests of sixty (60) seconds is reached. Note that this list is not exhaustive of the possible errors that could occur. In the event that errors other than these arise during LLM calls, an exception will be raised. The messaging of this exception is intended to help troubleshoot common problems, especially with custom LLM wrappers, as well as communicate the underlying error. This type of exception would look like the following:

The callable `fn` passed to `Guard(fn, ...)` failed with the following error:
  {Root error message here!}.

Validator OnFailActions

Guardrails provides a number of OnFailActions for when a validator fails. The OnFailAction specifies the corrective action that should be taken if the quality criteria is not met. The corrective action can be one of the following:

Action	Behavior	Supports Streaming?
`OnFailAction.NOOP`	Do nothing. The failure will still be recorded in the logs, but no corrective action will be taken.	Yes
`OnFailAction.EXCEPTION`	Raise an exception when validation fails.	Yes
`OnFailAction.REASK`	Reask the LLM to generate an output that meets the correctness criteria specified in the validator. The prompt used for reasking contains information about which quality criteria failed, which is auto-generated by the validator.	No
`OnFailAction.FIX`	Programmatically fix the generated output to meet the correctness criteria when possible. E.g. the formatter `provenance_llm` validator will remove any sentences that are estimated to be hallucinated.	No
`OnFailAction.FILTER`	(Only applicable for structured data validation) Filter the incorrect value. This only filters the field that fails, and will return the rest of the generated output.	No
`OnFailAction.REFRAIN`	Refrain from returning an output. This is useful when the generated output is not safe to return, in which case a `None` value is returned instead.	No
`OnFailAction.FIX_REASK`	First, fix the generated output deterministically, and then rerun validation with the deterministically fixed output. If validation fails, then perform reasking.	No

Custom OnFailActions can also be implemented, see the custom section in the how to use on fail actions guide.

Guidance on dealing with Validator errors

When a validator fails, Guardrails does two things

logs the failure in history (accessible via guard.history.last.failed_validations)
takes the corrective action specified by the on_fail action on the validator

While Reasks and Fixes are useful for simple usecase, as usecases get more complex, we recommend switching to an exception-based approach. This is because exceptions provide more flexibility in handling errors, and can be used to implement more complex error handling logic. They can also be used to more effectively route your LLM app along different paths based on the type of error that occurred and which set of validators failed.

​LLM Error Handling and Retries

​Validator OnFailActions

​Guidance on dealing with Validator errors

LLM Error Handling and Retries

Validator OnFailActions

Guidance on dealing with Validator errors