From 774ddc144c5321cb31716dbe0a54b7350f28a38f Mon Sep 17 00:00:00 2001 From: Timna Brown <24630902+brown9804@users.noreply.github.com> Date: Wed, 29 Oct 2025 12:35:48 -0600 Subject: [PATCH 1/3] Revise README with comprehensive project details Updated the README to include detailed information about the project, its purpose, infrastructure, resources, pipeline components, data flow, UI options, deployment steps, execution methods, monitoring, prerequisites, and licensing. --- README.md | 39 ++++++++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index feb85fd..670a0d6 100644 --- a/README.md +++ b/README.md @@ -31,21 +31,30 @@ Last updated: 2025-09-17 - [Solution Accelerator for AI Document Processor (ADP)](https://github.com/azure/ai-document-processor) - AI Factory -| **Category** | **Details** | -|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **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. | -| **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. | -| **Main Azure Resources** | - **Azure Function App**: Hosts the orchestrator and activity functions using Durable Functions to manage the document processing workflow.
- **Azure Storage Account**: Stores input documents (bronze container) and output results (gold container). Also holds prompt configurations if UI is not deployed.
- **Azure Static Web App**: Provides a user-friendly interface for uploading files, editing prompts, and triggering workflows.
- **Azure OpenAI**: Processes extracted text using LLMs to generate structured summaries or insights.
- **Azure Cognitive Services**: Specifically uses Document Intelligence for OCR and text extraction from uploaded files.
- **Cosmos DB**: Stores prompt configurations when the frontend UI is enabled, allowing dynamic updates from the web interface.
- **Key Vault**: Securely stores secrets, keys, and credentials used by the Function App and other services.
- **Application Insights**: Enables monitoring, logging, and diagnostics for the Function App and other components.
- **App Service Plan**: Provides the compute resources for running the Function App. | -| **Pipeline Components** | - `function_app.py`: Main orchestrator using Durable Functions chaining pattern.
- `activities/runDocIntel.py`: Extracts text from documents using Azure Document Intelligence.
- `activities/callAoai.py`: Sends extracted text and prompt to Azure OpenAI and receives structured JSON.
- `activities/writeToBlob.py`: Writes the final output to the gold container in blob storage. | -| **Data Flow** | 1. Upload document to bronze container
2. OCR via Document Intelligence
3. Send extracted text + prompt to Azure OpenAI
4. Receive structured JSON
5. Write output to gold container | -| **Frontend UI (Optional)** | - Allows business users to upload files and edit prompts
- Prompts are stored in Cosmos DB
- Users can trigger workflows and view job status directly from the interface | -| **Prompt Configuration** | - Without UI: Prompts are stored in `prompts.yaml` file in blob storage
- With UI: Prompts are stored and managed in Cosmos DB via the web interface | -| **Deployment Steps** | 1. Fork and clone the GitHub repo
2. Run `az login`, `azd auth login`, `azd up`
3. Provide User Principal ID for RBAC setup
4. Choose whether to deploy frontend UI | -| **Execution (Without UI)** | - Update `prompts.yaml` with desired instructions
- Send POST request to `http_start` endpoint with blob metadata
- Monitor pipeline execution via Log Stream | -| **Execution (With UI)** | - Upload files via web interface
- Edit system and user prompts
- Click "Start Workflow" to trigger pipeline
- View success/failure messages and job status | -| **Monitoring & Troubleshooting** | - Use Log Stream for real-time logs
- Use Log Analytics Workspace to query exceptions and performance metrics
- Use SSH console in Development Tools to inspect deployment logs and file system | -| **Pre-Requisites** | - Azure CLI
- Azure Developer CLI (azd)
- Node.js 18.x.x
- npm 9.x.x
- Python 3.11 | -| **License** | MIT License – Free to use, modify, and distribute with attribution. No warranty provided. | + | **Category** | **Details** | + |------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | **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. | + | **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. | + | **Main Azure Resources** | - **Azure Function App**: Hosts the orchestrator and activity functions using Durable Functions to manage the document processing workflow.
