Merge pull request #380 from microsoft/docs-gh-updates

docs: readme template and content updates

Author: aniaroramsft

Images/readme/customerTruth.png

@@ -0,0 +1,14 @@
+## Azure account setup
+
+1. Sign up for a [free Azure account](https://azure.microsoft.com/free/) and create an Azure Subscription.
+2. Check that you have the necessary permissions:
+    * Your Azure account must have `Microsoft.Authorization/roleAssignments/write` permissions, such as [Role Based Access Control Administrator](https://learn.microsoft.com/azure/role-based-access-control/built-in-roles#role-based-access-control-administrator-preview), [User Access Administrator](https://learn.microsoft.com/azure/role-based-access-control/built-in-roles#user-access-administrator), or [Owner](https://learn.microsoft.com/azure/role-based-access-control/built-in-roles#owner).
+    * Your Azure account also needs `Microsoft.Resources/deployments/write` permissions on the subscription level.
+
+You can view the permissions for your account and subscription by following the steps below: 
+- Navigate to the [Azure Portal](https://portal.azure.com/) and click on `Subscriptions` under 'Navigation' 
+- Select the subscription you are using for this accelerator from the list. 
+    - If you try to search for your subscription and it does not come up, make sure no filters are selected.
+- Select `Access control (IAM)` and you can see the roles that are assigned to your account for this subscription. 
+    - If you want to see more information about the roles, you can go to the `Role assignments`
+     tab and search by your account name and then click the role you want to view more information about.

Images/readme/quickDeploy.png

@@ -1,8 +1,8 @@
-## Content Processing
+## Content and Data Processing Workflow
 Additional details about how content processing is handled in the solution. This includes the workflow steps and how to use your own data in the solution.
 
 ### Workflow
-![image](../Images/readme_deployment/DocumentProcess.png)
+![image](./images/deployment/DocumentProcess.png)
 1. <u>Document upload</u><br/>
 Documents added to blob storage. Processing is triggered based on file check-in.
 
@@ -34,6 +34,10 @@ You can upload through the user interface files that you would like processed. T
 2. <u>Bulk File Processing</u><br/>
 You can take buik file processing since the web app saves uploaded files here also. This would be the ideal to upload a large number of document or files that are large in size. 
 
+    > **Document Upload Limit:** <br/>
+    Please ensure that the document you upload does not exceed a maximum size of 250 MB.
+    
+
 ### Modifying Processing Prompts 
 
 Prompt based processing is used for context extraction, summarization, and keyword/entity extraction. Modifications to the prompts will change what is extracted for the related workflow step.

Images/readme/supportingDocuments.png

@@ -1,4 +1,4 @@
-# Deployment Guide for Services
+# Deployment Guide
 
 > This repository presents a solution and reference architecture for the Knowledge Mining solution accelerator. Please note that the  **provided code serves as a demonstration and is not an officially supported Microsoft offering**.
 > 
@@ -10,8 +10,6 @@
 * [Deploy to Azure](#deploy-to-azure)
 * [Post-Deploy Configuration](#post-deploy-configuration)
 * [Next Steps](#next-steps)
-    * [Test APIs](./docs/TestApis.md)
-    * [Deploy Power Platform Client](./DeployPowerPlatformClient.md)
 
 ## Prerequisites
 
@@ -43,7 +41,7 @@
    3. Go to **Settings** and select **Resource Providers**.
    4. Check for Microsoft.Compute and click Register if it is not already registered.
    <br>
-   <img src="./Images/readme_deployment/Subscription_ResourceProvider.png" alt="ResourceProvider" width="900">
+   <img src="./images/deployment/Subscription_ResourceProvider.png" alt="ResourceProvider" width="900">
 
 
 ## Regional Availability
@@ -62,7 +60,7 @@ The deployment region for this model is fixed in 'East US'
 
 ## Deployment
 
-The automated deployment process is very straightforward and simplified via a single [deployment script](./Deployment/resourcedeployment.ps1) that completes in approximately 10-15 minutes:
+The automated deployment process is very straightforward and simplified via a single [deployment script](../Deployment/resourcedeployment.ps1) that completes in approximately 10-15 minutes:
 
 ### Automated Deployment Steps:
 1. Deploy Azure resources.
@@ -92,7 +90,7 @@ powershell.exe -ExecutionPolicy Bypass -File ".\resourcedeployment.ps1"
 ```
 
 You will be prompted for the following parameters with this Screen :  
-<img src="./Images/readme_deployment/Deployment_Screen01.png" width="900" alt-text="Input Parameters">  
+<img src="./images/deployment/Deployment_Screen01.png" width="900" alt-text="Input Parameters">  
 
 1. **Subscription ID** - copy/paste from Azure portal
 1. **Location** - Azure data center where resources will be deployed.
@@ -130,7 +128,7 @@ Let's check the message and configure your model's TPM rate higher to get better
 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/readme_deployment/Deployment_Screen02.png" alt="Success Deployment" width="900">
+<img src="./images/deployment/Deployment_Screen02.png" alt="Success Deployment" width="900">
 
 ## Next Steps
 
@@ -152,10 +150,10 @@ Don't miss this Url information. This is the application's endpoint URL and it s
 
 
 1. Browse to the project in Azure AI Foundry, and select **each of the 2 models** within the `Deployments` menu:  
-<img src="./Images/readme_deployment/Control_Model_TPM000.png" alt="Select Model" width="700">
+<img src="./images/deployment/Control_Model_TPM000.png" alt="Select Model" width="700">
 
 2. Increase the TPM value for **each model** for faster report generation:  
-<img src="./Images/readme_deployment/Control_Model_TPM001.png" alt="Set Token per minute" width="700">
+<img src="./images/deployment/Control_Model_TPM001.png" alt="Set Token per minute" width="700">
 
 ### 2. Data Uploading and Processing
 After increasing the TPM limit for each model, let's upload and process the sample documents.

Images/readme/userStory.png

@@ -2,7 +2,7 @@
 
 Additional details about the technical architecture of the Document Knowledge Mining solution accelerator. This describes the purpose and additional context of each component in the solution.
 
-![image](../Images/readme/architecture.png)
+![image](./images/readme/solution-architecture.png)
 
 
 ### Ingress Controller