Merge branch 'dev' of https://github.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator into process_new_data

Author: Pavan-Microsoft

.github/workflows/deploy.yml

@@ -0,0 +1,78 @@
+# name: Deploy MkDocs site to GitHub Pages
+
+# on:
+#   push:
+#     branches:
+#       - main
+
+# jobs:
+#   deploy:
+#     runs-on: ubuntu-latest
+
+#     steps:
+#       # Checkout the repo
+#       - name: Checkout repo
+#         uses: actions/checkout@v3
+
+#       # Set up Python environment
+#       - name: Set up Python
+#         uses: actions/setup-python@v4
+#         with:
+#           python-version: '3.x'
+
+#       # Install MkDocs and Material theme
+#       - name: Install MkDocs & Material theme
+#         run: |
+#           pip install mkdocs-material mkdocs-jupyter
+
+#       # Build the MkDocs site
+#       - name: Build the site
+#         run: |
+#           cd workshop
+#           mkdocs build
+#           touch site/.nojekyll  # Disable Jekyll
+
+#       # Deploy to GitHub Pages
+#       - name: Deploy to GitHub Pages
+#         uses: peaceiris/actions-gh-pages@v3
+#         with:
+#           github_token: ${{ secrets.GITHUB_TOKEN }}
+#           publish_dir: workshop/site
+
+name: Deploy MkDocs site to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+
+      - name: Install MkDocs and dependencies
+        run: |
+          pip install mkdocs-material mkdocs-jupyter
+
+      - name: Build MkDocs site
+        working-directory: docs/workshop
+        run: |
+          mkdocs build
+          touch site/.nojekyll  # Disable Jekyll processing
+
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: docs/workshop/site
+          # optionally set the branch to deploy to:
+          # publish_branch: gh-pages

README.md

@@ -15,19 +15,19 @@ Analysts working with large volumes of conversational data can use this solution
 </div>
 <br/>
 
-<h2><img src="./docs/images/readme/solution-overview.png" width="48" />
+<h2><img src="./documents/Images/ReadMe/solution-overview.png" width="48" />
 Solution overview
 </h2>
 
 Leverages Azure AI Content Understanding, Azure AI Search, Azure OpenAI Service, Semantic Kernel, Azure SQL Database, and Cosmos DB to process large volumes of conversational data. Audio and text inputs are analyzed through event-driven pipelines to extract and vectorize key information, orchestrate intelligent responses, and power an interactive web front-end for exploring insights using natural language.
 
 ### Solution architecture
-|![image](./docs/images/readme/solution-architecture.png)|
+|![image](./documents/Images/ReadMe/solution-architecture.png)|
 |---|
 
 ### Additional resources
 
-[Technical Architecture](./docs/TechnicalArchitecture.md)
+[Technical Architecture](./documents/TechnicalArchitecture.md)
 
 <br/>
 
@@ -55,14 +55,14 @@ Summarized conversations, topic generation, and key phrase extraction support fa
 
 
 <br /><br />
-<h2><img src="./docs/images/readme/quick-deploy.png" width="48" />
+<h2><img src="./documents/Images/ReadMe/quick-deploy.png" width="48" />
 Quick deploy
 </h2>
 
 ### How to install or deploy
 Follow the quick deploy steps on the deployment guide to deploy this solution to your own Azure subscription.
 
-[Click here to launch the deployment guide](./docs/DeploymentGuide.md)
+[Click here to launch the deployment guide](./documents/DeploymentGuide.md)
 <br/><br/>
 
 
@@ -114,12 +114,12 @@ _Note: This is not meant to outline all costs as selected SKUs, scaled use, cust
 either by deleting the resource group in the Portal or running `azd down`.
 
 <br /><br />
-<h2><img src="./docs/images/readme/business-scenario.png" width="48" />
+<h2><img src="./documents/Images/ReadMe/business-scenario.png" width="48" />
 Business scenario
 </h2>
 
 
-|![image](./docs/images/readme/ui.png)|
+|![image](./documents/Images/ReadMe/ui.png)|
 |---|
 
 <br/>
@@ -152,7 +152,7 @@ Clear, contextual insights empower employees to take meaningful action based on
 
