1. Session Store for AI Chatbots with DocumentDB

    Python

    If you're looking to implement a session store for an AI chatbot using Amazon DocumentDB, Pulumi provides you the capability to define the infrastructure needed in a programming language like Python. Amazon DocumentDB is a fully managed, scalable, NoSQL database service by AWS with support for MongoDB workloads. It's appropriate for handling session information due to its fast performance, scalability, and flexibility in storing schema-less data.

    In the following program, we will define the necessary AWS infrastructure components using Pulumi and Python. We will set up a DocumentDB cluster and provide it with a SubnetGroup, which decides in which subnets your DocumentDB instances will operate. Here's how you can do it:

    1. We will create an Amazon DocumentDB cluster for storing the session data.
    2. We will also create a Subnet Group, necessary for the DocumentDB cluster to define the subnets within your existing VPC where the cluster can operate.
    3. Finally, we'll use an Instance to create a compute instance within our cluster to interact with the database.

    First, ensure you have Pulumi installed and your AWS credentials configured. Then, you can use the program below to create your infrastructure:

    import pulumi import pulumi_aws as aws # Create a DocumentDB subnet group. The subnets must be in at least two different Availability Zones in the same region. docdb_subnet_group = aws.docdb.SubnetGroup("my-docdb-subnet-group", subnet_ids=[ "subnet-abcde012", # Replace with your actual subnet id "subnet-bcde012a", # Replace with your actual subnet id # ... add additional subnets if necessary ]) # Create a DocumentDB cluster docdb_cluster = aws.docdb.Cluster("my-docdb-cluster", cluster_identifier="my-cluster", engine="docdb", master_username="myusername", # Replace with your actual username master_password="mypassword", # Replace with your actual password db_subnet_group_name=docdb_subnet_group.name, skip_final_snapshot=True, availability_zones=[ "us-west-2a", "us-west-2b", # ... match the number of availability zones with your subnet count ], backup_retention_period=5, preferred_backup_window="07:00-09:00", preferred_maintenance_window="sat:08:00-sat:09:00", vpc_security_group_ids=[ "sg-123456", # Replace with your actual VPC security group id ]) # Create an instance within the cluster docdb_instance = aws.docdb.ClusterInstance("my-docdb-instance", cluster_identifier=docdb_cluster.cluster_identifier, engine="docdb", instance_class="db.r5.large") # Export the cluster endpoint to be used by your application pulumi.export("documentdb_endpoint", docdb_cluster.endpoint)

    In the code provided:

    • Replace subnet-abcde012 and subnet-bcde012a with your actual subnet IDs. These should be part of the VPC where you want your DocumentDB to reside.
    • Fill in myusername and mypassword with your desired administrative credentials for the DocumentDB.
    • Replace sg-123456 with your security group ID to determine network access rules.

    Once you complete your Pulumi program, you can deploy your infrastructure by running the following commands in the same directory as your Python script:

    pulumi up

    The pulumi up command will show you a preview of the resources Pulumi will create. If everything looks correct, you can proceed with the deployment. After deploying, Pulumi will output the connection endpoint of the DocumentDB cluster, which you can use to connect your AI Chatbot application and store session data.

    For more detailed documentation on the Pulumi resources used:


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