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Copy file name to clipboardExpand all lines: docs/deployment/autoscale_sku.md
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The Azure Functions Service Plan Autoscale settings are defined in the file located at `/infra/core/host/functions/functions.tf`. These settings enable automatic scaling of the Azure Functions Service Plan based on CPU usage metrics.
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-**Time Window:**`5 minutes`
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-**Scaling Action:** Decrease capacity by `2` with a cooldown of `2 minutes`.
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## App Service Plan Autoscale for Enrichment App
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### Overview
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### Customization
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To customize the App Service Plan Autoscale settings, modify the parameters mentioned above in the specified Terraform file. And Run the `make infrastructure` command.
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To customize the App Service Plan Autoscale settings, modify the parameters mentioned above in the specified terraform files. And Run the `make infrastructure` command.
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# SKU Settings Documentation
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### Overview
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The SKU settings for all Service Plans are defined in the file located at `/infra/main.tf`. The SKU (Stock Keeping Unit) represents the pricing tier or plan for your App Service. It defines the performance, features, and capacity of the App Service.
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The SKU settings for all Service Plans are defined in the file located at `/infra/variables.tf`. The SKU (Stock Keeping Unit) represents the pricing tier or plan for your App Service. It defines the performance, features, and capacity of the App Service.
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More information can be found [here.](https://azure.microsoft.com/en-us/pricing/details/app-service/windows/#purchase-options)
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## Web App Service Plan SKU
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**File Location:**`/infra/variables.tf`
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**File Location:**`/infra/main.tf`
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#### SKU Settings
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-**Name:**`S1`
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-**Capacity:**`3`
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### SKU Settings
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-**appServiceSkuSize**`S1`
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-**appServiceSkuTier**`Standard`
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## Functions Service Plan SKU
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**File Location:**`/infra/variables.tf`
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**File Location:**`/infra/main.tf`
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#### SKU Settings
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### SKU Settings
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-**Name:**`S2`
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-**Capacity:**`2`
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-**functionsAppSkuSize**`S2`
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-**functionsAppSkuTie:**`Standard`
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## Enrichment App Service Plan SKU
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**File Location:**`/infra/variables.tf`
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**File Location:**`/infra/main.tf`
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### SKU Settings
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#### SKU Settings
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-**Name:**`P1v3`
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-**Tier:**`PremiumV3`
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-**Size:**`P1v3`
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-**Family:**`Pv3`
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-**Capacity:**`1`
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-**enrichmentAppServiceSkuSize**`P1v3`
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-**enrichmentAppServiceSkuTier**`PremiumV3`
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### Enrichment Message Dequeue Parameter
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There exist a property that can be set in the local.env file called `DEQUEUE_MESSAGE_BATCH_SIZE` and is defaulted in the `infra/main.tf` and `app/enrichment/app.py` to the value of **3**. This means the app will process 3 messages from the queue at a time. This is found to be the most optimal with the existing configuration but can be increased if you also increase the enrichment app service SKU. It is important to note that there will be issues if it is increased more than the app service SKU can handle.
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**Note:** Adjusting the scale or Tier can cause outages until the redeployment occurs.
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### Steps to Scale Up:
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### Steps to Scale Up
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>1.**Sign in to the Azure Portal:**
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> - Open a web browser and navigate to the [Azure Portal](https://portal.azure.com/).
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> - Log in with your Azure account credentials.
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>2.**Navigate to the App Service:**
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> - In the left navigation pane, select "App Services."
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> - Click on the specific App Service you want to scale.
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>3.**Access the Scale Up Blade:**
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> - In the App Service menu, find and click on "Scale up (App Service plan)" in the left sidebar.
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>4.**Choose a New Pricing Tier:**
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> - On the "Scale Up" blade, you'll see different pricing tiers representing various levels of resources.
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> - Select the desired pricing tier that corresponds to the scale you need.
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>5.**Review and Apply Changes:**
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> - Review the information about the selected pricing tier, including its features and costs.
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> - Click the "Apply" or "Save" button to apply the changes.
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### Considerations
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### Considerations:
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-**Cost Implications:**
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- Be aware of the cost implications associated with higher pricing tiers. Review the Azure Pricing documentation for details on costs.
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-**Performance Impact:**
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- Scaling up provides additional resources, potentially improving performance. However, it's essential to assess whether your application benefits from the increased resources.
Copy file name to clipboardExpand all lines: docs/deployment/deployment.md
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The IA Accelerator needs to be sized appropriately based on your use case. Please review our [Sizing Estimator](/docs/costestimator.md) to help find the configuration that fits your needs.
