Merge branch 'dev' of https://github.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator into km-agentframework-v2

Author: Pavan-Microsoft

.github/workflows/deploy-KMGeneric.yml

@@ -124,7 +124,7 @@ jobs:
           az deployment group create \
             --resource-group ${{ env.RESOURCE_GROUP_NAME }} \
             --template-file infra/main.bicep \
-            --parameters solutionName=${{env.SOLUTION_PREFIX}} location="${{ env.AZURE_LOCATION }}" contentUnderstandingLocation="swedencentral" secondaryLocation="${{ env.AZURE_LOCATION }}" gptDeploymentCapacity=150 aiServiceLocation="${{ env.AZURE_LOCATION }}" createdBy="Pipeline" tags="{'SecurityControl':'Ignore','Purpose':'Deploying and Cleaning Up Resources for Validation','CreatedDate':'$current_date'}"
+            --parameters solutionName=${{env.SOLUTION_PREFIX}} location="${{ env.AZURE_LOCATION }}" contentUnderstandingLocation="swedencentral" secondaryLocation="${{ env.AZURE_LOCATION }}" gptDeploymentCapacity=150 aiServiceLocation="${{ env.AZURE_LOCATION }}" createdBy="Pipeline" tags="{'Purpose':'Deploying and Cleaning Up Resources for Validation','CreatedDate':'$current_date'}"
 
      
 

.github/workflows/job-deploy-linux.yml

@@ -144,10 +144,6 @@ jobs:
           else
             echo "❌ EXP DISABLED - Skipping EXP parameters"
           fi
-
-          # Set tags for deployment  
-          echo "Setting deployment tags..."
-          azd env set AZURE_TAG_SECURITY_CONTROL="Ignore"
           
           azd up --no-prompt
           

.github/workflows/job-deploy-windows.yml

@@ -137,10 +137,6 @@ jobs:
             Write-Host "❌ EXP DISABLED - Skipping EXP parameters"
           }
 
-          # Set tags for deployment  
-          Write-Host "Setting deployment tags..."
-          azd env set AZURE_TAG_SECURITY_CONTROL="Ignore"
-
           # Deploy using azd up
           azd up --no-prompt
           

README.md

@@ -1,6 +1,6 @@
 # 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.
+Gain actionable insights from large volumes of conversational data by identifying key themes, patterns, and relationships. Using Microsoft Foundry, Azure Content Understanding, Azure OpenAI Service, and Foundry IQ, 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.
 
@@ -22,7 +22,7 @@ Analysts working with large volumes of conversational data can use this solution
 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.
+Leverages Azure Content Understanding, Foundry IQ, 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](./documents/Images/ReadMe/solution-architecture.png)|
@@ -39,7 +39,7 @@ Leverages Azure AI Content Understanding, Azure AI Search, Azure OpenAI Service,
 <summary>Click to learn more about the key features this solution enables</summary>  
 
 - **Mined entities and relationships** <br/>  
-Azure AI Content Understanding and Azure OpenAI Service extract entities and relationships from unstructured data to create a knowledge base.
+Azure Content Understanding and Azure OpenAI Service extract entities and relationships from unstructured data to create a knowledge base.
 
 - **Processed data at scale** <br/>  
 Microsoft Fabric processes conversation data at scale, generating vector embeddings for efficient retrieval using the RAG (Retrieval-Augmented Generation) pattern.
@@ -98,8 +98,8 @@ _Note: This is not meant to outline all costs as selected SKUs, scaled use, cust
 
 | Product | Description | Tier / Expected Usage Notes | Cost |
 |---|---|---|---|
-| [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry) | Used to orchestrate and build AI workflows that combine Azure AI services. | Free Tier | [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/) |
-| [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) | Powers vector-based semantic search for retrieving indexed conversation data. | Standard S1; costs scale with document count and replica/partition settings. | [Pricing](https://azure.microsoft.com/pricing/details/search/) |
+| [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry) | Used to orchestrate and build AI workflows that combine Azure AI services. | Free Tier | [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/) |
+| [Foundry IQ](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) | Powers vector-based semantic search for retrieving indexed conversation data. | Standard S1; costs scale with document count and replica/partition settings. | [Pricing](https://azure.microsoft.com/pricing/details/search/) |
 | [Azure Storage Account](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview) | Stores transcripts, intermediate outputs, and application assets. | Standard LRS; usage-based cost by storage/operations. | [Pricing](https://azure.microsoft.com/pricing/details/storage/blobs/) |
 | [Azure Key Vault](https://learn.microsoft.com/en-us/azure/key-vault/general/overview) | Secures secrets, credentials, and keys used across the application. | Standard Tier; cost per operation (e.g., secret retrieval). | [Pricing](https://azure.microsoft.com/pricing/details/key-vault/) |
 | [Azure AI Services (OpenAI)](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/overview) | Enables language understanding, summarization, entity extraction, and chat capabilities using GPT models. | S0 Tier; pricing depends on token volume and model used (e.g., GPT-4o-mini). | [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/) |

