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Merge pull request #12 from MicrosoftCloudEssentials-LearningHub/AI-SA-refs
Update README with detailed project information
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‎.github/workflows/use-visitor-counter.yml‎

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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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ref: ${{ github.head_ref || github.ref_name }}
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- name: Shallow clone visitor counter logic
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run: git clone --depth=1 https://github.com/brown9804/github-visitor-counter.git
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git config --global user.name "github-actions[bot]"
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git config --global user.email "github-actions[bot]@users.noreply.github.com"
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- name: Commit and push changes (PR)
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if: github.event_name == 'pull_request'
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- name: Commit and merge changes
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env:
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TOKEN: ${{ secrets.GITHUB_TOKEN }}
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PR_BRANCH: ${{ github.head_ref || github.ref_name }}
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GIT_AUTHOR_NAME: github-actions[bot]
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GIT_AUTHOR_EMAIL: github-actions[bot]@users.noreply.github.com
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GIT_COMMITTER_NAME: github-actions[bot]
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GIT_COMMITTER_EMAIL: github-actions[bot]@users.noreply.github.com
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run: |
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git fetch origin
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git checkout -b ${{ github.event.pull_request.head.ref }} origin/${{ github.event.pull_request.head.ref }}
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git add "*.md" metrics.json
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git commit -m "Update visitor count" || echo "No changes to commit"
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git remote set-url origin https://x-access-token:${TOKEN}@github.com/${{ github.repository }}
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git pull --rebase origin ${{ github.event.pull_request.head.ref }} || echo "No rebase needed"
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git push origin HEAD:${{ github.event.pull_request.head.ref }}
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- name: Commit and push changes (non-PR)
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if: github.event_name != 'pull_request'
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env:
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TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: |
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git fetch origin
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git checkout ${{ github.event.pull_request.head.ref }} || git checkout -b ${{ github.event.pull_request.head.ref }} origin/${{ github.event.pull_request.head.ref }}
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git add "*.md" metrics.json
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git commit -m "Update visitor count" || echo "No changes to commit"
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git remote set-url origin https://x-access-token:${TOKEN}@github.com/${{ github.repository }}
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git pull --rebase origin ${{ github.event.pull_request.head.ref }} || echo "No rebase needed"
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git push origin HEAD:${{ github.event.pull_request.head.ref }}
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- name: Create Pull Request (non-PR)
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if: github.event_name != 'pull_request'
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uses: peter-evans/create-pull-request@v6
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with:
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token: ${{ secrets.GITHUB_TOKEN }}
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branch: update-visitor-count
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title: "Update visitor count"
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body: "Automated update of visitor count"
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base: main
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# Ensure we're on the correct branch
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git switch -c "$PR_BRANCH" || git switch "$PR_BRANCH"
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# Stage and commit changes if any
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git add -A
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git diff --staged --quiet || git commit -m "Update visitor count"
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# Pull and merge existing changes
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git pull origin "$PR_BRANCH" --no-rebase
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# Push all changes
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git push origin "$PR_BRANCH"