 <br /><br />
 
-<h2><img src="./docs/images/readme/supporting-documentation.png" width="48" />
+<h2><img src="./documents/Images/ReadMe/supporting-documentation.png" width="48" />
 Supporting documentation
 </h2>
 

docs/workshop/README.md

@@ -0,0 +1,40 @@
+# Conversation Knowledge Mining Solution Accelerator: Hands-on Workshop
+
+| [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator) | [![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator) | 
+|---|---|
+
+
+
+### About the Conversation Knowledge Mining Solution Accelerator
+
+Gain actionable insights from large volumes of conversational data by identifying key themes, patterns, and relationships. Using Azure AI Foundry, Azure AI Content Understanding, Azure OpenAI Service, and Azure AI Search, this solution analyzes unstructured dialogue and maps it to meaningful, structured insights.
+
+Capabilities such as topic modeling, key phrase extraction, speech-to-text transcription, and interactive chat enable users to explore data naturally and make faster, more informed decisions.
+
+Analysts working with large volumes of conversational data can use this solution to extract insights through natural language interaction. It supports tasks like identifying customer support trends, improving contact center quality, and uncovering operational intelligence—enabling teams to spot patterns, act on feedback, and make informed decisions faster.
+
+### Solution architecture
+![High-level architecture diagram for the solution](./docs/workshop/img/ReadMe/techkeyfeatures.png)
+
+### Workshop Guide
+
+The current repository is instrumented with a `workshop/docs` folder that contains the step-by-step lab guide for developers, covering the entire workflow from resource provisioning to ideation, evaluation, deployment, and usage.
+
+ You can **preview and extend** the workshop directly from this source by running the [MKDocs](https://www.mkdocs.org/) pages locally:
+
+1. Install the `mkdocs-material` package
+
+    ```bash
+    pip install mkdocs-material
+    ```
+
+2. Run the `mkdocs serve` command from the `workshop` folder
+
+    ```bash
+    cd workshop/docs
+    mkdocs serve -a localhost:5000
+    ```
+
+This should run the dev server with a preview of the workshop guide on the specified local address. Simply open a browser and navigate to `http://localhost:5000` to view the content.
+
+(Optional) If you want to deploy the workshop guide to a live site, you can use the `mkdocs gh-deploy` command to push the content to a GitHub Pages site.

workshop/docs/index.md docs/workshop/docs/index.mdworkshop/docs/index.md renamed to docs/workshop/docs/index.md

@@ -3,18 +3,19 @@
 ## Step 1: Create a Content Understanding Project
 
 - Navigate to the [AI Foundry homepage](https://ai.azure.com) and select Try Content Understanding.
+> **Note**: You will need to create a project in one of the following regions: westus, swedencentral, or australiaeast
 
   ![AI Foundry Homepage](../img/ai-services-landing-page.png)   
 
 - Select + Create to create a new Content Understand project.
 
-  ![CU Landing Page](../img/cu-create-project.png)
+  ![CU Landing Page](../img/cu-landing-page.png)
 
 - Provide a name for your project (i.e. call_analyzer), select create a new hub, keep the default Azure AI service connection and select Next
   ![create project](../img/create_project.png)
 - Keep the default storage account, select next and select Create project. 
 
-- Select Browse file to upload the [sample audio file](../data/convo_2c703f97-6657-4a15-b8b2-db6b96630b2d_2024-12-06%2006_00_00.wav).
+- Select Browse file to upload the sample audio file included in this [workshop](../data/convo_2c703f97-6657-4a15-b8b2-db6b96630b2d_2024-12-06%2006_00_00.wav).
 
   ![CU upload document](../img/cu-upload-document.png)
 

workshop/docs/workshop/.env.sample docs/workshop/docs/workshop/.env.sampleworkshop/docs/workshop/.env.sample renamed to docs/workshop/docs/workshop/.env.sample

@@ -7,20 +7,20 @@ To access and explore the ingested data:
 2. Locate the Resource Group where the solution is deployed
 
 3. Click on the Storage Account associated with the solution
-      ![image](../img/storage-blob.png)
+      ![image](../../img/storage-blob.png)
 
 4. Navigate to Containers section
-      ![image](../img/storage-container.png)
+      ![image](../../img/storage-container.png)
 
 5. Open the container named data
 
 You’ll see two folders:
 
 - audiodata → Contains uploaded call recordings
-      ![image](../img/audio-folder.png)
+      ![image](../../img/audio-folder.png)
 
 call_transcripts → Stores the transcript text used for AI processing
-      ![image](../img/call_transcripts.png)
+      ![image](../../img/call_transcripts.png)
 <!-- The Sample dataset used for this solution contains customer interactions categorized into various topics. Each entry includes:
 
 - **ConversationId**: Unique identifier for each conversation.

…op/docs/workshop/00-Use-Case-Scenerio.md …op/docs/workshop/00-Use-Case-Scenerio.mdworkshop/docs/workshop/00-Use-Case-Scenerio.md renamed to docs/workshop/docs/workshop/00-Use-Case-Scenerio.md workshop/docs/workshop/00-Use-Case-Scenerio.md renamed to docs/workshop/docs/workshop/00-Use-Case-Scenerio.md

@@ -5,7 +5,7 @@
 
 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](../support-docs/Images/ReadMe/ckm-ui.png)
+![image](../../../../../../documents/Images/ReadMe/ui.png)
 
 ### Features