text: your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.export OPENAI_API_BASE=https://your-resource-name.openai.azure.com# The API key for your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.export OPENAI_API_KEY=<your Azure OpenAI API key>Alternatively, you can configure the API right within your running Python environment:import osos.environ["OPENAI_API_TYPE"] = "azure"...Deploymentsâ€‹With Azure OpenAI, you set up your own deployments of the common GPT-3 and Codex models.  When calling the API, you need to specify the deployment you want to use.Note: These docs are for the Azure text completion models. Models like GPT-4 are chat models. They have a slightly different interface, and can be accessed via the AzureChatOpenAI class. For docs on Azure chat see Azure Chat OpenAI documentation.Let's say your deployment name is text-davinci-002-prod.  In the openai Python API, you can specify this deployment with the engine parameter.  For example:import openairesponse = openai.Completion.create(    engine="text-davinci-002-prod",    prompt="This is a test",    max_tokens=5)pip install openaiimport osos.environ["OPENAI_API_TYPE"] = "azure"os.environ["OPENAI_API_VERSION"] = "2023-05-15"os.environ["OPENAI_API_BASE"] = "..."os.environ["OPENAI_API_KEY"] = "..."# Import Azure OpenAIfrom langchain.llms import AzureOpenAI# Create an instance of Azure OpenAI# Replace the deployment name with your ownllm = AzureOpenAI(    deployment_name="td2",    model_name="text-davinci-002",)# Run the