feat: Add Multi-Industry Use Case Support (Contact Center & IT Helpdesk) with Deployment Improvements

.devcontainer/devcontainer.json

@@ -5,7 +5,10 @@
     "features": {
         "ghcr.io/azure/azure-dev/azd:latest": {},
         "ghcr.io/devcontainers/features/azure-cli:1": {},
-        "ghcr.io/devcontainers/features/docker-in-docker:2": {}
+        "ghcr.io/devcontainers/features/docker-in-docker:2": {},
+        "ghcr.io/jlaundry/devcontainer-features/mssql-odbc-driver:1": {
+			"version": "18"
+		}
     },
     "customizations": {
         "vscode": {

.devcontainer/setup_env.sh

@@ -6,4 +6,5 @@ git pull
 # provide execute permission to quotacheck script
 sudo chmod +x ./infra/scripts/checkquota_km.sh
 sudo chmod +x ./infra/scripts/quota_check_params.sh
-sudo chmod +x ./infra/scripts/run_process_data_scripts.sh
+sudo chmod +x ./infra/scripts/process_sample_data.sh
+sudo chmod +x ./infra/scripts/process_custom_data.sh

README.md

@@ -151,6 +151,18 @@ Employees can engage directly with conversational data using natural language, e
 Clear, contextual insights empower employees to take meaningful action based on data-driven evidence.
 
 
+</details>
+
+### Use Case
+<details>
+  <summary>Click to learn more about what use cases this solution provides</summary>
+<br/>
+
+  | **Use case** | **Persona** | **Challenges** | **Summary/approach** |
+  |---|---|---|---|
+  | Contact Center Customer Support | Analyst | Difficulty in extracting actionable insights from large, complex datasets due to limited context or practical considerations.   Limited ability to engage with data interactively, making it challenging to find the right questions to dig deeper.| Contextualized insights from mined data that enables employees to solve problems and take action. Interactive data that allow employees to ask questions and receive timely responses, providing better understanding and problem-solving.| 
+  IT Helpdesk | IT Helpdesk Analyst | Manually reviewing IT Helpdesk calls to identify recurring issues is time-consuming and inefficient. Creating graphs, analyzing performance problems, and drafting FAQs is often a slow process, leaving gaps in self-service support. | Address these challenges by leveraging AI to gain insights from call data, generating visual summaries, uncovering common issues, and producing FAQ content, transforming a labor-intensive review process into a fast, accurate, and actionable workflow. |
+
 </details>
 
 <br /><br />

azure.yaml

@@ -18,13 +18,18 @@ hooks:
       run: |
         Write-Host "Web app URL: "
         Write-Host "$env:WEB_APP_URL" -ForegroundColor Cyan
+        Write-Host "`nRun the following command in your Bash terminal. It will grant the necessary permissions between resources and your user account, and also process and load the sample data into the application."
+        Write-Host "bash ./infra/scripts/process_sample_data.sh" -ForegroundColor Cyan
       shell: pwsh
       continueOnError: false
       interactive: true
     posix:
       run: |
         echo "Web app URL: "
         echo $WEB_APP_URL
+        echo ""
+        echo "Run the following command in your Bash terminal. It will grant the necessary permissions between resources and your user account, and also process and load the sample data into the application."
+        echo "bash ./infra/scripts/process_sample_data.sh"
       shell: sh
       continueOnError: false
       interactive: true

documents/CustomizeData.md

@@ -14,15 +14,24 @@ If you would like to update the solution to leverage your own data please follow
 > **Note for WAF-aligned deployments:** If your deployment uses private networking, you'll need to log into a VM within the virtual network to upload files. See [VM login instructions](#how-to-login-to-vm-using-azure-bastion) below.
 
