1. AI-powered Content Moderation using Cosmos DB for Speedy Transactions


    To implement AI-powered content moderation with Azure Cosmos DB ensuring speedy transactions, we will create the necessary Azure resources using Pulumi. Azure Cosmos DB is a globally distributed, multi-model database service that can scale elastically and independently across throughput and storage across any number of Azure's geographic regions. It supports schema-less data, which makes it suitable for managing the flexible datasets required for content moderation tasks.

    The AI component for content moderation could involve using pre-built services like Azure Cognitive Services for text analytics, sentiment analysis, or custom models deployed on Azure Machine Learning. However, implementing the AI logic itself is beyond the scope of Pulumi but could be integrated through Azure functions or event-driven mechanisms triggered by Cosmos DB.

    Here's a simple Pulumi program to provision an Azure Cosmos DB account, a SQL API database, and a container suitable for content moderation purposes:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure CosmosDB Account account = azure_native.documentdb.DatabaseAccount( "cosmosdb_account", resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", locations=[{ "location_name": resource_group.location, }], ) # Create a Cosmos DB SQL Database database_name = "contentModerationDB" database = azure_native.documentdb.SqlResourceSqlDatabase( "sql_database", resource_group_name=resource_group.name, account_name=account.name, resource=azure_native.documentdb.SqlDatabaseResourceArgs( id=database_name, ), ) # Create a container for moderated content container_name = "moderatedContentContainer" container = azure_native.documentdb.SqlResourceSqlContainer( "sql_container", resource_group_name=resource_group.name, account_name=account.name, database_name=database.name, resource=azure_native.documentdb.SqlContainerResourceArgs( id=container_name, partition_key=azure_native.documentdb.ContainerPartitionKeyArgs( paths=["/contentId"], kind="Hash" ), ), options=azure_native.documentdb.CreateUpdateOptionsArgs( throughput=400, # Set throughput value as needed ), ) pulumi.export("account_endpoint", account.name.apply(lambda name: f"https://{name}.documents.azure.com:443/")) pulumi.export("database_id", database.id) pulumi.export("container_id", container.id)


    1. We start by creating a Resource Group, which is a logical container that holds related Azure resources. It simplifies resource management, billing, and access control.

    2. We then provision an Azure Cosmos DB account with the DatabaseAccount resource. The database_account_offer_type is set to Standard, which is a typical configuration for various workloads.

    3. Within that account, a Cosmos DB SQL API database is defined with the SqlResourceSqlDatabase resource. We have named it contentModerationDB. This database will host containers (aka collections or tables) where content can be stored and managed.

    4. We then create a Cosmos DB container named moderatedContentContainer using the SqlResourceSqlContainer resource. This container will hold the actual content items. Each item is expected to have a contentId field, which we specify as the partition key for scalability and performance.

    5. The CreateUpdateOptionsArgs object allows us to set the throughput (in RU/s) for the container, which can be fine-tuned based on performance requirements and anticipated workload.

    6. Finally, we export some important pieces of information:

      • The endpoint URL of the Cosmos DB account (account_endpoint) which you would use in your application to connect to the DB.
      • The unique identifiers of the created database (database_id) and container (container_id), which might be needed for further operations or reference in your application.

    This program assumes that Azure configurations for Pulumi are already set up. Once the Pulumi program is executed, it deploys the described resources in Azure. You can then integrate AI-powered content moderation logic using additional Azure services and connect to the provisioned Cosmos DB resources to store and retrieve moderated content.