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Prerequisites

Before attending the Intelligent App Development Workshop, please ensure you have the following prerequisites in place:

  1. Azure account: A Microsoft Azure account with an active subscription. If you don't have one, sign up for a free trial.
  2. Azure subscription with access enabled for the Azure OpenAI Service - For more details, see the Azure OpenAI Service documentation on how to get access.
  3. Azure OpenAI resource - For this workshop, you'll need to deploy at least one model such as GPT 4. See the Azure OpenAI Service documentation for more details on deploying models and model availability.

Development Environment Setup

You have the option of using Github Codespaces or your local development environment.

Using Github Codespaces (recommmended)

If using Github Codespaces all prerequisites will be pre-installed, however you will need to create a fork as follows:

  1. Navigate to this link to create a new fork (must be logged into your github account).
  2. Accept the default values and click on "Create fork" which will take you to the forked repository in the browser.
  3. From your forked repository click on the "<> Code" button. Then click on the "Create codespace on main" button.

Using local development environment

If you prefer using a computer with using a local development environment, the following pre-requisites need to be installed:

  1. Git: Ensure you have Git installed on your computer.
  2. Azure CLI: Install the Azure Command-Line Interface (CLI) to interact with Azure services and manage resources from the command line.
  3. .NET SDK: install .NET SDK to build and run .NET projects.
  4. Docker: Install Docker Desktop to build and run containerized applications.
  5. Node.Js: Install Node.Js to build and run web application.
  6. Azure Development CLI: Install azd to be able to provision and deploy application to Azure.
  7. bash/shell terminal: the lessons assume bash/shell script syntax. If using Windows, either you can either using Git Bash (included when you install Git) or installing WSL (Windows Subsystem for Linux).

Next you will need to clone this repo using:

git clone https://github.com/Azure/intelligent-app-workshop.git

Change directory into cloned repo:

cd intelligent-app-workshop

Initial Setup

  1. Copy and rename the file appsettings.json.example into the corresponding lesson directory as follows (example command for Lesson1):

    cp workshop/dotnet/Lessons/appsettings.json.example workshop/dotnet/Lessons/Lesson1/appsettings.json

  2. Create Azure OpenAI Service and retrieve the Endpoint URL, API Key and deployed model name then update newly created appsettings.json

    1. Get Azure OpenAI access values (from Azure Portal):

      1. First we need to create a new Azure OpenAI Service, so let's start there. Go to the Azure Portal.
      2. Click on Create A Resource
      3. On the search bar type Azure OpenAI and hit enter
      4. Locate Azure OpenAI and click Create
      5. On the Create Azure OpeanAI page, provide the following information for the fields on the Basics tab:
        • Subscription: The Azure subscription to used for your service.
        • Resource group: The Azure resource group to contain your Azure OpenAI service resource. You can create a new group or use a pre-existing group.
        • Region: The location of your instance. Different locations can introduce latency, but they don't affect the runtime availability of your resource.
        • Name: A descriptive and unique name for your Azure AI Service resource, such as aoai-intelligent-app-workshop-myid.
        • Pricing Tier: The pricing tier for the resource. Currently, only the Standard S0 tier is available for the Azure AI Service.
        • Check the box to acknowledge that you have read and understood all the Responsible AI notices.
      6. Click Next.
      7. Review default Network values and click Next
      8. On the Tags tab click Next
      9. Click Create.
      10. From the deployment page, wait for the deployment to complete and then click Go to resource
      11. Expand the Resource Management section in the sidebar (menu at left)
      12. Click the Keys and Endpoint option - you should see the following: KEY 1, KEY 2 and Endpoint.
      13. Copy the KEY 1 value and paste it into the apiKey value within the OpenAI element in the appsettings.json file.

      14. Copy the Endpoint value and paste it as the endpoint value within the OpenAI element in the appsettings.json file.

        Azure Open AI Keys and Endpoint

      Next, we need to create deployments from the Azure OpenAI models.

      1. Click the Model deployments option in the sidebar (left menu) for Azure OpenAI resource.
      2. In the destination page, click Manage Deployments
      3. (Optional) You can directly navigate to the Azure AI Foundry portal.

      This will take you to the Azure AI Foundry website, where we'll find the other values as described below.

    2. Create and get Azure OpenAI deployment value (from Azure AI Foundry):

      1. Navigate to Azure AI Foundry from your resource as described above.
      2. Click the Deployments tab (sidebar, left) to view currently deployed models.
      3. If your desired model is not deployed, click on Deploy Model then select to Deploy Base Model.
      4. You will need a chat completion model. For this workshop we recommend using gpt-4o. Select gpt-4o from the drop down and click Confirm.
      5. Accept the default gpt-4o values and click Deploy Terminal
      6. Update appsettings.json deploymentName field with your model deployment name.
      7. Use the Deployment Name value (e.g. gpt-4o) as the deploymentName value within the OpenAI element in the appsettings.json file.

  3. Additionally, we need to obtain an API Key to be able to get stock prices from polygon.io. You can sign up for a free API Key by creating a login. This value will be needed for Lesson 3.

    1. Once logged in, from the polygon.io Dashboard locate the Keys section. Copy the default key value and paste it as the apiKey value within the StockService element in the appsettings.json file.

By ensuring you have completed these prerequisites, you'll be well-prepared to dive into the Intelligent App Development Workshop and make the most of the hands-on learning experience.