1. Voice Bot Integration for Amazon Connect


    Integrating a voice bot with Amazon Connect involves several steps that can be facilitated by Pulumi's infrastructure as code approach. Amazon Connect is a cloud-based contact center solution, and Amazon Lex is a service for building conversational interfaces into any application using voice and text. Here is how you could create Amazon Connect with a bot integration using Pulumi and AWS SDK for Python (Boto3):

    1. Set up the Amazon Connect instance: You would start by creating an Amazon Connect instance. This acts as the container for your contact center.

    2. Design a contact flow: Contact flows are the interactive paths that a caller experiences, and they can include interactions with the Lex bot.

    3. Create a Lex bot: This bot will handle dialogue with the caller. You design the conversations that the bot can handle and specify how it will interact with the caller.

    4. Associate the Lex bot with Amazon Connect: This step involves configuring Amazon Connect to use the Lex bot as part of a contact flow.

    5. Deploy and Test: After everything is set up, you will deploy the changes and test to ensure that the bot is correctly integrated with Amazon Connect and is handling calls as expected.

    Here is a Python program using Pulumi to set up a simple Amazon Connect instance and integrate an Amazon Lex bot:

    import pulumi import pulumi_aws as aws # Create an Amazon Connect instance connect_instance = aws.connect.Instance("exampleConnectInstance", identity_management_type="CONNECT_MANAGED", inbound_calls_enabled=True, outbound_calls_enabled=True) # Define the Amazon Lex bot (the definition should be extended to include intent, clarification prompt, etc.) lex_bot = aws.lex.Bot("exampleLexBot", locale=aws.lex.BotLocaleArgs( value="en-US", ), child_directed=False, create_version=False, description="An example Lex bot.", abort_statement=aws.lex.BotAbortStatementArgs( messages=[aws.lex.BotAbortStatementMessageArgs( content="Sorry, I'm not able to assist at this time", content_type="PlainText", )], ), intents=[], # Further configuration for intents, slots, and prompts can be added here ) # Associate the bot with the Amazon Connect instance connect_bot_association = aws.connect.BotAssociation("exampleBotAssociation", instance_id=connect_instance.id, lexBot=aws.connect.BotAssociationLexBotArgs( name=lex_bot.name, lex_region="us-east-1", # Change this to the region where you have deployed the Lex bot )) # Register outputs pulumi.export("connect_instance_arn", connect_instance.arn) pulumi.export("lex_bot_name", lex_bot.name) pulumi.export("connect_bot_association_id", connect_bot_association.id)

    Here's what each part of the code does:

    • We create an Amazon Connect instance with aws.connect.Instance. This instance is configured to handle both inbound and outbound calls.

    • A Lex bot is defined using aws.lex.Bot. In a real-world example, you'd add more configuration to specify the bot's behavior, such as its intents (actions it can perform) and conversation flows.

    • The aws.connect.BotAssociation resource associates the Lex bot with the Connect instance. Without this step, the bot would not be usable within Amazon Connect.

    • Finally, we export key identifiers such as the ARN for the Connect instance and the Lex bot name, which can be helpful for future reference or integration with other resources.

    Remember, additional steps like designing and deploying the actual contact flow within Amazon Connect are not covered here. You would typically do this in the AWS Management Console, as Pulumi does not yet have abstractions for designing the flow via code. However, once designed, you could deploy and manage the contact flows programmatically using the AWS SDK (Boto3) if needed.