1. Scalable Graph Database for AI Knowledge Graphs on Cosmos DB


    In Azure, Cosmos DB is a globally distributed, multi-model database service that provides scale, performance, and ease of development with its range of APIs for different data models. In particular, Cosmos DB offers an API optimized for graph databases, which is perfect for scenarios like building AI knowledge graphs where you need efficient query capabilities over complex data relationships. The Cosmos DB Gremlin API is one of these APIs specifically designed for graph databases.

    To create a scalable AI knowledge graph on Cosmos DB using Pulumi, we'll start by creating an Azure resource group, then we'll proceed to set up a Cosmos DB account with the Gremlin (Graph) API as the default experience. After that, we'll define a graph database and a graph within that database.

    In this program, we will be using the azure-native Pulumi package as it contains the most recent and comprehensive Azure resources, compared to the classic azure package.

    Here is a Pulumi program that sets up a scalable Graph Database for AI Knowledge Graphs on Azure Cosmos DB:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import documentdb as cosmosdb # Create a resource group for all resources resource_group = azure_native.resources.ResourceGroup('ai_knowledge_graph_rg') # Create an Azure CosmosDB account with the Gremlin (Graph) API as the default experience cosmos_db_account = cosmosdb.DatabaseAccount('cosmos_db_account', resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type=cosmosdb.DatabaseAccountOfferType.STANDARD, capabilities=[cosmosdb.CapabilityArgs(name='EnableGremlin')], consistency_policy=cosmosdb.ConsistencyPolicyArgs( default_consistency_level=cosmosdb.DefaultConsistencyLevel.SESSION, ), locations=[cosmosdb.LocationArgs( location_name=resource_group.location, failover_priority=0, )], ) # Define a database within our Cosmos DB account graph_database = cosmosdb.SqlResourceSqlDatabase('graph_database', resource_group_name=resource_group.name, account_name=cosmos_db_account.name, resource=cosmosdb.SqlDatabaseResourceArgs(id='knowledge_graph_db'), options=cosmosdb.CreateUpdateOptionsArgs( throughput=400, # Set throughput (RU/s) - adjust as needed for scalability ), ) # Define a graph within the database knowledge_graph = cosmosdb.SqlResourceSqlContainer('knowledge_graph', resource_group_name=resource_group.name, account_name=cosmos_db_account.name, database_name=graph_database.name, resource=cosmosdb.SqlContainerResourceArgs(id='ai_knowledge_graph'), options=cosmosdb.CreateUpdateOptionsArgs( throughput=400, # Set throughput (RU/s) for the graph itself - adjust as needed ), ) # Export the endpoint and primary key of the Cosmos DB account primary_master_key = cosmos_db_account.primary_master_key.apply( lambda key: key if key else pulumi.Output.secret("<primary-master-key>").apply(pulumi.secret) ) # Export the Cosmos DB Graph endpoint for connecting your applications pulumi.export('cosmos_db_endpoint', cosmos_db_account.document_endpoint) pulumi.export('cosmos_db_primary_master_key', primary_master_key)

    Explanation of the program:

    • We import the necessary Pulumi packages for Azure.
    • We create an Azure resource group, which is a container for grouping related resources for an Azure solution.
    • We set up the Cosmos DB account specifying EnableGremlin capability to ensure it's suitable for graph databases.
    • We then create a graph database named knowledge_graph_db and specify its throughput. The throughput (measured in RU/s or Request Units per second) is a measure of the performance level of the database, which you can scale as needed.
    • Inside the database, we define a container named ai_knowledge_graph, which will store the graph data.
    • We output the Cosmos DB endpoint and the primary master key, which are needed to connect to the database from your applications.

    We've defined throughput parameters directly in the options for simplicity, but you might want to manage this separately or include autoscaling settings as your application's needs evolve.

    Once this Pulumi program is deployed, it creates an Azure Cosmos DB account and a graph database ready to be used for storing and querying AI knowledge graphs at scale with the Gremlin API. To manage and interact with the graph, you will typically use Gremlin query language through a Gremlin-compatible client in the programming language of your choice.