Merge pull request #383 from microsoft/dev

fix: merging dev changes to main branch

Author: Roopan-Microsoft

.github/workflows/test.yml

@@ -51,7 +51,7 @@ jobs:
       - name: Run tests with coverage
         if: env.skip_tests == 'false'
         run: |
-          pytest --cov=. --cov-report=term-missing --cov-report=xml
+          pytest --cov=. --cov-report=term-missing --cov-report=xml --ignore=tests/e2e-test/tests
           
       - name: Skip coverage report if no tests
         if: env.skip_tests == 'true'

docs/CustomizingAzdParameters.md

@@ -9,8 +9,8 @@ By default this template will use the environment name as the prefix to prevent
 | Name                            | Type   | Default Value     | Purpose                                                                                             |
 | ------------------------------- | ------ | ----------------- | --------------------------------------------------------------------------------------------------- |
 | `AZURE_ENV_NAME`                | string | `macae`           | Used as a prefix for all resource names to ensure uniqueness across environments.                   |
-| `AZURE_LOCATION`                | string | `swedencentral`   | Location of the Azure resources. Controls where the infrastructure will be deployed.                |
-| `AZURE_ENV_OPENAI_LOCATION`     | string | `swedencentral`   | Specifies the region for OpenAI resource deployment.                                                |
+| `AZURE_LOCATION`                | string | `<User selects during deployment>`   | Location of the Azure resources. Controls where the infrastructure will be deployed.                |
+| `AZURE_ENV_OPENAI_LOCATION`     | string | `<User selects during deployment>`   | Specifies the region for OpenAI resource deployment.                                                |
 | `AZURE_ENV_MODEL_DEPLOYMENT_TYPE` | string | `GlobalStandard` | Defines the deployment type for the AI model (e.g., Standard, GlobalStandard).                     |
 | `AZURE_ENV_MODEL_NAME`          | string | `gpt-4o`          | Specifies the name of the GPT model to be deployed.                                                |
 | `AZURE_ENV_FOUNDRY_PROJECT_ID`          | string | `<Existing Workspace Id>`          | Set this if you want to reuse an AI Foundry Project instead of creating a new one.                                                |                        

docs/DeploymentGuide.md

@@ -233,7 +233,7 @@ The easiest way to run this accelerator is in a VS Code Dev Containers, which wi
 
 ## Detailed Development Container setup instructions
 
-The solution contains a [development container](https://code.visualstudio.com/docs/remote/containers) with all the required tooling to develop and deploy the accelerator. To deploy the Chat With Your Data accelerator using the provided development container you will also need:
+The solution contains a [development container](https://code.visualstudio.com/docs/remote/containers) with all the required tooling to develop and deploy the accelerator. To deploy the Multi-Agent solutions accelerator using the provided development container you will also need:
 
 - [Visual Studio Code](https://code.visualstudio.com)
 - [Remote containers extension for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
@@ -287,7 +287,7 @@ The files for the dev container are located in `/.devcontainer/` folder.
 
    - You can use the Bicep extension for VSCode (Right-click the `.bicep` file, then select "Show  deployment plan") or use the Azure CLI:
      ```bash
-     az deployment group create -g <resource-group-name> -f deploy/macae-dev.bicep --query 'properties.outputs'
+     az deployment group create -g <resource-group-name> -f infra/main.bicep --query 'properties.outputs'
      ```
    - **Note**: You will be prompted for a `principalId`, which is the ObjectID of your user in Entra ID. To find it, use the Azure Portal or run:
 
@@ -301,7 +301,7 @@ The files for the dev container are located in `/.devcontainer/` folder.
 
      **Role Assignments in Bicep Deployment:**
 
-     The **macae-dev.bicep** deployment includes the assignment of the appropriate roles to AOAI and Cosmos services. If you want to modify an existing implementation—for example, to use resources deployed as part of the simple deployment for local debugging—you will need to add your own credentials to access the Cosmos and AOAI services. You can add these permissions using the following commands:
+     The **main.bicep** deployment includes the assignment of the appropriate roles to AOAI and Cosmos services. If you want to modify an existing implementation—for example, to use resources deployed as part of the simple deployment for local debugging—you will need to add your own credentials to access the Cosmos and AOAI services. You can add these permissions using the following commands:
 
      ```bash
      az cosmosdb sql role assignment create --resource-group <solution-accelerator-rg> --account-name <cosmos-db-account-name> --role-definition-name "Cosmos DB Built-in Data Contributor" --principal-id <aad-user-object-id> --scope /subscriptions/<subscription-id>/resourceGroups/<solution-accelerator-rg>/providers/Microsoft.DocumentDB/databaseAccounts/<cosmos-db-account-name>
@@ -320,11 +320,16 @@ The files for the dev container are located in `/.devcontainer/` folder.
 