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/)
@@ -176,7 +176,8 @@ 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          |
+| **Azure Content Understanding Location** | Region for content understanding resources.                                                               | swedencentral          |
+| **Use Case**                      | Industry use case: **telecom** 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            |
@@ -188,7 +189,7 @@ When you start the deployment, most parameters will have **default values**, but
 | **Image Tag**                               | Docker image tag to deploy. Common values: `latest_waf`, `dev`, `hotfix`.                  | latest_waf       |
 | **Use Local Build**                         | Boolean flag to determine if local container builds should be used.                         | false             |
 | **Existing Log Analytics Workspace**        | To reuse an existing Log Analytics Workspace ID.                                                          | *(empty)*              |
-| **Existing Azure AI Foundry Project**        | To reuse an existing Azure AI Foundry Project ID instead of creating a new one.              | *(empty)*          |
+| **Existing Microsoft Foundry Project**        | To reuse an existing Microsoft Foundry Project ID instead of creating a new one.              | *(empty)*          |
 
 
 
@@ -215,7 +216,7 @@ Depending on your subscription quota and capacity, you can [adjust quota setting
 </details>
 <details>
 
-  <summary><b>Reusing an Existing Azure AI Foundry Project</b></summary>
+  <summary><b>Reusing an Existing Microsoft Foundry Project</b></summary>
 
   Guide to get your [Existing Project ID](/documents/re-use-foundry-project.md)
 
@@ -245,6 +246,10 @@ 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. 
+5. Choose the use case: 
+   - **telecom**
+   - **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.
 
@@ -304,7 +309,7 @@ Once you've opened the project in [Codespaces](#github-codespaces), [Dev Contain
       <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>
+      <CU-Endpoint> <AI-Agent-Endpoint> <CU-API-Version> <Use-Case>
     ```
 
 10. Once the script has run 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`.

documents/Images/ReadMe/solution-architecture.png

@@ -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.

documents/SampleQuestions.md

@@ -13,13 +13,13 @@ Stores uploaded call transcripts and audio files. Serves as the initial staging
 ### Azure AI Content Understanding  
 Processes the audio and text files to extract conversation details, including speaker turns, timestamps, and semantic structure.
 
-### Azure AI Search  
+### Foundry IQ 
 Indexes the vectorized transcripts for semantic search. Enables rapid retrieval of relevant conversation snippets and contextual fragments using vector search and keyword matching.
 
 ### SQL Database  
 Stores structured output including extracted entities, mapped concepts, and additional metadata.
 
-### Azure AI Services  
+### Microsoft Foundry 
 Performs topic modeling on enriched transcript data, uncovering themes and conversation patterns using pre-trained models.
 
 ### Azure OpenAI Service  

documents/TechnicalArchitecture.md

@@ -1,13 +1,13 @@
 [← Back to *DEPLOYMENT* guide](/documents/DeploymentGuide.md#deployment-options--steps)
 
-# Reusing an Existing Azure AI Foundry Project
-To configure your environment to use an existing Azure AI Foundry Project, follow these steps:
+# Reusing an Existing Microsoft Foundry Project
+To configure your environment to use an existing Microsoft Foundry Project, follow these steps:
 ---
 ### 1. Go to Azure Portal
 Go to https://portal.azure.com
 
-### 2. Search for Azure AI Foundry
-In the search bar at the top, type "Azure AI Foundry" and click on it. Then select the Foundry service instance where your project exists.
+### 2. Search for Microsoft Foundry
+In the search bar at the top, type "Microsoft Foundry" and click on it. Then select the Foundry service instance where your project exists.
 
 ![alt text](../documents/Images/re_use_foundry_project/azure_ai_foundry_list.png)