Update documentation and add new guides for Azure deployment, including resource deletion and log analytics workspace reuse

Author: Kingshuk-Microsoft

azure.yaml

@@ -5,6 +5,6 @@ name: document-knowledge-mining-solution-accelerator
 requiredVersions:
   azd: '>= 1.18.0'
 
-metadata:
-  template: document-knowledge-mining-solution-accelerator@1.0
-  name: document-knowledge-mining-solution-accelerator@1.0
+# metadata:
+#   template: document-knowledge-mining-solution-accelerator@1.0
+#   name: document-knowledge-mining-solution-accelerator@1.0

docs/AzureAIModelQuotaSettings.md

@@ -0,0 +1,9 @@
+# How to Check & Update AI Model Quota
+
+Please follow [quota check instructions guide](./QuotaCheck.md) to check quota availability by region.
+
+1. **Navigate** to the [Azure AI Foundry portal](https://ai.azure.com/).
+2. **Select** the AI Project associated with this accelerator.
+3. **Go to** the `Management Center` from the bottom-left navigation menu.
+4. Select `Request Quota`
+5. Request More Quota with fill up the form for 'Request for Quota Increase' or delete any unused model deployments as needed.

docs/DeleteResourceGroup.md

@@ -0,0 +1,56 @@
+# Deleting Resources After a Failed Deployment in Azure Portal
+
+If your deployment fails and you need to clean up the resources manually, follow these steps in the Azure Portal.
+
+---
+
+## **1. Navigate to the Azure Portal**
+
+1. Open [Azure Portal](https://portal.azure.com/).
+2. Sign in with your Azure account.
+
+---
+
+## **2. Find the Resource Group**
+
+1. In the search bar at the top, type **"Resource groups"** and select it.
+2. Locate the **resource group** associated with the failed deployment.
+
+![Resource Groups](./images/readme/portal_services_resource_groups.png)
+
+![Resource Groups](./images/readme/portal_resource_groups_search.png)
+
+---
+
+## **3. Delete the Resource Group**
+
+1. Click on the **resource group name** to open it.
+2. Click the **Delete resource group** button at the top.
+
+![Delete Resource Group](./images/readme/portal_resource_group_delete.png)
+
+3. Type the resource group name in the confirmation box and click **Delete**.
+
+📌 **Note:** Deleting a resource group will remove all resources inside it.
+
+---
+
+## **4. Delete Individual Resources (If Needed)**
+
+If you don’t want to delete the entire resource group, follow these steps:
+
+1. Open **Azure Portal** and go to the **Resource groups** section.
+2. Click on the specific **resource group**.
+3. Select the **resource** you want to delete (e.g., App Service, Storage Account).
+4. Click **Delete** at the top.
+
+![Delete Individual Resource](./images/readme/portal_web_app_delete.png)
+
+---
+
+## **5. Verify Deletion**
+
+- After a few minutes, refresh the **Resource groups** page.
+- Ensure the deleted resource or group no longer appears.
+
+📌 **Tip:** If a resource fails to delete, check if it's **locked** under the **Locks** section and remove the lock.

docs/DeploymentGuide.md

@@ -0,0 +1,91 @@
+# Post Deployment Configuration
+
+## Step 1: Execute the Script
+
+### 1.1 Open PowerShell, change directory where you code cloned, then run the deploy script:  
+
+```
+cd .\Deployment\  
+```  
+
+### 1.2 Choose the appropriate command based on your deployment method:
+
+**If you deployed using `azd up` command:**
+```
+.\resourcedeployment.ps1
+```
+
+**If you deployed using custom templates, ARM/Bicep deployments, or `az deployment group` commands:**
+```
+.\resourcedeployment.ps1 -ResourceGroupName "<your-resource-group-name>"
+```
+
+> **Note:** Replace `<your-resource-group-name>` with the actual name of the resource group containing your deployed Azure resources.
+
+> **💡 Tip**: Since this guide is for azd deployment, you'll typically use the first command without the `-ResourceGroupName` parameter.
+
+If you run into issue with PowerShell script file not being digitally signed, you can execute below command:
+
+```
+powershell.exe -ExecutionPolicy Bypass -File ".\resourcedeployment.ps1"
+```
+
+### 1.3 You will be prompted for the following parameters with this Screen :  
+
+<img src="./images/deployment/Deployment_Input_Param_01.png" width="900" alt-text="Input Parameters">
+
+#### 1.3.1 **Email** - used for issuing certificates in Kubernetes clusters from the [Let's Encrypt](https://letsencrypt.org/) service. Email address should be valid.  
+
+<img src="./images/deployment/Deployment_Login_02.png" width="900" alt-text="Login">
+
+#### 1.3.2 You will be prompted to Login, Select a account and proceed to Login.
+
+#### 1.3.3 **GO !** - Post Deployment Script executes Azure Infrastructure configuration, Application code compile and publish into Kubernetes Cluster.
+
+#### 1.3.4 Deployment Complete
+#### 🥳🎉 First, congrats on finishing Deployment!
+Let's check the message and configure your model's TPM rate higher to get better performance.  
+You can check the Application URL from the final console message.  
+Don't miss this Url information. This is the application's endpoint URL and it should be used for your data importing process.  
+
+<img src="./images/deployment/Deployment_Screen02.png" alt="Success Deployment" width="900">
+
+### Manual Deployment Steps:
+**Create Content Filter** - Please follow below steps
+> * Navigate to project in Azure OpenAI, then go to Azure AI Foundry, select Safety + security
+> * Click on Create Content Filter and set the filters to a high threshold for the following categories:
+    ```
+    Hate, Sexual, Self-harm, Violence
+    ```
+> * Please select the checkbox of profanity
+> * Leave all other configurations at their default settings and click on create
+
+## Step 2: Configure Azure OpenAI Rate Limits
+
+> **Capacity Note:**
+> * The deployment script creates models with a setting of 1 token per minute (TPM) rate limit.
+> * Faster performance can be achieved by increasing the TPM limit with Azure AI Foundry.
+> * Capacity varies for [regional quota limits](https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits#regional-quota-limits) as well as for [provisioned throughput](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/provisioned-throughput).
+> * As a starting point, we recommend the following quota threshold be set up for this service run.  
+
+| Model Name             | TPM Threshold |
+|------------------------|---------------|
+| GPT-4.1-mini           | 100K TPM      |
+| text-embedding-3-large | 200K TPM      |
+
+
+> **⚠️ Warning:**  **Insufficient quota can cause failures during the upload process.** Please ensure you have the recommended capacity or request for additional capacity before start uploading the files.
+
+
+### 2.1. Browse to the project in Azure AI Foundry, and select **each of the 2 models** within the `Deployments` menu:  
+<img src="./images/deployment/Control_Model_TPM000.png" alt="Select Model" width="700">
+
+### 2.2. Increase the TPM value for **each model** for faster report generation:  
+<img src="./images/deployment/Control_Model_TPM001.png" alt="Set Token per minute" width="700">
+
+### 3. Data Uploading and Processing
+After increasing the TPM limit for each model, let's upload and process the sample documents.
+
+Execute this command:
+
+<img src="./images/deployment/Deployment_last_step.png" alt="Set Token per minute" width="700">