...
All config values can be found in https://portal.azure.com/. If you already have an app with deployed model - just grab existing config values, otherwise read "Short workflow to create all needed resources in Azure"
| translate5 system config ID | explanation | Where to find the value in Azure cloud |
|---|---|---|
| runtimeOptions.plugins.OpenAI.Azure.subscriptionId |
...
| Id of the subscription used for the |
...
| GPT "application" in Azure | Home > Subscriptions |
| runtimeOptions.plugins.OpenAI.Azure.resourceGroupName |
...
| Name of the resource group used for the |
...
| GPT "application" in Azure | Search > Resource groups > Create |
| runtimeOptions.plugins.OpenAI.Azure.accountName |
...
Account name which is also used as a part of custom domain name in Azure | Go to ai.azure.com and select desired app (if more than one) and in browser address line after accounts/ you'll have an account name | |
runtimeOptions.plugins.OpenAI.Azure.tenantId |
...
| Directory (tenant) id |
...
| Search > Microsoft Entra ID > Manage > App registrations > %YOUR APP% > |
...
| Overview |
| runtimeOptions.plugins.OpenAI.Azure.clientId |
...
| Application (client) id |
...
| Search > Microsoft Entra ID > Manage > App registrations > %YOUR APP% > |
...
| Overview |
| runtimeOptions.plugins.OpenAI.Azure.clientSecret |
...
| Client secret for accessing resource management |
...
| Search > Microsoft Entra ID > Manage > App registrations > %YOUR APP% > Manage > Certificates and secrets | |
| runtimeOptions.plugins.OpenAI.Azure.deploymentType |
...
| deployment type (sku) |
...
| https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/concepts/deployment-types, normally equals to "standard" | ||
| runtimeOptions.plugins.OpenAI.server | API endpoint where chat completion requests are sent to | https://ai.azure.com/ > Home > Azure OpenAI endpoint (please note URL must contain "/apenai" at the end) |
| runtimeOptions.plugins.OpenAI.apiToken | API access token | https://ai.azure.com/ > Home > API key (1/2) |
Short workflow to create all needed resources in Azure
...
After language resource is created - translate5 automatically creates a model deployment of the selected model in Azure cloud. Deployment name will contain translate5 language resource id for ease identifying.
Even after deleting the language resource, the deployed model will not be deleted (could be implemented, if it makes sense).
Fine tuning
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/fine-tuning?tabs=azure-openai&pivots=rest-api
When finetuning a model - a new deployment is created after fine tuning is completed. Please note that Azure documentation says:
After you deploy a customized model, if at any time the deployment remains inactive for greater than fifteen (15) days, the deployment is deleted. The deployment of a customized model
...
is inactive if the model was deployed more than fifteen (15) days ago and no completions or chat completions calls were made to it during a continuous 15-day period.
...
The deletion of an inactive deployment doesn't delete or affect the underlying customized model, and the customized model can be redeployed at any time.