1. Graph-Based AI Workflows with Gremlin API on Cosmos DB


    Azure Cosmos DB is a globally distributed, multi-model database service that provides the means to work with data using a variety of APIs for different data models. For graph-based AI workflows, you can use the Gremlin API provided by Cosmos DB to manage and query graph data.

    Here is a Pulumi program in Python that provisions an Azure Cosmos DB account configured to use the Gremlin API for storing and managing graph-based data. This setup can be utilized for various AI workflows where graph databases are beneficial, such as recommendation engines or social network analysis.

    Let's break down the steps:

    1. Import the required modules from the Pulumi SDK.
    2. Configure Azure resources like resource groups, Cosmos DB account, and a Gremlin database with a graph.
    3. Export the necessary outputs, such as the Cosmos DB account endpoint and primary keys, which you'll need to access your database outside of Pulumi.

    Below is the Pulumi program that accomplishes the above steps:

    import pulumi import pulumi_azure_native as azure_native # Set up a resource group for our Cosmos DB resources resource_group = azure_native.resources.ResourceGroup('ai-workflows-rg') # Create an Azure Cosmos DB account configured for the Gremlin API cosmosdb_account = azure_native.documentdb.DatabaseAccount('gremlin-ai-db-account', resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", # Set the offer type for your account capabilities=[{ "name": "EnableGremlin" # Enable the Gremlin API }] ) # Create a Gremlin database within the Cosmos DB Account gremlin_database = azure_native.documentdb.GremlinResourceGremlinDatabase('ai-gremlin-database', resource_group_name=resource_group.name, account_name=cosmosdb_account.name, resource={ "id": "graph-database" # Set the unique name for the Gremlin database } ) # Create a Gremlin graph within the Gremlin database gremlin_graph = azure_native.documentdb.GremlinResourceGremlinGraph('ai-gremlin-graph', resource_group_name=resource_group.name, account_name=cosmosdb_account.name, database_name=gremlin_database.name, resource={ "id": "graph" # Set a unique name for the Gremlin graph }, options={ "throughput": 400 # Set the throughput for your graph (measured in RU/s) } ) # Export the Cosmos DB account endpoint and primary master key # These will be needed to connect to your database from your application pulumi.export('cosmosdb_endpoint', cosmosdb_account.document_endpoint) pulumi.export('primary_master_key', pulumi.Output.secret(cosmosdb_account.primary_master_key))

    In the program, an instance of azure_native.resources.ResourceGroup is created to group all Azure resources in one logical collection. Then azure_native.documentdb.DatabaseAccount is created with capabilities set to enable the Gremlin API, which is necessary to interface with the graph-based database.

    With the database account in place, azure_native.documentdb.GremlinResourceGremlinDatabase creates a Gremlin-specific database instance, and azure_native.documentdb.GremlinResourceGremlinGraph provides the actual graph resource within that database, which can be used for creating vertices and edges for your graph.

    The options section of the graph resource allows you to specify performance throughput settings, where you can adjust it based on the expected workload.

    Lastly, the Pulumi program exports the Cosmos DB endpoint and primary master key as outputs. These can be used to connect to the database from any application that is leveraging Gremlin-based queries for AI or any other purpose.

    Remember to replace account_name and resource IDs with unique names as needed, and adjust your database settings like throughput according to your specific requirements.