1. Real-time Sentiment Analysis in Amazon Connect


    To set up real-time sentiment analysis in Amazon Connect, you need to start by integrating Amazon Connect with AWS resources like AWS Lambda and Amazon Comprehend for performing the sentiment analysis on live calls. Pulumi allows you to define and deploy cloud resources with real programming languages, and for this purpose, we can use Pulumi with Python.

    The general steps for real-time sentiment analysis in Amazon Connect using Pulumi will be:

    1. Create an Amazon Connect instance if you don't already have one.
    2. Set up a contact flow to handle the incoming calls.
    3. Create a Lambda function that will get the real-time transcription of calls from Amazon Connect and perform sentiment analysis using Amazon Comprehend.
    4. Integrate the Lambda function with Amazon Connect.
    5. Create an IAM role with the necessary permissions policy that allows Amazon Connect to invoke the Lambda function.

    Here is a Pulumi program in Python that does just that:

    import pulumi import pulumi_aws as aws # Replace the following variables with your specific IDs/ARNs/values connect_instance_id = "your-connect-instance-id" lambda_role_arn = "arn:aws:iam::123456789012:role/AWSLambdaConnectExecutionRole" # Create a new Lambda function for sentiment analysis sentiment_analysis_lambda = aws.lambda_.Function("sentimentAnalysisLambda", role=lambda_role_arn, runtime="python3.8", handler="lambda_function.lambda_handler", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./sentiment-analysis-lambda") }), # Environment variables may include Amazon Connect instance ID or other configs environment={ "variables": { "CONNECT_INSTANCE_ID": connect_instance_id, "COMPREHEND_LANGUAGE_CODE": "en" } }) # Create an AWS Lambda Permission to allow Amazon Connect to invoke the Lambda function lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", function=sentiment_analysis_lambda.arn, principal="connect.amazonaws.com", source_arn=f"arn:aws:connect:{aws.get_region().name}:{aws.get_caller_identity().account_id}:instance/{connect_instance_id}" ) # This Pulumi program assumes the following: # 1. The 'sentimentAnalysisLambda' Lambda function code is already available within the './sentiment-analysis-lambda' directory. # 2. The AWSLambdaConnectExecutionRole IAM role exists and has the necessary permissions. # 3. The Amazon Connect instance is already set up with the correct instance ID. # The Python lambda handler function would need code that # uses AWS Comprehend for sentiment analysis of the transcribed # call text, and return the sentiment result to Amazon Connect. # Outputs pulumi.export("sentiment_analysis_lambda", sentiment_analysis_lambda.arn) pulumi.export("lambda_invoke_permission", lambda_permission.id)

    In this program, we define and deploy two AWS resources using Pulumi:

    1. aws.lambda_.Function: This creates an AWS Lambda function with the name sentimentAnalysisLambda. It expects that you have a Python file in the ./sentiment-analysis-lambda directory that contains the code to handle sentiment analysis logic. The handler for the function is set as "lambda_function.lambda_handler", which means Pulumi expects a Python file named lambda_function.py with a function lambda_handler(event, context).

    2. aws.lambda_.Permission: This resource gives Amazon Connect the permission to invoke the Lambda function that we created earlier. Notice how we format source_arn dynamically with the aws.get_region and aws.get_caller_identity calls, which get the AWS account's region and account ID, respectively.

    Remember to include import pulumi_aws as aws at the beginning of your Pulumi program, as it contains the necessary AWS resources like Lambda and S3.

    This Pulumi program is a base for setting up the sentiment analysis integration. You would also need an Amazon Comprehend service set up and a way to hook into the real-time data stream from Amazon Connect, typically using Amazon Kinesis. Your Lambda function's code will take care of calling Amazon Comprehend to analyze the sentiment.

    Please ensure that you have AWS CLI configured correctly with the necessary permissions and Pulumi CLI installed to run this program. Also, remember to adjust the variables and fill in with your specific values where placeholders are provided.

    Note: Above code outlines the setup of the Lambda function for handling sentiment analysis and giving it necessary permissions but does not cover the entirety of setting up all the components in Amazon Connect (such as contact flows, integrating Amazon Lex bots, etc.). You may need to follow detailed Amazon Connect setup documentation for those aspects.