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@@ -1,4 +1,4 @@
 .vscode
 .vs
 .venv
-__pycache__
+__pycache__
@@ -53,6 +53,7 @@ Offers ability to [start with an existing Azure AI Project](docs/transfer_projec
 4. If deploying from your [local environment](docs/local_environment_steps.md), install the [Azure CLI (AZ)](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and the [Azure Developer CLI (AZD)](https://learn.microsoft.com/en-us/azure/developer/azure-developer-cli/install-azd?tabs=winget-windows%2Cbrew-mac%2Cscript-linux&pivots=os-windows).
 5. If deploying via [GitHub Codespaces](docs/github_code_spaces_steps.md) - requires the user to be on a GitHub Team or Enterprise Cloud plan.
 6. If leveraging [GitHub Actions](docs/github_actions_steps.md).
+7. Optionally [include a sample AI chat application](/docs/sample_app_setup.md) with the deployment.
 
 ### Check Azure OpenAI Quota Availability  
 
@@ -90,6 +91,8 @@ QUICK DEPLOY
 ## Connect to and validate access to the new environment 
 Follow the post deployment steps [Post Deployment Steps](docs/github_code_spaces_steps.md) to connect to the isolated environment.
 
+## Deploy Sample Application with the new environment
+Optionally include a [sample AI chat application](/docs/sample_app_setup.md) to showcase a production AI application deployed to a secure environment.
 
 ## Deploy your application in the isolated environment
 - Leverage the Microsoft Learn documentation to provision an app service instance within your secure network [Configure Web App](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/on-your-data-configuration#azure-ai-foundry-portal)
 
