-
Notifications
You must be signed in to change notification settings - Fork 563
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Support fallback deployments to accompany fallback models #183
Conversation
This is useful for example in Azure OpenAI deployments where you have a different deployment per model, so the current fallback implementation doesn't work (still uses the same deployment for each fallback attempt)
Question: write me a poem about this PR Answer: No longer bound, to a single deployment id, With each model a deployment, it pairs, In the settings template, a new field appears, So here's to the coder, who saw the need, |
Question: is the PR complete? (answer as if you are Elon Musk) Answer: |
@@ -14,6 +14,7 @@ key = "" # Acquire through https://platform.openai.com | |||
#api_version = '2023-05-15' # Check Azure documentation for the current API version | |||
#api_base = "" # The base URL for your Azure OpenAI resource. e.g. "https://<your resource name>.openai.azure.com" | |||
#deployment_id = "" # The deployment name you chose when you deployed the engine | |||
#fallback_deployments = [] # Match your fallback models from configuration.toml with the appropriate deployment_id |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
#fallback_deployments = [] # Match your fallback models from configuration.toml with the appropriate deployment_id | |
#fallback_deployments = [] # For each fallback model specified in configuration.toml in the [config] section, specify the appropriate deployment_id |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Done. Thanks for the clarification :)
/improve |
Support fallback deployments to accompany fallback models
This is useful for example in Azure OpenAI deployments where you have a different deployment per model, so the current fallback implementation doesn't work (still uses the same deployment for each fallback attempt)