1. Semantic Search for Legal Documents with AWS Kendra.

    Python

    To implement a semantic search for legal documents using AWS Kendra with Pulumi, you'll need to set up an Amazon Kendra index and configure data sources that Kendra will use to index your documents. Here's how you can do it step by step.

    First, you need to create an Amazon Kendra index, which is a searchable data store that contains your legal documents. You'll configure this index with the necessary capacity units and access control.

    Next, you'll set up your document data source. For legal documents, these are likely to be stored in an Amazon S3 bucket, but Kendra supports a variety of data source types. You'll define a data source configuration tailored to where your legal documents are stored, and AWS Kendra will pull documents from this data source to be indexed.

    Once your documents are indexed, you can use the AWS Kendra API to search through your index for documents semantically related to a user's query. AWS Kendra uses machine learning models to understand the context and relationship between words in your documents, providing more relevant search results than a simple keyword search.

    Now let's translate this into a Pulumi Python program:

    import pulumi import pulumi_aws as aws # Create an Amazon Kendra Index where your documents will be indexed and searched. kendra_index = aws.kendra.Index("kendraIndex", name="legal-documents-index", edition="DEVELOPER_EDITION", # DEVELOPER_EDITION is cost-effective for a proof of concept. role_arn="arn:aws:iam::123456789012:role/kendra-index-role", # Replace with your IAM role ARN. tags={ "Environment": "poc", }, description="Index for searching legal documents", capacity_units=aws.kendra.IndexCapacityUnitsArgs( query_capacity_units=2, # Adjust based on your query traffic. storage_capacity_units=2, # Adjust based on the expected volume of documents. ) ) # Define the data source configuration. # Assuming legal documents are stored in an S3 bucket. s3_data_source = aws.kendra.DataSource("s3DataSource", name="legal-documents-s3", index_id=kendra_index.id, type="S3", role_arn="arn:aws:iam::123456789012:role/kendra-s3-datasource-role", # Replace with your IAM role ARN. description="S3 data source for legal documents", schedule="cron(0 2 * * ? *)", # Daily at 2:00 am. tags={ "Environment": "poc", }, configuration=aws.kendra.DataSourceConfigurationArgs( s3_configuration=aws.kendra.DataSourceConfigurationS3ConfigurationArgs( bucket_name="legal-documents-bucket", # Replace with your S3 bucket name. exclusion_patterns=["*.tmp"], # You can exclude temporary or unrelated files. ) ) ) # Create an AWS Kendra FAQ data source if there is a collection of FAQs related to legal documents. kendra_faq = aws.kendra.Faq("kendraFaq", name="legal-documents-faq", index_id=kendra_index.id, role_arn="arn:aws:iam::123456789012:role/kendra-s3-datasource-role", # Use the same role as the S3 data source. s3_path=aws.kendra.FaqS3PathArgs( bucket="faq-documents-bucket", # Replace with your FAQ S3 bucket name. key="legal-faq.json", # JSON or CSV file containing your FAQ data. ), description="FAQ for legal documents", tags={ "Environment": "poc", } ) # Export the URL endpoint to access the index for searching. pulumi.export("legal_documents_index_endpoint", kendra_index.endpoint)

    In this Pulumi program, you define your resources and their configurations as objects using Pulumi's classes. The aws.kendra.Index class creates a new Amazon Kendra index. Your documents will be imported into this index from the data source defined with aws.kendra.DataSource using S3 configuration.

    If you have FAQs that can be used to help answer queries, you can create an Amazon Kendra FAQ data source with aws.kendra.Faq, which allows you to add structured Q&A content to your index.

    Lastly, the program exports the URL endpoint of the index, which you can use to integrate with your applications that will take user queries and search the Kendra index for relevant information.

    Remember to replace placeholders such as arn:aws:iam::123456789012:role/kendra-s3-datasource-role and S3 bucket names with appropriate values that reflect your AWS environment.

    Please also configure the IAM roles with the proper permissions that allow Kendra to access your S3 buckets and to perform actions required for indexing and searching. Detailed instructions on setting up IAM roles for Kendra can be found in the AWS Kendra documentation.

    For more information on configuring AWS Kendra with Pulumi, please refer to:


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