How can I deploy gpt4o-mini on Azure and retrieve a custom LLM endpoint URL?

Below is a detailed explanation on what to do when using Azure OpenAI to deploy a model like gpt4o-mini.

---

Key Steps for Deploying on Azure

1. Create an Azure OpenAI Resource
   It sounds like you’ve already created an Azure OpenAI resource. Make sure you have a valid resource with the proper subscription and permissions enabled for the model you want to deploy.

2. Deploy Your Model
   In Azure’s portal (or via the Studio), you need to create a deployment for your model. During this step, you'll specify the model (in your case, gpt4o-mini) and choose a deployment name and settings. This step is fundamental since it binds the model to a deployment that can later be referenced by its unique endpoint.

3. Retrieve the Endpoint URL
   Once the deployment is complete, navigate to the “Keys and Endpoint” section of your Azure OpenAI resource in the Azure portal. There, you’ll see your resource’s base URL (e.g., https://<your-resource-name>.openai.azure.com/).
   The complete endpoint for your deployment will include the deployment name and API version. For example, it typically follows a URL pattern such as:

   PLAINTEXTCopied
   https://<your-resource-name>.openai.azure.com/openai/deployments/<deployment-id>/chat/completions?api-version=2023-03-15-preview

   Replace <your-resource-name> with your actual resource name and <deployment-id> with your deployment’s identifier.

4. Using the Endpoint in Your Application
   In your application, use the above endpoint URL instead of the documentation-suggested OpenAI API endpoint. Make sure to include the API version and correct paths required for your deployment. Your API requests will need to include your Azure API key for authorization, similar to the approach used in the standard OpenAI API curl examples.

---

Example Request Using cURL

Below is an example showing how to make a request to Azure's endpoint once you’ve set everything up:

BASHCopied
curl https://<your-resource-name>.openai.azure.com/openai/deployments/<deployment-id>/chat/completions?api-version=2023-03-15-preview \
  -H "Content-Type: application/json" \
  -H "api-key: <your-azure-api-key>" \
  -d '{
      "messages": [{"role": "user", "content": "Say this is a test!"}],
      "temperature": 0.7
  }'

Note:

• Replace <your-resource-name>, <deployment-id>, and <your-azure-api-key> with your actual values.
• The URL structure and the endpoints for Azure OpenAI differ from the standard OpenAI API. Be sure to reference Azure’s official documentation for any additional parameters or changes to the API version you might need.

---

Common Pitfalls

• Missing Deployment Step:
  If you only create the Azure resource and test via the playground without deploying the model, there is no dedicated endpoint for your application to call. The key step is creating the deployment, which then provides the endpoint URL.

• Incorrect Endpoint Usage:
  Ensure that you’re assembling the URL in the format expected by Azure. Using the standard OpenAI endpoint won’t work with an Azure deployment.

• API Version Mismatch:
  Make sure the API version in your URL matches the one specified in your Azure deployment documentation.

---

Following these steps should help you create a new deployment of gpt4o-mini and obtain an endpoint URL that you can use as a "Custom LLM URL" in your AI application. If issues persist, double-check the configuration in the Azure portal and consult Azure OpenAI’s documentation on deployment and endpoint usage.