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feat: Added main.waf.parameters.json with updated parameters and updated deployment.md with waf deployment steps
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content-gen/docs/AZD_DEPLOYMENT.md

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azd env set azureOpenaiAPIVersion 2024-12-01-preview
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```
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### 4. Enable Optional Features (WAF Pillars)
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### 4. Choose Deployment Configuration
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```bash
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# Enable private networking (VNet integration)
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azd env set enablePrivateNetworking true
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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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# Enable monitoring (Log Analytics + App Insights)
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azd env set enableMonitoring true
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By default, the `azd up` command uses the [`main.parameters.json`](../infra/main.parameters.json) file to deploy the solution. This file is pre-configured for a **sandbox environment**.
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# Enable scalability (auto-scaling, higher SKUs)
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azd env set enableScalability true
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For **production deployments**, the repository also provides [`main.waf.parameters.json`](../infra/main.waf.parameters.json), which applies a [Well-Architected Framework (WAF) aligned](https://learn.microsoft.com/en-us/azure/well-architected/) configuration. This can be used for Production scenarios.
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# Enable redundancy (zone redundancy, geo-replication)
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azd env set enableRedundancy true
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```
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**How to choose your deployment configuration:**
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* **To use sandbox/dev environment** — Use the default `main.parameters.json` file.
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* **To use production configuration:**
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Before running `azd up`, copy the contents from the production configuration file to your main parameters file:
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1. Navigate to the `infra` folder in your project.
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2. Open `main.waf.parameters.json` in a text editor (like Notepad, VS Code, etc.).
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3. Select all content (Ctrl+A) and copy it (Ctrl+C).
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4. Open `main.parameters.json` in the same text editor.
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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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content-gen/docs/DEPLOYMENT.md

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### Deploying with AZD
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Once you've opened the project in [Codespaces](#github-codespaces), [Dev Containers](#vs-code-dev-containers), or [locally](#local-environment), you can deploy it to Azure by following the steps in the [AZD Deployment Guide](AZD_DEPLOYMENT.md)
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Once you've opened the project in [Codespaces](#github-codespaces), [Dev Containers](#vs-code-dev-containers), or [locally](#local-environment), you can deploy it to Azure by following the steps in the [AZD Deployment Guide](AZD_DEPLOYMENT.md).
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## Post Deployment Steps
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content-gen/infra/main.parameters.json

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},
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"imageTag": {
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"value": "${imageTag=latest}"
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},
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"enablePrivateNetworking": {
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"value": "${enablePrivateNetworking}"
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},
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"enableMonitoring": {
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"value": "${enableMonitoring}"
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},
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"enableScalability": {
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"value": "${enableScalability}"
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},
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"enableRedundancy": {
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"value": "${enableRedundancy}"
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}
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}
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}
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{
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"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentParameters.json#",
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"contentVersion": "1.0.0.0",
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"parameters": {
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"solutionName": {
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"value": "${AZURE_ENV_NAME}"
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},
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"location": {
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"value": "${AZURE_LOCATION}"
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},
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"gptModelName": {
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"value": "${gptModelName}"
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},
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"gptModelVersion": {
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"value": "${gptModelVersion}"
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},
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"gptModelDeploymentType": {
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"value": "${gptModelDeploymentType}"
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},
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"gptModelCapacity": {
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"value": "${gptModelCapacity}"
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},
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"imageModelChoice": {
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"value": "${imageModelChoice}"
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},
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"dalleModelCapacity": {
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"value": "${dalleModelCapacity}"
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},
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"embeddingModel": {
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"value": "${embeddingModel}"
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},
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"embeddingDeploymentCapacity": {
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"value": "${embeddingDeploymentCapacity}"
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},
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"azureOpenaiAPIVersion": {
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"value": "${azureOpenaiAPIVersion}"
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},
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"azureAiServiceLocation": {
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"value": "${azureAiServiceLocation}"
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},
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"existingLogAnalyticsWorkspaceId": {
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"value": "${existingLogAnalyticsWorkspaceId}"
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},
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"azureExistingAIProjectResourceId": {
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"value": "${azureExistingAIProjectResourceId}"
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},
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"acrName": {
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"value": "${acrName}"
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},
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"imageTag": {
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"value": "${imageTag=latest}"
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},
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"enableMonitoring": {
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"value": true
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},
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"enablePrivateNetworking": {
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"value": true
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},
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"enableScalability": {
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"value": true
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},
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"virtualMachineAdminUsername": {
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"value": "${AZURE_ENV_VM_ADMIN_USERNAME}"
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},
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"virtualMachineAdminPassword": {
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"value": "${AZURE_ENV_VM_ADMIN_PASSWORD}"
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}
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}
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}

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