Conversation knowledge mining solution accelerator

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User story

Overview

This solution accelerator enables customers with large amounts of conversational data to improve decision-making by leveraging intelligence to uncover insights, relationships, and patterns from customer interactions. It empowers users to gain valuable knowledge and drive targeted business impact.

This solution accelerator leverages Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and Azure Search to transform large volumes of conversational data into actionable insights through topic modeling, key phrase extraction, speech-to-text transcription, and interactive chat experiences.

Version history: An updated version of the Conversation Knowledge Mining solution accelerator was published on xx/xx/xxxx. If you deployed the accelerator prior to that date, please see “Version history” in the Supporting documentation section.

Technical key features

Below is an image of the solution accelerator.

Use case / scenario

An analyst managing large volumes of conversational data needs a solution to visualize key insights and uncover patterns using natural language. An interactive dashboard enables them to explore rich, actionable insights for faster, and more informed decision-making.

This solution empowers analysts with tools to ask questions and receive real-time, contextualized responses. It streamlines problem-solving, enhances collaboration, and fosters innovation by making data-driven insights accessible and shareable.

The sample data used in this repository is synthetic and generated using Azure Open AI service. The data is intended for use as sample data only.

Solution accelerator architecture

Quick deploy

Prerequisites

To use this solution accelerator, you will need access to an Azure subscription with permission to create resource groups and resources.

How to install/deploy

1. Please check the link Azure Products by Region and choose a region where Azure AI Search, Azure OpenAI services, Azure AI Foundry Services are available.

2. Deploy Azure resources
   Click the following deployment button to create the required resources for this accelerator directly in your Azure Subscription.

   

   1. Most fields will have a default name set already. You will need to update the following Azure OpenAI settings:

      • Region - the region where the resources will be created in

      • Solution Prefix - provide a 6 alphanumeric value that will be used to prefix resources

      • Other Location - location of resources (required for Azure SQL and CosmoDB resources)

3. Create Fabric workspace

   1. Navigate to (Fabric Workspace)
   2. Click on Workspaces from left Navigation
   3. Click on + New Workspace
      1. Provide Name of Workspace
      2. Provide Description of Workspace (optional)
      3. Click Apply
   4. Open Workspace
   5. Create Environment
      1. Click + New Item (in Workspace)
      2. Select Environment from list
      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
      7. Click Publish
   6. Retrieve Workspace ID from URL, refer to documentation additional assistance (here)

   ***Note: Wait until the Environment is finished publishing prior to proceeding witht the next steps.

4. Deploy Fabric resources and artifacts

   1. Navigate to (Azure Portal)
   2. Click on Azure Cloud Shell in the top right of navigation Menu (add image)
   3. Run the run the following commands:
      1. az login ***Follow instructions in Azure Cloud Shell for login instructions
      2. rm -rf ./nckmdeployment
      3. git clone https://github.com/nchandhi/nckmdeployment
      4. cd ./nckmdeployment/Deployment/scripts/fabric_scripts
      5. sh ./run_fabric_items_scripts.sh keyvault_param workspaceid_param solutionprefix_param
         1. keyvault_param - the name of the keyvault that was created in Step 1
         2. workspaceid_param - the workspaceid created in Step 2
         3. solutionprefix_param - prefix used to append to lakehouse upon creation

5. Add App Authentication

   Follow steps in App Authentication to configure authenitcation in app service.

Upload additional files

All files WAV files can be uploaded in the corresponding Lakehouse in the data/Files folder:

• Audio (WAV files): Upload Audio files in the cu_audio_files_all folder.

Post-deployment

• To process additional files, manually execute the pipeline_notebook after uploading new files.
• The OpenAI prompt can be modified within the Fabric notebooks.

Supporting documentation

How to customize

If you'd like to customize the accelerator, here are some ways you might do that:

• Ingest your own audio conversation files by uploading them into the cu_audio_files_all lakehouse folder and run the data pipeline

Additional resources

• Microsoft Fabric documentation - Microsoft Fabric | Microsoft Learn
• Azure OpenAI Service - Documentation, quickstarts, API reference - Azure AI services | Microsoft Learn
• Azure AI Content Understanding documentation
• Azure AI Foundry documentation
• Speech service documentation - Tutorials, API Reference - Azure AI services - Azure AI services | Microsoft Learn

Version history

Release 3

An updated version of the Conversation Knowledge Mining (CKM) solution accelerator was published on xx/xx/xxxx. If you deployed the accelerator prior to that date, please note that CKM Release 3 cannot be deployed over CKM Release 2. Please also note that the CKM Release 3 conversation and audio file formats has been revised therefore files for prior releases are no longer compatible. For resources related to CKM Release 2, please visit our archive (link-to-archive).

Release 2

An updated version of the Conversation Knowledge Mining (CKM) solution accelerator was published on 08/15/2024. If you deployed the accelerator prior to that date, please note that CKM v2 cannot be deployed over CKM v1. Please also note that the CKM Release 2 JSON conversation file format has been revised to include additional metadata, therefore CKM v1 files are no longer compatible. For resources related to CKM v1, please visit our archive (link-to-archive).

Customer truth

Customer stories coming soon.

Responsible AI Transparency FAQ

Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.

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