@@ -1,17 +1,42 @@
-name: deploy-your-ai-application-in-production
-infra:
-    provider: "bicep"
-metadata:
-  template: deploy-your-ai-application-in-production@1.0
-hooks:
-  preup:
-    windows:
-      shell: pwsh
-      run: ./scripts/set_conns_env_vars.ps1
-      interactive: true
-      continueOnError: false
-    posix:
-      shell: sh
-      run: chmod u+r+x ./scripts/set_conns_env_vars.sh; ./scripts/set_conns_env_vars.sh
-      interactive: true
-      continueOnError: false
+name: deploy-your-ai-application-in-production
+
+requiredVersions:
+  azd: ">=1.15.0 !=1.17.1"
+infra:
+    provider: "bicep"
+metadata:
+  template: deploy-your-ai-application-in-production@1.0
+hooks:
+  preup:
+    windows:
+      shell: pwsh
+      run: ./scripts/set_conns_env_vars.ps1
+      interactive: true
+      continueOnError: false
+    posix:
+      shell: sh
+      run: sudo chmod u+r+x ./scripts/set_conns_env_vars.sh; sudo ./scripts/set_conns_env_vars.sh
+      interactive: true
+      continueOnError: false
+  preprovision:
+    posix:
+      shell: sh
+      run: sudo chmod u+r+x ./scripts/auth_init.sh; sudo ./scripts/auth_init.sh
+      interactive: true
+      continueOnError: false
+    windows:
+      shell: pwsh
+      run: ./scripts/auth_init.ps1
+      interactive: true
+      continueOnError: false
+  postprovision:
+      posix:
+        shell: sh
+        run: sudo chmod u+r+x ./scripts/process_sample_data.sh; sudo chmod u+r+x ./scripts/postprovision.sh; sudo ./scripts/postprovision.sh
+        interactive: true
+        continueOnError: false 
+      windows:
+        shell: pwsh
+        run: ./scripts/postprovision.ps1;
+        interactive: true
+        continueOnError: false
@@ -0,0 +1,84 @@
+# Setup Sample Application
+
+This solution includes an optional sample AI chat application that can be instantiaed along with the other resources to showcase a production-ready, end-to-end application running securly on Azure. Application image is pulled from a public registry and the [source code can be found here](https://github.com/microsoft/sample-app-aoai-chatGPT).
+
+## Pre-Deployment
+
+### Setup Entra App Registration
+
+The sample application requires an [application registration in Microsoft Entra](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app). This is used for authentication. The deployment process will automatically create the application registration by default or an existing applicaiton registration can be used.
+
+#### Create Application Registration Automatically
+
+Following the steps below and executing a deployment will automatically create the Application Registration in Microsoft Entra and set the required environment variables. The application registration will then be used for that AZD environment when deploying. The executing user will need sufficient permissions on the tenant to create registrations (like the [Application Developer role](https://learn.microsoft.com/en-us/entra/identity/role-based-access-control/permissions-reference#application-developer)).
+
+#### Use Existing Application Registration
+
+In the Azure Portal, either [create a new registration](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app) or navigate to an existing registration.
+
+* Note the *Application (client) ID* and *Object ID* displayed on the overview page.
+* Navigate to "Certificates & secrets" > "New client secret".
+* Enter a description and expiration, then click "Add".
+* Copy and securely store the generated client secret value, as it will not be shown again.
+
+The client ID and client secret are required for authenticating your application with Microsoft Entra.
+
+Set the following environment variables after establishing an AZD environment:
+
+```sh
+azd env set 'AZURE_AUTH_APP_ID' '<your-object-id>'
+azd env set 'AZURE_AUTH_CLIENT_ID' '<your-client-id>'
+azd env set 'AZURE_AUTH_CLIENT_SECRET' '<your-client-secret>'
+```
+
+## Deployment
+
+### Setup Environment Variables
+
+In order to have the sample application infrastructure deployed, certain parameter requirements must be met. Set specific environment variables listed in the below AZD command block after setting up a new AZD environment and prior to running `azd up` to properly deploy the sample application. 
+
+```sh
+azd env set 'AZURE_APP_SAMPLE_ENABLED' 'true'
+azd env set 'AZURE_AI_SEARCH_ENABLED' 'true'
+azd env set 'AZURE_COSMOS_DB_ENABLED' 'true'
+```
+
+### AI Models Parameter Requirements
+
+Also, the `aiModelDeployments` parameter in the [main.parameters.json](/infra/main.parameters.json) must contain two AI model deployments in this specific order (Note: the default values meet these requirements):
+
+1. Text Embedding model (e.g., `text-embedding-ada-002`, `text-embedding-3-small`, `text-embedding-3-large`)
+2. Chat Completion model (e.g., `gpt-4`, `gpt-4o`, `gpt-4o-mini`)
+
+### Deploy
+
+Follow the [standard deployment guide](./local_environment_steps.md).
+
+## Post-Deployment
+
+1. **Access AI Foundry**
+    - Connect to your VM jump box using Azure Bastion.
+    - Once connected, browse to the Azure Portal
+    - Select the Azure AI Project resource and load the AI Foundry
+
+2. **Create a Data Source**
+    - In AI Foundry, select *Data + Indexes*, and click *+New Data*
+    - For Data Source, select to Upload Files/Folders, then Upload Files
+    - Give the Data Source a name and click Create    
+
+3. **Create an Index**
+    - In AI Foundry, select *Data + Indexes*, and click *+New Index*
+    - Select your Data Source
+    - Choose the existing Azure Cognitive Search service
+    - Keep the suggested Index name or supply a different name
+    - In the Search settings, select the *text-embedding-3-model* model deployment.
+    - Review and click Create Vector Index. Note this can take a few minutes to complete.
+
+4. **Update App Service Environment Variable**
+    - After indexing completes, note the name of your new Index.
+    - In the Azure Portal, navigate to the Azure App Service and update the relevant Environment Variable in the Configuration with this Index name.
+
+5. **Launch and Use the Application**
+    - Navigate to the Azure App Service in the Azure Portal
+    - Browse application and begin chatting with your data.
+
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