Azure Function using OpenAI TextCompletion input binding with Java

Browse code

This sample highlights how to use the Azure Functions OpenAI TextCompletion input binding to send content to Azure OpenAI and get a result using Java.

You can learn more about the OpenAI trigger and bindings extension in the GitHub documentation and in the Official OpenAI extension documentation.

You can opt in to have a VNet used in the sample by setting SKIP_VNET to true in the parameters.

Prerequisites

• Java Developer Kit (JDK) version 17:
  • Other supported Java versions require updates to the pom.xml file.
  • The local JAVA_HOME environment variable must be set to the install location of the correct version of the JDK.
• Apache Maven, version 3.0 or above.
• Azure Functions Core Tools v4.x
• Azure OpenAI resource
• Azurite
• If using Visual Studio Code to run and debug locally:
  • Visual Studio Code
  • Azure Functions extension

Prepare your local environment

Create Azure OpenAI resource for local and cloud dev-test

Run the following command to download the project code

Bash

azd init -t https://github.com/Azure-Samples/azure-functions-completion-openai-java

Enable scripts to create local settings file after deployment Mac/Linux:

Bash

chmod +x ./infra/scripts/*.sh 

Windows:

Powershell

set-executionpolicy remotesigned

Once you have your Azure subscription, run the following in a new terminal window to create Azure OpenAI and other resources needed:

Bash

azd provision

Alternatively, you can opt-in to have a VNet used in the sample. To do so, use azd env to configure SKIP_VNET to false before running azd up:

Bash

azd env set SKIP_VNET false
azd up

Take note of the value of AZURE_OPENAI_ENDPOINT which can be found in ./.azure/<env name from azd provision>/.env. It will look something like:

Bash

AZURE_OPENAI_ENDPOINT="https://cog-<unique string>.openai.azure.com/"

Alternatively you can create an OpenAI resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource and view the Endpoint value. You will also need to deploy a model, e.g. with name completion and model gpt-4o.

Create local.settings.json (should be in the same folder as host.json). This will be created if you run azd provision

JSON

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
    "FUNCTIONS_WORKER_RUNTIME": "java",
    "AZURE_OPENAI_ENDPOINT": "<paste from above>",
    "CHAT_MODEL_DEPLOYMENT_NAME": "completion"
    }
}

Permissions

Add the following permissions to the Azure OpenAI resource

Cognitive Services OpenAI User - give your user account access to the OpenAI resource if you did not create the OpenAI resource to test locally and the Azure Function App's Managed Identity when running in Azure. If you used azd provision this step is already done - your logged in user and your function's managed identity already have permissions granted.

Run your app from the terminal

1. Open the folder in a new terminal.

2. From the app folder, run these commands to start the Functions host locally:

   Bash

   mvn clean package
   mvn azure-functions:run
   

3. In the command palette (F1), type Azurite: Start, which enables debugging using local storage.

4. Open the test.http project file, update the port on the localhost URL (if needed), and then use the built-in HTTP client to call the PostUserContent endpoint. This requires the RestClient extension installed.

Run your app using Visual Studio Code

1. Open the folder in a new terminal.
2. Run the code . code command to open the project in Visual Studio Code.
3. In the command palette (F1), type Azurite: Start, which enables debugging using local storage.
4. Press Run/Debug (F5) to run in the debugger. Select Debug anyway if prompted about local emulator not running.
5. Open the test.http project file, update the port on the localhost URL (if needed), and then use the built-in HTTP client to call the PostUserContent endpoint. This requires the RestClient extension installed.

Deploy to Azure

Run this command to provision the function app, with any required Azure resources, and deploy your code:

shell

azd up

You're prompted to supply these required deployment parameters:

Parameter	Description
Environment name	An environment that's used to maintain a unique deployment context for your app. You won't be prompted if you created the local project using azd init.
Azure subscription	Subscription in which your resources are created.
Azure location	Azure region in which to create the resource group that contains the new Azure resources. Only regions that currently support the Flex Consumption plan are shown.

After publish completes successfully, azd provides you with the URL endpoints of your new functions, but without the function key values required to access the endpoints. To learn how to obtain these same endpoints along with the required function keys, see Invoke the function on Azure in the companion article Quickstart: Create and deploy functions to Azure Functions using the Azure Developer CLI.

Redeploy your code

You can run the azd up command as many times as you need to both provision your Azure resources and deploy code updates to your function app.

注意

Deployed code files are always overwritten by the latest deployment package.

Clean up resources

When you're done working with your function app and related resources, you can use this command to delete the function app and its related resources from Azure and avoid incurring any further costs (--purge does not leave a soft delete of AI resource and recovers your quota):

shell

azd down --purge