‎README.md‎

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[![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
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[brown9804](https://github.com/brown9804)
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Last updated: 2025-08-07
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Last updated: 2025-10-29
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----------
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- [Solution Accelerator for AI Document Processor (ADP)](https://github.com/azure/ai-document-processor) - AI Factory
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| **Category** | **Details** |
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|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| **Purpose** | Automate document processing using Azure services and LLMs. Extracts data from files (PDF, Word, MP3), processes via Azure OpenAI, and outputs structured insights (JSON/CSV) to blob storage. |
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| **Infrastructure Provisioning** | Uses Bicep templates to deploy all required Azure resources. Automates setup of networking, identity, and access controls using RBAC and managed identities to ensure secure and scalable deployment. |
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| **Main Azure Resources** | - **Azure Function App**: Hosts the orchestrator and activity functions using Durable Functions to manage the document processing workflow.<br>- **Azure Storage Account**: Stores input documents (bronze container) and output results (gold container). Also holds prompt configurations if UI is not deployed.<br>- **Azure Static Web App**: Provides a user-friendly interface for uploading files, editing prompts, and triggering workflows.<br>- **Azure OpenAI**: Processes extracted text using LLMs to generate structured summaries or insights.<br>- **Azure Cognitive Services**: Specifically uses Document Intelligence for OCR and text extraction from uploaded files.<br>- **Cosmos DB**: Stores prompt configurations when the frontend UI is enabled, allowing dynamic updates from the web interface.<br>- **Key Vault**: Securely stores secrets, keys, and credentials used by the Function App and other services.<br>- **Application Insights**: Enables monitoring, logging, and diagnostics for the Function App and other components.<br>- **App Service Plan**: Provides the compute resources for running the Function App. |
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| **Pipeline Components** | - `function_app.py`: Main orchestrator using Durable Functions chaining pattern.<br>- `activities/runDocIntel.py`: Extracts text from documents using Azure Document Intelligence.<br>- `activities/callAoai.py`: Sends extracted text and prompt to Azure OpenAI and receives structured JSON.<br>- `activities/writeToBlob.py`: Writes the final output to the gold container in blob storage. |
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| **Data Flow** | 1. Upload document to bronze container<br>2. OCR via Document Intelligence<br>3. Send extracted text + prompt to Azure OpenAI<br>4. Receive structured JSON<br>5. Write output to gold container |
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| **Frontend UI (Optional)** | - Allows business users to upload files and edit prompts<br>- Prompts are stored in Cosmos DB<br>- Users can trigger workflows and view job status directly from the interface |
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| **Prompt Configuration** | - Without UI: Prompts are stored in `prompts.yaml` file in blob storage<br>- With UI: Prompts are stored and managed in Cosmos DB via the web interface |
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| **Deployment Steps** | 1. Fork and clone the GitHub repo<br>2. Run `az login`, `azd auth login`, `azd up`<br>3. Provide User Principal ID for RBAC setup<br>4. Choose whether to deploy frontend UI |
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| **Execution (Without UI)** | - Update `prompts.yaml` with desired instructions<br>- Send POST request to `http_start` endpoint with blob metadata<br>- Monitor pipeline execution via Log Stream |
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| **Execution (With UI)** | - Upload files via web interface<br>- Edit system and user prompts<br>- Click "Start Workflow" to trigger pipeline<br>- View success/failure messages and job status |
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| **Monitoring & Troubleshooting** | - Use Log Stream for real-time logs<br>- Use Log Analytics Workspace to query exceptions and performance metrics<br>- Use SSH console in Development Tools to inspect deployment logs and file system |
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| **Pre-Requisites** | - Azure CLI<br>- Azure Developer CLI (azd)<br>- Node.js 18.x.x<br>- npm 9.x.x<br>- Python 3.11 |
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| **License** | MIT License – Free to use, modify, and distribute with attribution. No warranty provided. |
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| **Category** | **Details** |
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|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| **Purpose** | Automate document processing using Azure services and LLMs. Extracts data from files (PDF, Word, MP3), processes via Azure OpenAI, and outputs structured insights (JSON/CSV) to blob storage. |
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| **Infrastructure Provisioning** | Uses Bicep templates to deploy all required Azure resources. Automates setup of networking, identity, and access controls using RBAC and managed identities to ensure secure and scalable deployment. |
