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Updated the deployment readme
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‎content-gen/docs/AZD_DEPLOYMENT.md‎

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# Login to Azure CLI (required for some post-deployment scripts)
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az login
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```
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Alternatively, login to Azure using a device code (recommended when using VS Code Web):
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```
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az login --use-device-code
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```
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### 2. Initialize Environment
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azd env new content-gen-dev
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```
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### 3. Configure Parameters (Optional)
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The deployment has sensible defaults, but you can customize:
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```bash
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# Set the Azure region (default: eastus)
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azd env set AZURE_LOCATION swedencentral
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# Set AI Services region (must support your models)
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azd env set azureAiServiceLocation swedencentral
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# GPT Model configuration
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azd env set gptModelName gpt-4o
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azd env set gptModelVersion 2024-11-20
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azd env set gptModelDeploymentType GlobalStandard
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azd env set gptModelCapacity 50
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# Image generation model (dalle-3 or gpt-image-1)
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azd env set imageModelChoice gpt-image-1
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azd env set dalleModelCapacity 1
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# Embedding model
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azd env set embeddingModel text-embedding-3-large
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azd env set embeddingDeploymentCapacity 50
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# Azure OpenAI API version
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azd env set azureOpenaiAPIVersion 2024-12-01-preview
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```
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### 4. Choose Deployment Configuration
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### 3. Choose Deployment Configuration
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The [`infra`](../infra) folder contains the [`main.bicep`](../infra/main.bicep) Bicep script, which defines all Azure infrastructure components for this solution.
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5. Select all existing content (Ctrl+A) and paste the copied content (Ctrl+V).
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6. Save the file (Ctrl+S).
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### 5. Deploy
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### 4. Deploy
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```bash
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azd up
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6. **Configure** RBAC and Cosmos DB roles
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7. **Upload** sample data and create the search index
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## Deployment Parameters Reference
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `AZURE_LOCATION` | eastus | Primary Azure region |
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| `azureAiServiceLocation` | eastus | Region for AI Services (must support chosen models) |
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| `gptModelName` | gpt-4o | GPT model for content generation |
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| `gptModelVersion` | 2024-11-20 | Model version |
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| `gptModelDeploymentType` | GlobalStandard | Deployment type |
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| `gptModelCapacity` | 50 | TPM capacity (in thousands) |
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| `imageModelChoice` | dalle-3 | Image model: `dalle-3` or `gpt-image-1` |
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| `dalleModelCapacity` | 1 | Image model capacity |
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| `embeddingModel` | text-embedding-3-large | Embedding model |
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| `embeddingDeploymentCapacity` | 50 | Embedding TPM capacity |
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| `enablePrivateNetworking` | false | Enable VNet and private endpoints |
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| `enableMonitoring` | false | Enable Log Analytics + App Insights |
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| `enableScalability` | false | Enable auto-scaling |
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| `enableRedundancy` | false | Enable zone/geo redundancy |
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## Using Existing Resources
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### Reuse Existing AI Foundry Project
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azd env set existingLogAnalyticsWorkspaceId "/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.OperationalInsights/workspaces/<workspace-name>"
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```
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### Use Existing Container Registry
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```bash
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# Set the name of your existing ACR
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azd env set acrName myexistingacr
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```
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## Post-Deployment
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After `azd up` completes, you'll see output like:
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azd env get-value RESOURCE_GROUP_NAME
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```
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## Day-2 Operations
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### Update the Application
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After making code changes:
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```bash
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# Rebuild and redeploy everything
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azd up
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# Or just redeploy (no infra changes)
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azd deploy
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```
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### Update Only the Backend (Container)
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```bash
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# Get ACR and ACI names
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ACR_NAME=$(azd env get-value ACR_NAME)
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ACI_NAME=$(azd env get-value CONTAINER_INSTANCE_NAME)
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RG_NAME=$(azd env get-value RESOURCE_GROUP_NAME)
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# Build and push new image
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az acr build --registry $ACR_NAME --image content-gen-app:latest --file ./src/WebApp.Dockerfile ./src
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# Restart ACI to pull new image
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az container restart --name $ACI_NAME --resource-group $RG_NAME
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```
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### Update Only the Frontend
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```bash
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cd src/app/frontend
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npm install && npm run build
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cd ../frontend-server
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zip -r frontend-deploy.zip static/ server.js package.json package-lock.json
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az webapp deploy \
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--resource-group $(azd env get-value RESOURCE_GROUP_NAME) \
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--name $(azd env get-value APP_SERVICE_NAME) \
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--src-path frontend-deploy.zip \
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--type zip
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```
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### View Logs
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```bash
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**Solution**: Check the Dockerfile and ensure all required files are present:
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```bash
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# Manual build for debugging
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cd src
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cd src/app
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docker build -f WebApp.Dockerfile -t content-gen-app:test .
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```
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## Related Documentation
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- [Manual Deployment Guide](DEPLOYMENT.md)
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- [Image Generation Configuration](IMAGE_GENERATION.md)
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- [Azure Developer CLI Documentation](https://learn.microsoft.com/azure/developer/azure-developer-cli/)