 3. If you have audio files, upload them to `custom_audiodata` folder. If you have call transcript files, upload them to `custom_transcripts` folder.
-4. Navigate to the terminal and run the `run_process_data_scripts.sh` to process the new data into the solution with the following commands. 
-    ```shell
-    cd infra/scripts
-
-    az login
-
-    bash run_process_data_scripts.sh resourcegroupname_param
+4. Navigate to the terminal and run the `process_custom_data.sh` script to process the new data into the solution with the following commands:
+
+    ```bash
+    bash ./infra/scripts/process_custom_data.sh
+    ```
+
+    If you don't have `azd env` then you need to pass parameters along with the command. Parameters are grouped by service for clarity. The command will look like the following:
+
+    ```bash
+    bash ./infra/scripts/process_custom_data.sh \
+      <Resource-Group-Name> <Azure-Subscription-ID> \
+      <Storage-Account-Name> <Storage-Container-Name> \
+      <SQL-Server-Name> <SQL-Database-Name> <Backend-User-MID-Client-ID> <Backend-User-MID-Display-Name> \
+      <AI-Search-Name> <Search-Endpoint> \
+      <AI-Foundry-Resource-ID> <CU-Foundry-Resource-ID> \
+      <OpenAI-Endpoint> <Embedding-Model> <Deployment-Model> \
+      <CU-Endpoint> <AI-Agent-Endpoint> <CU-API-Version>
     ```
-    a. resourcegroupname_param - the name of the resource group.
 
 ## How to Login to VM Using Azure Bastion
 

documents/DeploymentGuide.md

@@ -6,11 +6,11 @@ To deploy this solution, ensure you have access to an [Azure subscription](https
 
 Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/?products=all&regions=all) page and select a **region** where the following services are available:
 
-- [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry)
-- [Azure AI Content Understanding Service](https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/)
+- [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry)
+- [Azure Content Understanding Service](https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/)
 - [Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/)
 - [GPT Model Capacity](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models)
-- [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
+- [Foundry IQ](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
 - [Azure SQL Database](https://learn.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview)
 - [Azure Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/introduction)
 - [Azure Container Apps](https://learn.microsoft.com/en-us/azure/container-apps/)
@@ -150,6 +150,7 @@ If you're not using one of the above options for opening the project, then you'l
     - [Python 3.9+](https://www.python.org/downloads/)
     - [Docker Desktop](https://www.docker.com/products/docker-desktop/)
     - [Git](https://git-scm.com/downloads)
+    - [Microsoft ODBC Driver 18](https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver16) for SQL Server.
 
 2. Clone the repository or download the project code via command-line:
 
@@ -176,6 +177,7 @@ When you start the deployment, most parameters will have **default values**, but
 | **Azure Region**                            | The region where resources will be created.                                                               | *(empty)*              |
 | **Environment Name**                        | A **3–20 character alphanumeric value** used to generate a unique ID to prefix the resources.             | env\_name              |
 | **Azure AI Content Understanding Location** | Region for content understanding resources.                                                               | swedencentral          |
+| **Use Case**                      | Industry use case: **Contact-center** or **IT_helpdesk**.  | (empty)               |
 | **Secondary Location**                      | A **less busy** region for **Azure SQL and Azure Cosmos DB**, useful in case of availability constraints. | eastus2                |
 | **Deployment Type**                         | Select from a drop-down list (allowed: `Standard`, `GlobalStandard`).                                     | GlobalStandard         |
 | **GPT Model**                               | Choose from **gpt-4, gpt-4o, gpt-4o-mini**.                                                               | gpt-4o-mini            |
@@ -244,12 +246,64 @@ Once you've opened the project in [Codespaces](#github-codespaces), [Dev Contain
 
 3. Provide an `azd` environment name (e.g., "ckmapp").
 4. Select a subscription from your Azure account and choose a location that has quota for all the resources. 
-    -- This deployment will take *7-10 minutes* to provision the resources in your account and set up the solution with sample data.
+5. Choose the use case: 
+   - **Contact-center**
+   - **IT_helpdesk** 
+
+    - This deployment generally takes **7-10 minutes** to provision the resources in your account and set up the solution.
     - If you encounter an error or timeout during deployment, changing the location may help, as there could be availability constraints for the resources.
 
