1. Corporate Knowledge Base Creation using AWS Kendra.


    Creating a corporate knowledge base using AWS Kendra involves several steps. AWS Kendra is an intelligent search service powered by machine learning, that enables organizations to provide more relevant information to customers and employees, when they need it.

    To construct this with Pulumi in Python:

    • We'll set up an AWS Kendra Index. An index holds all the data that is searchable.
    • Then, we'll attach data sources to the index. These can be various repositories like Amazon S3 buckets, relational databases, or other services containing your corporate data.
    • We can also create FAQs and add them to the index for quick searching of common questions.
    • Optionally, we can create an AWS Kendra Experience which crafts a user experience around the index by configuring layout and interactions.

    Below is a detailed Pulumi Python program that sets up a basic AWS Kendra knowledge base:

    import pulumi import pulumi_aws as aws # Create a Kendra Index - fundamental for the knowledge base kendra_index = aws.kendra.Index("corporate-knowledge-base-index", name="CorporateKnowledgeBase", # Giving the index a descriptive name role_arn=pulumi.Output.secret("<ARN_for_AWS_role_with_permissions>"), # ARN of the role with needed permissions for Kendra description="Index for Corporate Knowledge Base", # Optional description edition="DEVELOPER_EDITION", # Edition based on the scale (DEVELOPER_EDITION or ENTERPRISE_EDITION) ) # Attach an S3 bucket data source to the Kendra Index s3_data_source = aws.kendra.DataSource("s3-data-source", # This DataSource will be linked to our previously created index index_id=kendra_index.id, name="CorporateKnowledgeBaseS3DataSource", type="S3", role_arn=pulumi.Output.secret("<ARN_for_AWS_role_with_permissions_to_access_S3>"), data_source_configuration={ "s3Configuration": { "bucketName": "<Name_of_your_S3_bucket_with_data>", }, }, ) # Create an FAQ and associate it with the Index faq = aws.kendra.Faq("corporate-faq", index_id=kendra_index.id, name="CorporateFAQ", role_arn=pulumi.Output.secret("<ARN_for_AWS_role_with_permissions_to_access_S3>"), s3_path={ "bucket": "<Name_of_S3_bucket_with_FAQ_data>", # Your bucket with FAQ data "key": "<Path_to_FAQ_file_in_S3_bucket>", # Path to FAQ documents in S3 bucket }, file_format="CSV", # The format of the FAQ (CSV or JSON) ) # Define an AWS Kendra Experience for end-users experience = aws.kendra.Experience("corporate-advisor-experience", index_id=kendra_index.id, name="CorporateAdvisor", # Name of the Experience component role_arn=pulumi.Output.secret("<ARN_for_AWS_role>"), configuration={ # Here we can configure aspects like user identity mapping if required "userIdentityConfiguration": { "identityAttributeName": "UserId", }, }, ) # Exports to provide outputs that can be used to interact with or manage the resources pulumi.export("index_id", kendra_index.id) pulumi.export("s3_data_source_name", s3_data_source.name) pulumi.export("faq_name", faq.name) pulumi.export("experience_id", experience.id)

    Each step corresponds to a resource created and managed by Pulumi in the AWS Kendra service:

    • We start by creating a kendra.Index, which acts as the central repository where all searchable content will reside.
    • Next, a data source, in this case, an S3 bucket, is attached to the index using kendra.DataSource. This way, the documents and information stored in the specified S3 bucket become searchable through Kendra.
    • FAQs, which are common questions and answers, are added as a resource via kendra.Faq, thus enabling quick look-up for these in the knowledge base.
    • An Experience is an optional resource for creating a user interface around the Kendra index. This could be used to provide users with a web interface to search and interact with the knowledge base.

    Finally, we export the IDs and names of the created resources, providing handy references for managing the knowledge base outside of Pulumi.

    Please replace placeholders like <ARN_for_AWS_role_with_permissions> and <Name_of_your_S3_bucket_with_data> with actual values corresponding to your AWS environment and resources.

    To run this Pulumi program:

    1. Install Pulumi and set up the AWS provider.
    2. Save this code to a file named __main__.py.
    3. Ensure AWS credentials are configured for Pulumi to interact with your AWS account.
    4. Run pulumi up to create the resources in AWS.

    Remember, the AWS role you specify for Kendra and S3 interactions must have the necessary permissions for Kendra to access the bucket and objects.