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To change the size of components deployed, make changes in the [Main Terraform](/infra/main.tf) file.
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To change the size of components deployed, make changes in the [Terraform Variables](/infra/variables.tf) file.
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Once you have completed the Sizing Estimator and sized your deployment appropriately, please move on to the Configuring your Environment step.
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ENABLE_DEV_CODE | No | Defaults to `false`. It is not recommended to enable this flag, it is for development testing scenarios only.
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APPLICATION_TITLE | No | Defaults to "". Providing a value for this parameter will replace the Information Assistant's title in the black banner at the top of the UX.
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ENTRA_OWNERS | No | Defaults to "". Additional user id's you wish to assign as owners of created Azure Entra objects by way of assign to a security group.
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MAX_CSV_FILE_SIZE | No | Defaults to 20 (MB's) for the maximum file size for an uploaded CSV
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SERVICE_MANAGEMENT_REFERENCE | No | Defaults to "". Sets the service management reference value on Azure Entra objects created by Information Assistant if required by your organization.
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MAX_CSV_FILE_SIZE | Yes | Defaults to 20. This value limits the size of CSV files in MBs that will be supported for upload in the Tabular Data Assistant UX feature.
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PASSWORD_LIFETIME | No | Defaults to 365. The number of days that passwords associated with created identities are set to expire after creation. Change this setting if needed to conform to you policy requirements
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## Log into Azure using the Azure CLI
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deploy Deploy infrastructure and application code
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build Build application code
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infrastructure Deploy infrastructure
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extract-env Extract infrastructure.env file from TF output
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extract-env Extract infrastructure.env file from Terraform output
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deploy-webapp Deploys the web app code to Azure App Service
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deploy-functions Deploys the functioncode to Azure Function Host
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deploy-enrichments Deploys the web app code to Azure App Service
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deploy-search-indexes Deploy search indexes
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extract-env-debug-webapp Extract infrastructure.debug.env file from TF output
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extract-env-debug-functions Extract local.settings.json to debug functions from TF output
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extract-env-debug-webapp Extract infrastructure.debug.env file from Terraform output
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extract-env-debug-functions Extract local.settings.json to debug functions from Terraform output
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functional-tests Run functional tests to check the processing pipeline is working
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merge-databases Upgrade from bicep to terraform
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import-state import state of current services to TF state
Copy file name to clipboardExpand all lines: docs/features/architectural_decisions.md
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Initially, we explored Azure AI Search's built-in skillset for tasks like entity recognition and key phrase extraction. However, due to the additional overhead of utilizing the skillset from Azure AI Search, we opted for custom data processing to extract key phrases and entities such as organizations, locations, and events. This approach enriched the search index by providing additional metadata and context, thereby enhancing retrieval effectiveness. Additionally, we employed embeddings to capture semantic relationships and contextual nuances, improving our understanding of textual data.
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To generate embeddings, we empowered users to choose the embedding model that best suits their content and use case, acknowledging that a one-size-fits-all approach is not ideal. Users have the flexibility to opt for the closed-source Azure Open AI embedding or one of the open-source embedding models, including the multilingual embedding model.
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To generate embeddings, we empowered users to choose the embedding model that best suits their content and use case, acknowledging that a one-size-fits-all approach is not ideal. Users have the flexibility to opt for the closed-source Azure OpenAI embedding or one of the open-source embedding models, including the multilingual embedding model.
Copy file name to clipboardExpand all lines: docs/knownissues.md
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### Solution
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These are only in the Logic App Preview Designer. Switching to the Generally Available Designer will resolve these errors. They are purely visual errors in the Preview Designer and have no impact on how the Logic App functions.
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---
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## Error: CredentialInvalidLifetimeAsPerAppPolicy: Credential lifetime exceeds the max value allowed as per assigned
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### Solution
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Your organization's policy places a limit on the lifetime of an identities password. In your copy of Local.env there is a setting called PASSWORD_LIFETIME. This value is used when creating or updating the identity password and has a default value of the number of days the password will exist before expiring. Change this value to a number of days that your organization allows.
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To view the value after deploying go the Microsoft Entra ID page from the Azure Portal home page. Then search your tenant for infoasst_mgmt_access_<your-5-character-suffix> as shown in the image below.
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Next click on the App Registration value, and then the page will open for that applciuation registration. Then select Clients & Secrets from the left menu. You will then see the expiry date of the password that was applied through Terraform.
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