-5. Once the deployment has completed successfully, open the [Azure Portal](https://portal.azure.com/), go to the deployed resource group, find the App Service, and get the app URL from `Default domain`.
+5. Once the deployment has completed successfully, copy the bash command from terminal: (ex: `bash ./infra/scripts/process_sample_data.sh`) for later use.
+
+6. Create and activate a virtual environment in bash terminal:
+
+    ```shell
+    python -m venv .venv
+    ```
+
+    **For Windows (Bash):**
+    ```shell
+    source .venv/Scripts/activate
+    ```
+
+    **For Linux/VS Code Web (Bash):**
+    ```shell
+    source .venv/bin/activate
+    ```
+
+7. Login to Azure:
+
+    ```shell
+    az login
+    ```
+
+    Alternatively, login to Azure using a device code (recommended when using VS Code Web):
+
+    ```shell
+    az login --use-device-code
+    ```
+8. Run the bash script from the output of the azd deployment. The script will look like the following:
+
+    ```bash
+    bash ./infra/scripts/process_sample_data.sh
+    ```
+
+    If you don't have `azd env` then you need to pass parameters along with the command. Parameters are grouped by service for clarity. The command will look like the following:
+
+    ```bash
+    bash ./infra/scripts/process_sample_data.sh \
+      <Resource-Group-Name> <Azure-Subscription-ID> \
+      <Storage-Account-Name> <Storage-Container-Name> \
+      <SQL-Server-Name> <SQL-Database-Name> <Backend-User-MID-Client-ID> <Backend-User-MID-Display-Name> \
+      <AI-Search-Name> <Search-Endpoint> \
+      <AI-Foundry-Resource-ID> <CU-Foundry-Resource-ID> \
+      <OpenAI-Endpoint> <Embedding-Model> <Deployment-Model> \
+      <CU-Endpoint> <AI-Agent-Endpoint> <CU-API-Version> <Use-Case>
+    ```
+
+9. Once the deployment has completed successfully, open the [Azure Portal](https://portal.azure.com/), go to the deployed resource group, find the App Service, and get the app URL from `Default domain`.
 
-6. If you are done trying out the application, you can delete the resources by running `azd down`.
+10. You can now delete the resources by running `azd down`, if you are done trying out the application.
    > **Note:** If you deployed with `enableRedundancy=true` and Log Analytics workspace replication is enabled, you must first disable replication before running `azd down` else resource group delete will fail. Follow the steps in [Handling Log Analytics Workspace Deletion with Replication Enabled](./LogAnalyticsReplicationDisable.md), wait until replication returns `false`, then run `azd down`.
 
 ### 🛠️ Troubleshooting

documents/LocalDebuggingSetup.md

@@ -11,12 +11,12 @@ Install these tools before you start:
   - [Azure Tools](https://marketplace.visualstudio.com/items?itemName=ms-vscode.vscode-node-azure-pack)
   - [Bicep](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-bicep)
   - [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
-- [Python 3.11](https://www.python.org/downloads/). **Important:** Check "Add Python to PATH" during installation.
+- [Python 3.9+](https://www.python.org/downloads/). **Important:** Check "Add Python to PATH" during installation.
 - [PowerShell 7.0+](https://github.com/PowerShell/PowerShell#get-powershell).
 - [Node.js (LTS)](https://nodejs.org/en).
 - [Git](https://git-scm.com/downloads).
 - [Azure Developer CLI (azd) v1.18.0+](https://learn.microsoft.com/en-us/azure/developer/azure-developer-cli/install-azd).
-- [Microsoft ODBC Driver 17](https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver16) for SQL Server.
+- [Microsoft ODBC Driver 18](https://learn.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver16) for SQL Server.
 
 
 ## Setup Steps

documents/SampleQuestions.md

@@ -5,6 +5,7 @@ To help you get started, here are some **Sample Prompts** you can ask in the app
 > Note: To avoid rate limit errors, pause for 2–3 seconds after a response before submitting the next question. <br>
 Average response time is 8–14 seconds.
 
+For Contact Center (telecom) use case:  
 1. Ask the following questions:
    - Total number of calls by date for last 7 days.
       - To view the response data as a graph, just prompt "Generate Chart".
@@ -16,5 +17,12 @@ Average response time is 8–14 seconds.
 
 ![GenerateDraft](Images/Samplequestions1.png)
 
+For IT Helpdesk use case: 
+1. Ask the following questions: 
+   - Please provide the total number of calls by date for last 7 days
+   - Generate a bar chart showing the number of helpdesk calls per day for the last week.
+   - Provide a summary of performance issues users reported this week.
+   - Turn these key topics into a structured FAQ.
+
 
 This structured approach helps users quickly extract actionable insights from client conversations to help users understand priorities, trends, and opportunities for better engagement.