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.github/workflows/broken-links-checker.yml

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uses: lycheeverse/lychee-action@v2.6.1
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with:
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args: >
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--verbose --exclude-mail --no-progress --exclude ^https?://
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--verbose --no-progress --exclude ^https?://
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${{ steps.changed-markdown-files.outputs.all_changed_files }}
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failIfEmpty: false
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env:
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uses: lycheeverse/lychee-action@v2.6.1
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with:
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args: >
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--verbose --exclude-mail --no-progress --exclude ^https?://
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--verbose --no-progress --exclude ^https?://
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'**/*.md'
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failIfEmpty: false
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env:

docs/DeploymentGuide.md

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</details>
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<details>
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<summary><b>Deploy in VS Code</b></summary>
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<summary><b>Deploy in VS Code Dev Containers</b></summary>
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### VS Code Dev Containers
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<details>
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<summary><b>Configurable Deployment Settings</b></summary>
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When you start the deployment, most parameters will have **default values**, but you can update the following settings by following the steps [here](../docs/CustomizingAzdParameters.md):
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| **Setting** | **Description** | **Default Value** |
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| ------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------- |
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| **Azure Region** | The region where resources will be created. | East US |
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| **Azure AI Content Understanding Location** | Location for the **Content Understanding** service. | Sweden Central |
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| **Secondary Location** | A **less busy** region for **Azure Cosmos DB**, useful in case of availability constraints. | eastus2 |
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| **Deployment Type** | Select from a drop-down list. | GlobalStandard |
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| **GPT Model** | Choose from **gpt-4o**. | gpt-4o |
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| **GPT Model Version** | GPT model version used in the deployment. | 2024-08-06 |
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| **GPT Model Deployment Capacity** | Configure capacity for **GPT models**. | 30k |
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| **Use Local Build** | Boolean flag to determine if local container builds should be used. | false |
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| **Image Tag** | Image version for deployment (allowed values: `latest`, `dev`, `hotfix`). | latest |
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| **Existing Log Analytics Workspace** | To reuse an existing Log Analytics Workspace ID instead of creating a new one. | *(none)* |
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| **Existing Azure AI Foundry Project** | To reuse an existing Azure AI Foundry Project ID instead of creating a new one. | *(none)* |
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When you start the deployment, most parameters will have **default values**, but you can update the following settings by following the steps [here](../docs/CustomizingAzdParameters.md)
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</details>
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- **Linux/macOS**:
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```bash
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cd ./infra/scripts/
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./docker-build.sh
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```
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- **Windows (PowerShell)**:
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```powershell
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cd .\infra\scripts\
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.\docker-build.ps1
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```
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- **Execute Script to registering Schemas**
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- Move the folder to samples/schemas in ContentProcessorApi - [/src/ContentProcessorApi/samples/schemas](/src/ContentProcessorApi/samples/schemas)
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Bash
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Git Bash
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```bash
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cd src/ContentProcessorAPI/samples/schemas
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```
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Powershell
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```Powershell
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cd .\src\ContentProcessorAPI\samples\schemas\
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```
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- Then use below command
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Git Bash
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```bash
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./register_schema.sh https://<< API Service Endpoint>>/schemavault/ schema_info_sh.json
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```
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Windows
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Powershell
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```Powershell
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./register_schema.ps1 https://<< API Service Endpoint>>/schemavault/ .\schema_info_ps1.json
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```
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- **Verify Results**
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![schema file registration](./images/SchemaFileRegistration.png)
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3. **Import Sample Data**
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4. **Deleting Resources After a Failed Deployment**
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- Follow steps in [Delete Resource Group](./DeleteResourceGroup.md) if your deployment fails and/or you need to clean up the resources.
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## Running the application
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To help you get started, here's the [Sample Workflow](./SampleWorkflow.md) you can follow to try it out.
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## Environment configuration for local development & debugging
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**Creatign env file**
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**Creating env file**
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> Navigate to the `src` folder of the project.
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docs/SampleWorkflow.md

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# Sample Workflow
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To help you get started, here’s a **sample process** you can follow in the app.
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## **Process**
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> Note: Download sample data files for **Invoices** and **Property Claims** from [here](../src/ContentProcessorAPI/samples).
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### **API Documentation**
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- Click on **API Documentation** to view and explore the available API endpoints and their details.
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### **Upload**
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> Note: Average response time is 01 minute.
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_Sample Operations:_
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- Select the **Schema** under the Processing Queue pane.
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- Click on the **Import Content** button.
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- Choose a file from the downloaded list for data extraction corresponding to the **Schema** selected.
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- Click the **Upload** button.
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### **Review and Process**
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_Sample Operation:_
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- Once the file status is marked as completed, click on the file.
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- Once the batch processing is done, the file is ready to review and the extracted data is displayed in the **Output Review** pane and corresponding file is visible in the **Source Document** pane.
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- Edit any incorrect data in the JSON which is shown in the **Output Review** pane under **Extracted Results** tab.
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- Add notes under the **Comments** and save the changes by clicking on the **Save** button.
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- You can view the process steps in the **Output Review** pane under the **Process Steps** tab and expand the extract, Map, and evaluate sections to see the outputs from each process step.
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![Application](images/sampleworkflow1.png)
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### **Delete**
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_Sample operation:_
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- Click the **three-dot menu** at the end of the row to expand options, then select **Delete** to remove the item.
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This structured approach ensures that users can efficiently extract key information, and organize structured outputs for easy search and analysis.

docs/images/sampleworkflow1.png

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infra/main.bicep

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metadata description = 'Bicep template to deploy the Content Processing Solution Accelerator with AVM compliance.'
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// ========== Parameters ========== //
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@description('Required. Name of the solution to deploy.')
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@minLength(3)
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@maxLength(20)
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@description('Required. Name of the solution to deploy. This should be 3-20 characters long.')
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param solutionName string = 'cps'
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@description('Optional. Location for all Resources.')
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param location string = resourceGroup().location

infra/main.json

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"_generator": {
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"name": "bicep",
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"version": "0.37.4.10188",
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"templateHash": "14310770063360835011"
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"templateHash": "11985360537269850534"
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},
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"name": "Content Processing Solution Accelerator",
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"description": "Bicep template to deploy the Content Processing Solution Accelerator with AVM compliance."
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"solutionName": {
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"type": "string",
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"defaultValue": "cps",
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"minLength": 3,
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"maxLength": 20,
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"metadata": {
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"description": "Required. Name of the solution to deploy."
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"description": "Required. Name of the solution to deploy. This should be 3-20 characters long:"
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}
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},
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"location": {
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"avmContainerApp",
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"avmContainerApp_API",
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"avmManagedIdentity",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').storageBlob)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').storageQueue)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').storageBlob)]",
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"virtualNetwork"
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"dependsOn": [
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"avmManagedIdentity",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').aiServices)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').contentUnderstanding)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').openAI)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').cognitiveServices)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').aiServices)]",
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"logAnalyticsWorkspace",
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"virtualNetwork"
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"avmManagedIdentity",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').contentUnderstanding)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').cognitiveServices)]",
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"[format('avmPrivateDnsZones[{0}]', variables('dnsZoneIndex').cognitiveServices)]",
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"virtualNetwork"
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]
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},

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