In direct connections to OpenAI, there's an observed issue where the model returned in the response does not always match the model requested. For example, if the turbo-preview model is requested, the response might contain the 0125 model instead. Currently, this results in a failure during model validation checks, as an exact match is expected.
Proposed Solution
It is proposed to downgrade model validation from an error level to a warning level. This adjustment will allow for inconsistencies in returned models, accepting minor discrepancies while still logging them for review and monitoring. This change aims to accommodate the fluid nature of model updates and variations in responses without causing system failures or interruptions in service.
Justification
This change is necessary to maintain operational stability and flexibility in handling model updates and changes from OpenAI's side. Strict model validation can lead to unnecessary failures and service disruptions, which can be avoided by allowing a margin of tolerance for model discrepancies.
Expected Outcome
Model validation will log warnings instead of errors for mismatched models in responses.
System stability and flexibility in integration with OpenAI's API will improve.
Unnecessary service interruptions due to strict model validation will be minimized.
Request
Please consider making this adjustment in the model validation process to ensure smoother operation and integration with OpenAI's evolving models and services.
Looking forward to your feedback and suggestions on this matter.
Description
In direct connections to OpenAI, there's an observed issue where the model returned in the response does not always match the model requested. For example, if the
turbo-previewmodel is requested, the response might contain the0125model instead. Currently, this results in a failure during model validation checks, as an exact match is expected.Proposed Solution
It is proposed to downgrade model validation from an error level to a warning level. This adjustment will allow for inconsistencies in returned models, accepting minor discrepancies while still logging them for review and monitoring. This change aims to accommodate the fluid nature of model updates and variations in responses without causing system failures or interruptions in service.
Justification
This change is necessary to maintain operational stability and flexibility in handling model updates and changes from OpenAI's side. Strict model validation can lead to unnecessary failures and service disruptions, which can be avoided by allowing a margin of tolerance for model discrepancies.
Expected Outcome
Request
Please consider making this adjustment in the model validation process to ensure smoother operation and integration with OpenAI's evolving models and services.
Looking forward to your feedback and suggestions on this matter.