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| **Main Azure Resources** | - **Azure Function App**: Hosts the orchestrator and activity functions using Durable Functions to manage the document processing workflow.<br>- **Azure Storage Account**: Stores input documents (bronze container) and output results (gold container). Also holds prompt configurations if UI is not deployed.<br>- **Azure Static Web App**: Provides a user-friendly interface for uploading files, editing prompts, and triggering workflows.<br>- **Azure OpenAI**: Processes extracted text using LLMs to generate structured summaries or insights.<br>- **Azure Cognitive Services**: Specifically uses Document Intelligence for OCR and text extraction from uploaded files.<br>- **Cosmos DB**: Stores prompt configurations when the frontend UI is enabled, allowing dynamic updates from the web interface.<br>- **Key Vault**: Securely stores secrets, keys, and credentials used by the Function App and other services.<br>- **Application Insights**: Enables monitoring, logging, and diagnostics for the Function App and other components.<br>- **App Service Plan**: Provides the compute resources for running the Function App. |
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| **Pipeline Components** | - `function_app.py`: Main orchestrator using Durable Functions chaining pattern.<br>- `activities/runDocIntel.py`: Extracts text from documents using Azure Document Intelligence.<br>- `activities/callAoai.py`: Sends extracted text and prompt to Azure OpenAI and receives structured JSON.<br>- `activities/writeToBlob.py`: Writes the final output to the gold container in blob storage. |
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| **Data Flow** | 1. Upload document to bronze container<br>2. OCR via Document Intelligence<br>3. Send extracted text + prompt to Azure OpenAI<br>4. Receive structured JSON<br>5. Write output to gold container |
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| **Frontend UI (Optional)** | - Allows business users to upload files and edit prompts<br>- Prompts are stored in Cosmos DB<br>- Users can trigger workflows and view job status directly from the interface |
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| **Prompt Configuration** | - Without UI: Prompts are stored in `prompts.yaml` file in blob storage<br>- With UI: Prompts are stored and managed in Cosmos DB via the web interface |
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| **Deployment Steps** | 1. Fork and clone the GitHub repo<br>2. Run `az login`, `azd auth login`, `azd up`<br>3. Provide User Principal ID for RBAC setup<br>4. Choose whether to deploy frontend UI |
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| **Execution (Without UI)** | - Update `prompts.yaml` with desired instructions<br>- Send POST request to `http_start` endpoint with blob metadata<br>- Monitor pipeline execution via Log Stream |
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| **Execution (With UI)** | - Upload files via web interface<br>- Edit system and user prompts<br>- Click "Start Workflow" to trigger pipeline<br>- View success/failure messages and job status |
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| **Monitoring & Troubleshooting** | - Use Log Stream for real-time logs<br>- Use Log Analytics Workspace to query exceptions and performance metrics<br>- Use SSH console in Development Tools to inspect deployment logs and file system |
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| **Pre-Requisites** | - Azure CLI<br>- Azure Developer CLI (azd)<br>- Node.js 18.x.x<br>- npm 9.x.x<br>- Python 3.11 |
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| **License** | MIT License – Free to use, modify, and distribute with attribution. No warranty provided. |
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<img width="835" height="535" alt="image" src="https://github.com/user-attachments/assets/61dbac57-f635-4dd6-9292-50e51823a8c4" />
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> Data flow:
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<img width="930" height="620" alt="image" src="https://github.com/user-attachments/assets/2f01a07b-71cd-4ee9-b316-c0a2273d01b2" />
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> ZTA:
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<img width="1840" height="935" alt="image" src="https://github.com/user-attachments/assets/947ddf0d-daf3-4df3-949c-1271a3fef7bb" />
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- [Use Azure AI services with SynapseML in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-science/how-to-use-ai-services-with-synapseml)
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- [Plan and manage costs for Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/costs-plan-manage)
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<!-- START BADGE -->
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<div align="center">
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<img src="https://img.shields.io/badge/Total%20views-1304-limegreen" alt="Total views">
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<p>Refresh Date: 2025-09-17</p>
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<img src="https://img.shields.io/badge/Total%20views-1298-limegreen" alt="Total views">
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<p>Refresh Date: 2025-10-29</p>
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</div>
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<!-- END BADGE -->

‎terraform-infrastructure/README.md‎

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<!-- START BADGE -->
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<div align="center">
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<img src="https://img.shields.io/badge/Total%20views-1304-limegreen" alt="Total views">
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<p>Refresh Date: 2025-09-17</p>
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<img src="https://img.shields.io/badge/Total%20views-1298-limegreen" alt="Total views">
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<p>Refresh Date: 2025-10-29</p>
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</div>
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<!-- END BADGE -->

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