- **Azure Storage Account**: Stores input documents (bronze container) and output results (gold container). Also holds prompt configurations if UI is not deployed.
- **Azure Static Web App**: Provides a user-friendly interface for uploading files, editing prompts, and triggering workflows.
- **Azure OpenAI**: Processes extracted text using LLMs to generate structured summaries or insights.
- **Azure Cognitive Services**: Specifically uses Document Intelligence for OCR and text extraction from uploaded files.
- **Cosmos DB**: Stores prompt configurations when the frontend UI is enabled, allowing dynamic updates from the web interface.
- **Key Vault**: Securely stores secrets, keys, and credentials used by the Function App and other services.
- **Application Insights**: Enables monitoring, logging, and diagnostics for the Function App and other components.
- **App Service Plan**: Provides the compute resources for running the Function App. | + | **Pipeline Components** | - `function_app.py`: Main orchestrator using Durable Functions chaining pattern.
- `activities/runDocIntel.py`: Extracts text from documents using Azure Document Intelligence.
- `activities/callAoai.py`: Sends extracted text and prompt to Azure OpenAI and receives structured JSON.
- `activities/writeToBlob.py`: Writes the final output to the gold container in blob storage. | + | **Data Flow** | 1. Upload document to bronze container
2. OCR via Document Intelligence
3. Send extracted text + prompt to Azure OpenAI
4. Receive structured JSON
5. Write output to gold container | + | **Frontend UI (Optional)** | - Allows business users to upload files and edit prompts
- Prompts are stored in Cosmos DB
- Users can trigger workflows and view job status directly from the interface | + | **Prompt Configuration** | - Without UI: Prompts are stored in `prompts.yaml` file in blob storage
- With UI: Prompts are stored and managed in Cosmos DB via the web interface | + | **Deployment Steps** | 1. Fork and clone the GitHub repo
2. Run `az login`, `azd auth login`, `azd up`
3. Provide User Principal ID for RBAC setup
4. Choose whether to deploy frontend UI | + | **Execution (Without UI)** | - Update `prompts.yaml` with desired instructions
- Send POST request to `http_start` endpoint with blob metadata
- Monitor pipeline execution via Log Stream | + | **Execution (With UI)** | - Upload files via web interface
- Edit system and user prompts
- Click "Start Workflow" to trigger pipeline
- View success/failure messages and job status | + | **Monitoring & Troubleshooting** | - Use Log Stream for real-time logs
- Use Log Analytics Workspace to query exceptions and performance metrics
- Use SSH console in Development Tools to inspect deployment logs and file system | + | **Pre-Requisites** | - Azure CLI
- Azure Developer CLI (azd)
- Node.js 18.x.x
- npm 9.x.x
- Python 3.11 | + | **License** | MIT License – Free to use, modify, and distribute with attribution. No warranty provided. | + + image + + > Data flow: + + image + + > ZTA: + image - [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) - [Plan and manage costs for Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/costs-plan-manage) From 2ecc763ba4125386d14e5144efaaad4aaea803e6 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 29 Oct 2025 18:36:09 +0000 Subject: [PATCH 2/3] Update last modified date in Markdown files --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 670a0d6..3a435bf 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ Costa Rica [![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/) [brown9804](https://github.com/brown9804) -Last updated: 2025-09-17 +Last updated: 2025-10-29 ---------- From 48813db3f5e5fb7c6adc4fac0248ac9582bd2549 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 29 Oct 2025 18:36:12 +0000 Subject: [PATCH 3/3] Update visitor count --- README.md | 4 ++-- terraform-infrastructure/README.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 670a0d6..948006a 100644 --- a/README.md +++ b/README.md @@ -478,7 +478,7 @@ Last updated: 2025-09-17
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diff --git a/terraform-infrastructure/README.md b/terraform-infrastructure/README.md index 0ddeee6..885b04f 100644 --- a/terraform-infrastructure/README.md +++ b/terraform-infrastructure/README.md @@ -109,7 +109,7 @@ graph TD;
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