Merge pull request #492 from microsoft/main

fix: downmerge to dev from main

Author: Prajwal-Microsoft

.github/workflows/deploy.yml

@@ -39,5 +39,4 @@ jobs:
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: docs/workshop/site
-          # optionally set the branch to deploy to:
-          # publish_branch: gh-pages
+          

docs/workshop/README.md

@@ -23,13 +23,13 @@ The current repository is instrumented with a `workshop/docs` folder that contai
 1. Install the `mkdocs-material` package
 
     ```bash
-    pip install mkdocs-material
+    pip install mkdocs-material mkdocs-jupyter
     ```
 
 2. Run the `mkdocs serve` command from the `workshop` folder
 
     ```bash
-    cd workshop/docs
+    cd docs/workshopcd
     mkdocs serve -a localhost:5000
     ```
 

docs/workshop/docs/workshop/Challenge-1/Code_Walkthrough/02_Frontend.md

@@ -5,7 +5,9 @@
 
 The frontend is a **React-based web interface** that allows users to explore insights from conversations, interact with an AI-powered chatbot, and view dynamic visualizations.
 
-![image](../../../../../../documents/Images/ReadMe/ui.png)
+
+![image](../../img/ReadMe/ckm-ui.png)
+
 
 ### Features
 

docs/workshop/docs/workshop/Challenge-1/Solution_Overview.md

@@ -2,7 +2,9 @@
 <!-- ## Overview -->
 The Conversation Knowledge Mining Solution Accelerator is a robust application designed to extract actionable insights from conversational data. It leverages Azure AI services and provides an interactive user interface for querying and visualizing data. The solution is built with a modular architecture, combining a React-based frontend, a FastAPI backend, and Azure services for data processing and storage.
 
-![image](../../../../../documents/Images/ReadMe/solution-architecture.png)
+
+![image](../img/ReadMe/ckm-sol-arch.png)
+
 
 The solution extracts insights from call audio files or transcripts and enables users to interact with the data via a chatbot and dynamic charts:
 

docs/workshop/docs/workshop/Challenge-5/notebooks/video_chapter_generation.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "\n",
     "## Pre-requisites\n",
-    "1. Follow [README](../README.md#configure-azure-ai-service-resource) to create essential resource that will be used in this sample.\n",
+    "1. Follow [README](../docs/create_azure_ai_service.md) to create essential resource that will be used in this sample.\n",
     "1. Install required packages"
    ]
   },
@@ -92,7 +92,7 @@
    "metadata": {},
    "source": [
     "## Create a custom analyzer and submit the video to generate the description\n",
-    "The custom analyzer schema is defined in [../analyzer_templates/video_content_understanding.json](../analyzer_templates/video_content_understanding.json). The main custom field is `segmentDescription` as we need to get the descriptions of video segments and feed them into chatGPT to generate the scenes and chapters. Adding transcripts will help to increase the accuracy of scenes/chapters segmentation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
+    "The custom analyzer schema is defined in **../analyzer_templates/video_content_understanding.json**. The main custom field is `segmentDescription` as we need to get the descriptions of video segments and feed them into chatGPT to generate the scenes and chapters. Adding transcripts will help to increase the accuracy of scenes/chapters segmentation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
     "\n",
     "In this example, we will use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to Azure Content Understanding service. Then, we will analyze the video to get the segment descriptions and transcripts.\n"
    ]

docs/workshop/docs/workshop/Challenge-5/notebooks/video_tag_generation.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "\n",
     "## Pre-requisites\n",
-    "1. Follow [README](../README.md#configure-azure-ai-service-resource) to create essential resource that will be used in this sample.\n",
+    "1. Follow [README](../docs/create_azure_ai_service.md) to create essential resource that will be used in this sample.\n",
     "1. Install required packages"
    ]
   },
@@ -92,7 +92,7 @@
    "metadata": {},
    "source": [
     "## Create a custom analyzer and submit the video to generate tags\n",
-    "The custom analyzer schema is defined in [../analyzer_templates/video_tag.json](../analyzer_templates/video_tag.json). The custom fields are `segmentDescription`, `transcript` and `tags`. Adding description and transcripts helps to increase the accuracy of tag generation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
+    "The custom analyzer schema is defined in **../analyzer_templates/video_tag.json**. The custom fields are `segmentDescription`, `transcript` and `tags`. Adding description and transcripts helps to increase the accuracy of tag generation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
     "\n",
     "In this example, we will use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to Azure Content Understanding service. Then, we will analyze the video to get the segment tags.\n"
    ]