 5. **Create a `.env` file:**
 
-   - Navigate to the `src` folder and create a `.env` file based on the provided `.env.sample` file.
+   - Navigate to the `src\backend` folder and create a `.env` file based on the provided `.env.sample` file.
+   - Update the `.env` file with the required values from your Azure resource group in Azure Portal App Service environment variables.
+   - Alternatively, if resources were
+   provisioned using `azd provision` or `azd up`, a `.env` file is automatically generated in the `.azure/<env-name>/.env`
+   file. To get your `<env-name>` run `azd env list` to see which env is default.
 
 6. **Fill in the `.env` file:**
 
    - Use the output from the deployment or check the Azure Portal under "Deployments" in the resource group.
+   - Make sure to set APP_ENV to "**dev**" in `.env` file.
 
 7. **(Optional) Set up a virtual environment:**
 
@@ -337,8 +342,19 @@ The files for the dev container are located in `/.devcontainer/` folder.
      ```bash
      pip install -r requirements.txt
      ```
+     
+9. **Build the frontend (important):**
 
-9. **Run the application:**
+    - Before running the frontend server, you must build the frontend to generate the necessary `build/assets` directory.
+
+      From the `src/frontend` directory, run:
+
+      ```bash
+      npm install
+      npm run build
+      ```
+
+10. **Run the application:**
 
 - From the src/backend directory:
 

docs/LocalDeployment.md

@@ -18,6 +18,8 @@ param enableTelemetry bool = true
 
 param existingLogAnalyticsWorkspaceId string = ''
 
+param azureopenaiVersion string = '2025-01-01-preview'
+
 // Restricting deployment to only supported Azure OpenAI regions validated with GPT-4o model
 @metadata({
   azd : {
@@ -1000,7 +1002,7 @@ module containerApp 'br/public:avm/res/app/container-app:0.14.2' = if (container
           }
           {
             name: 'AZURE_OPENAI_API_VERSION'
-            value: '2025-01-01-preview' //TODO: set parameter/variable
+            value: azureopenaiVersion
           }
           {
             name: 'APPLICATIONINSIGHTS_INSTRUMENTATION_KEY'
@@ -1718,3 +1720,22 @@ type webSiteConfigurationType = {
   @description('Optional. The tag of the container image to be used by the Web Site.')
   containerImageTag: string?
 }
+
+
+output COSMOSDB_ENDPOINT string = 'https://${cosmosDbResourceName}.documents.azure.com:443/'
+output COSMOSDB_DATABASE string = cosmosDbDatabaseName
+output COSMOSDB_CONTAINER string = cosmosDbDatabaseMemoryContainerName
+output AZURE_OPENAI_ENDPOINT string = 'https://${aiFoundryAiServicesResourceName}.openai.azure.com/'
+output AZURE_OPENAI_MODEL_NAME string = aiFoundryAiServicesModelDeployment.name
+output AZURE_OPENAI_DEPLOYMENT_NAME string = aiFoundryAiServicesModelDeployment.name
+output AZURE_OPENAI_API_VERSION string = azureopenaiVersion
+// output APPLICATIONINSIGHTS_INSTRUMENTATION_KEY string = applicationInsights.outputs.instrumentationKey
+// output AZURE_AI_PROJECT_ENDPOINT string = aiFoundryAiServices.outputs.aiProjectInfo.apiEndpoint
+output AZURE_AI_SUBSCRIPTION_ID string = subscription().subscriptionId
+output AZURE_AI_RESOURCE_GROUP string = resourceGroup().name
+output AZURE_AI_PROJECT_NAME string = aiFoundryAiProjectName
+output AZURE_AI_MODEL_DEPLOYMENT_NAME string = aiFoundryAiServicesModelDeployment.name
+// output APPLICATIONINSIGHTS_CONNECTION_STRING string = applicationInsights.outputs.connectionString
+output AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME string = aiFoundryAiServicesModelDeployment.name
+output AZURE_AI_AGENT_ENDPOINT string = aiFoundryAiServices.outputs.aiProjectInfo.apiEndpoint
+output APP_ENV string = 'Prod'