‎content-gen/docs/DEPLOYMENT.md‎

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| ------------------------------------------- | --------------------------------------------------------------------------------------------------------- | ---------------------- |
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| **Azure Region** | The region where resources will be created. | *(empty)* |
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| **Environment Name** | A **3–20 character alphanumeric value** used to generate a unique ID to prefix the resources. | env\_name |
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| **GPT Model** | Choose from **gpt-4, gpt-4o, gpt-4o-mini**. | gpt-4o-mini |
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| **GPT Model Version** | The version of the selected GPT model. | 2024-07-18 |
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| **GPT Model** | Choose from **gpt-4, gpt-4o, gpt-4o-mini, gpt-5.1**. | gpt-5.1 |
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| **GPT Model Version** | The version of the selected GPT model. | 2025-11-13 |
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| **OpenAI API Version** | The Azure OpenAI API version to use. | 2025-01-01-preview |
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| **GPT Model Deployment Capacity** | Configure capacity for **GPT models** (in thousands). | 30k |
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| **DALL-E Model** | DALL-E model for image generation. | dall-e-3 |
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| **GPT Model Deployment Capacity** | Configure capacity for **GPT models** (in thousands). | 150k |
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| **Image Model** | Choose from **dall-e-3, gpt-image-1, gpt-image-1.5** | gpt-image-1 |
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| **Image Tag** | Docker image tag to deploy. Common values: `latest`, `dev`, `hotfix`. | latest |
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| **Use Local Build** | Boolean flag to determine if local container builds should be used. | false |
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| **Existing Log Analytics Workspace** | To reuse an existing Log Analytics Workspace ID. | *(empty)* |
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| **Existing Azure AI Foundry Project** | To reuse an existing Azure AI Foundry Project ID instead of creating a new one. | *(empty)* |
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Follow steps in [App Authentication](./AppAuthentication.md) to configure authentication in app service. Note: Authentication changes can take up to 10 minutes.
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2. **Assign RBAC Roles (if needed)**
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If you encounter 401/403 errors, run the RBAC assignment script and wait 5-10 minutes for propagation:
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```shell
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bash ./scripts/assign_rbac_roles.sh
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```
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3. **Deleting Resources After a Failed Deployment**
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2. **Deleting Resources After a Failed Deployment**
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- Follow steps in [Delete Resource Group](./DeleteResourceGroup.md) if your deployment fails and/or you need to clean up the resources.
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## Troubleshooting
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<details>
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<summary><b>Common Issues and Solutions</b></summary>
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### 401 Unauthorized Errors
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**Symptom**: API calls return 401 errors
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**Cause**: Missing RBAC role assignments
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**Solution**: Run `assign_rbac_roles.sh` and wait 5-10 minutes for propagation
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### 403 Forbidden from Cosmos DB
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**Symptom**: Cosmos DB operations fail with 403
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### Image Generation Not Working
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**Symptom**: DALL-E requests fail
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**Symptom**: DALL-E/GPT-Image requests fail
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**Cause**: Missing DALL-E model deployment or incorrect endpoint
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**Cause**: Missing DALL-E/GPT-Image model deployment or incorrect endpoint
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**Solution**:
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1. Verify DALL-E 3 deployment exists in Azure OpenAI resource
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2. Check `AZURE_OPENAI_DALLE_ENDPOINT` and `AZURE_OPENAI_DALLE_DEPLOYMENT` environment variables
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1. Verify DALL-E 3 or GPT-Image-1 or GPT-Image-1.5 deployment exists in Azure OpenAI resource
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2. Check `AZURE_OPENAI_IMAGE_MODEL` environment variable
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</details>
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## Environment Variables Reference
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<details>
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<summary><b>Backend Environment Variables (ACI)</b></summary>
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| Variable | Description | Example |
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|----------|-------------|---------|
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| AZURE_OPENAI_ENDPOINT | GPT model endpoint | https://ai-account.cognitiveservices.azure.com/ |
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| AZURE_OPENAI_DEPLOYMENT_NAME | GPT deployment name | gpt-4o-mini |
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| AZURE_OPENAI_DALLE_ENDPOINT | DALL-E endpoint | https://dalle-account.cognitiveservices.azure.com/ |
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| AZURE_OPENAI_DALLE_DEPLOYMENT | DALL-E deployment name | dall-e-3 |
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| COSMOS_ENDPOINT | Cosmos DB endpoint | https://cosmos.documents.azure.com:443/ |
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| COSMOS_DATABASE | Database name | content-generation |
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| AZURE_STORAGE_ACCOUNT_NAME | Storage account | storagecontentgen |
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| AZURE_STORAGE_CONTAINER | Product images container | product-images |
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| AZURE_STORAGE_GENERATED_CONTAINER | Generated images container | generated-images |
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</details>
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<details>
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<summary><b>Frontend Environment Variables (App Service)</b></summary>
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| Variable | Description | Example |
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|----------|-------------|---------|
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| BACKEND_URL | Backend API URL | http://backend.contentgen.internal:8000 |
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| WEBSITES_PORT | App Service port | 3000 |
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</details>
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## Sample Workflow
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## Sample Prompts
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To get started with the Content Generation solution, follow these steps:
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To help you get started, here are some **sample prompts** you can use with the Content Generation Solution:
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1. **Task:** From the welcome screen, select one of the suggested prompts. Sample prompts include:
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- *"I need to create a social media post about paint products for home remodels. The campaign is titled 'Brighten Your Springtime' and the audience is new homeowners. I need marketing copy plus an image."*
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- *"Generate a social media campaign with ad copy and an image. This is for 'Back to School' and the audience is parents of school age children. Tone is playful and humorous."*
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- "Create a product description for a new eco-friendly water bottle"
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- "Generate marketing copy for a summer sale campaign"
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- "Write social media posts promoting our latest product launch"
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- "Create an image for a blog post about sustainable living"
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- "Generate a product image showing a modern office setup"
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2. **Task:** Click the **"Confirm Brief"** button.
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> **Observe:** The system analyzes the creative brief to provide suggestions later.
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These prompts serve as a great starting point to explore the solution's capabilities with text generation, image generation, and content management.
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3. **Task:** Select a product from the product list, then click **"Generate Content"**.
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> **Observe:** Enters "Thinking Process" with a "Generating Content.." spinner. Once complete, the detailed output is displayed.
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## Architecture Overview
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