docs/workshop/mkdocs.yml

@@ -117,7 +117,7 @@ nav:
       - CU-AI Foundry: workshop/Challenge-0/CU-Challenge.md
   - Challenge 1:
       - Solution Overview: workshop/Challenge-1/Solution_Overview.md
-      - Deployment: workshop/Challenge-1/index.md
+      - Deployment: workshop/Challenge-1/Deployment.md
       # - App Authentication: workshop/Challenge-1/App_Authentication.md
       - Explore Data: workshop/Challenge-1/Code_Walkthrough/01_Data_Explore.md
       - Frontend: workshop/Challenge-1/Code_Walkthrough/02_Frontend.md
@@ -134,8 +134,8 @@ nav:
       - Knowledge Mining API Notebook: workshop/Challenge-3-and-4/knowledge_mining_api.ipynb
   - Challenge 5:
       - Overview: workshop/Challenge-5/index.md
-      - Chapter Generation Notebook: workshop/Challenge-5/video_chapter_generation.ipynb
-      - Tag Generation Notebook: workshop/Challenge-5/video_tag_generation.ipynb
+      - Chapter Generation Notebook: workshop/Challenge-5/notebooks/video_chapter_generation.ipynb
+      - Tag Generation Notebook: workshop/Challenge-5/notebooks/video_tag_generation.ipynb
   - Challenge 6:
       - Overview: workshop/Challenge-6/index.md
       - Content Safety Evaluation Notebook: workshop/Challenge-6/Content_safety_evaluation.ipynb

documents/AppAuthentication.md

@@ -1,53 +1,33 @@
-## Add Authentication in Azure App Service configuration
+# Set Up Authentication in Azure App Service
+
+This document provides step-by-step instructions to configure Azure App Registrations for a front-end application.
+
+## Prerequisites
+
+- Access to **Microsoft Entra ID**
+- Necessary permissions to create and manage **App Registrations**
+  
+## Step 1: Add Authentication in Azure App Service configuration
+
 1. Click on `Authentication` from left menu.
 
   ![Authentication](Images/AppAuthentication.png)
 
-2. Click on `+ Add Provider` to see a list of identity providers.
+2. Click on `+ Add identity provider` to see a list of identity providers.
 
   ![Authentication Identity](Images/AppAuthenticationIdentity.png)
 
-3. Click on `+ Add Provider` to see a list of identity providers.
+3. Click on `Identity Provider` dropdown to see a list of identity providers.
 
   ![Add Provider](Images/AppAuthIdentityProvider.png)
 
-4. Select the first option `Microsoft Entra Id` from the drop-down list.
- 
+4. Select the first option `Microsoft Entra Id` from the drop-down list and select `client secret expiration` under App registration.
+> NOTE: If `Create new app registration` is disabled, then go to [Create new app registration](/documents/create_new_app_registration.md) and come back to this step to complete the app authentication.
+
  ![Add Provider](Images/AppAuthIdentityProviderAdd.png)
 
 5. Accept the default values and click on `Add` button to go back to the previous page with the identity provider added.
 
  ![Add Provider](Images/AppAuthIdentityProviderAdded.png)
 
- ### Creating a new App Registration
-1. Click on `Home` and select `Microsoft Entra ID`.
-
-![Microsoft Entra ID](Images/MicrosoftEntraID.png)
-
-2. Click on `App registrations`.
-
-![App registrations](Images/Appregistrations.png)
-
-3. Click on `+ New registration`.
-
-![New Registrations](Images/NewRegistration.png)
-
-4. Provide the `Name`, select supported account types as `Accounts in this organizational directory only(Contoso only - Single tenant)`, select platform as `Web`, enter/select the `URL` and register.
-
-![Add Details](Images/AddDetails.png)
-
-5. After application is created successfully, then click on `Add a Redirect URL`.
-
-![Redirect URL](Images/AddRedirectURL.png)
-
-6. Click on `+ Add a platform`.
-
-![+ Add platform](Images/AddPlatform.png)
-
-7. Click on `Web`.
-
-![Web](Images/Web.png)
-
-8. Enter the `web app URL` (Provide the app service name in place of XXXX) and Save. Then go back to [Step 4] and follow from _Point 4_ choose `Pick an existing app registration in this directory` from the Add an Identity Provider page and provide the newly registered App Name.
-
-![Add Details](Images/WebAppURL.png)
+6. You have successfully added app authentication, and now required to log in to access the application.

documents/DeploymentGuide.md

@@ -16,7 +16,7 @@ Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/g
 - [Azure Container Apps](https://learn.microsoft.com/en-us/azure/container-apps/)
 - [Azure Container Registry](https://learn.microsoft.com/en-us/azure/container-registry/)
 - [Embedding Deployment Capacity](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#embedding-models)
-- [Azure Semantic Search](./docs/AzureSemanticSearchRegion.md)
+- [Azure Semantic Search](./AzureSemanticSearchRegion.md)
 
 Here are some example regions where the services are available: East US, East US2, Australia East, UK South, France Central.
 

documents/Fabric_deployment.md

@@ -24,7 +24,7 @@ If you'd like to customize the solution accelerator, here are some ways you migh
         3.  Provide name for Environment and click Create
         4.  Select Public libraries in left panel
         5.  Click Add from .yml
-        6.  Upload .yml from [here](./Deployment/scripts/fabric_scripts/ckm_cu_env.yml)
+        6.  Upload .yml from [here](.././infra/scripts/fabric_scripts/ckm_cu_env.yml)
         7.  Click Publish
     7.  Retrieve Workspace ID from URL, refer to documentation additional assistance ([here](https://learn.microsoft.com/en-us/fabric/admin/portal-workspace#identify-your-workspace-id))