Amazon VGT2 Las Vegas: Streamlining Enterprise Knowledge Integration with Amazon Q Business

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In the rapidly evolving landscape of artificial intelligence, businesses now have the capability to develop AI-driven assistants that cater specifically to their operational needs. Amazon Q Business, a cutting-edge generative AI assistant, is designed to address a variety of tasks including answering queries, summarizing content, generating written material, and efficiently executing tasks based on data from enterprise systems.

The ingestion of large datasets is essential for functions such as document analysis, summarization, and knowledge management. These processes often require handling a significant volume of documents, which can be both time-consuming and labor-intensive. However, the challenge lies in the orchestration of workflows to collect data from various sources, making large-scale data ingestion a complex task.

This article presents a comprehensive solution utilizing Amazon Q Business to facilitate the integration of enterprise knowledge bases at scale.

Improving AWS Support Engineering Efficiency

The AWS Support Engineering team had been burdened with the tedious task of manually searching through a multitude of tools, internal resources, and public AWS documentation to resolve customer inquiries. When faced with intricate issues, this process became especially drawn-out, impacting the speed at which customers received solutions. To mitigate this, the team adopted a chat assistant powered by Amazon Q Business. This innovative solution processes information from hundreds of thousands of support requests, escalation notices, public documentation, re:Post articles, and AWS blog entries.

By leveraging Amazon Q Business, which simplifies the development and management of machine learning infrastructure, the team rapidly deployed their chat solution. Pre-built connectors such as Amazon Simple Storage Service (Amazon S3), along with document retrieval and upload capabilities, streamlined the data ingestion process, allowing for prompt and accurate responses to customer inquiries.

This post outlines an end-to-end solution using Amazon Q Business to tackle similar enterprise data challenges, demonstrating how it can enhance operational efficiency and customer service across various sectors. We will start by discussing large-scale data integration with Amazon Q Business, including data preprocessing, implementing security measures, and best practices. Subsequently, we will introduce the deployment of the solution using three AWS CloudFormation templates.

Solution Overview

The subsequent architecture diagram illustrates the high-level design of a solution that has proven effective in real-world applications for AWS Support Engineering. It capitalizes on the robust capabilities of Amazon Q Business. We will detail the implementation of core components, including the configuration of enterprise data sources for knowledge base creation, document indexing, and the establishment of thorough security protocols.

Amazon Q Business accommodates three user types within its identity and access management framework:

• Service User – An end-user who accesses Amazon Q Business applications with permissions assigned by their administrator to fulfill job responsibilities.
• Service Administrator – A user responsible for managing Amazon Q Business resources and determining feature access for service users within the organization.
• IAM Administrator – A user tasked with creating and overseeing access policies for Amazon Q Business through the AWS IAM Identity Center.

The following workflow outlines how a service user accesses the application:

1. The service user initiates an interaction with the Amazon Q Business application via a web interface at an endpoint URL.
2. The service user’s permissions are authenticated through IAM Identity Center, an AWS service that connects workforce users to AWS-managed applications like Amazon Q Business. This facilitates end-user authentication and simplifies access management.
3. The authenticated service user submits queries in natural language to the Amazon Q Business application.
4. The application formulates and provides answers based on enterprise data stored in an S3 bucket connected as a data source to Amazon Q Business. This S3 bucket data is consistently updated, ensuring that Amazon Q Business accesses the latest information for responses by utilizing a retriever to extract data from the index.

Large-Scale Data Ingestion

Before data can be ingested into Amazon Q Business, it may require transformation into compatible formats. Additionally, it may contain sensitive information or personally identifiable information (PII) that necessitates redaction. These challenges highlight the need for orchestrating tasks such as transformation, redaction, and secure ingestion.

Data Ingestion Workflow

To streamline orchestration, this solution employs AWS Step Functions, a visual workflow service that efficiently manages tasks and workloads through built-in AWS integrations and error handling. The solution utilizes the Step Functions Map state, enabling parallel processing of multiple items in a dataset, thereby enhancing workflow orchestration and expediting overall processing.

An example architecture for data ingestion through an endpoint interfacing with a large dataset is illustrated. Step Functions orchestrates AWS services like AWS Lambda and organizational APIs such as DataStore to securely ingest, process, and store data. The workflow comprises the following steps:

1. The Prepare Map Input Lambda function readies the necessary input for the Map state. For instance, the Datastore API may require specific inputs like date ranges to query the data.
2. The Ingest Data Lambda function retrieves data from the Datastore API—whether within or outside the virtual private cloud (VPC)—based on the inputs from the Map state. To manage large volumes effectively, the data is divided into smaller chunks, preventing overload on the Lambda function. This approach allows Step Functions to oversee the workload, retry failed segments, and isolate failures to individual chunks without disrupting the entire ingestion process.
3. The retrieved data is stored in an S3 data bucket for subsequent processing.
4. The Process Data Lambda function uses Amazon Comprehend to redact sensitive information. Amazon Comprehend provides real-time APIs, such as DetectPiiEntities and DetectEntities, utilizing natural language processing (NLP) machine learning models to identify text segments for redaction. When PII is detected, the terms are redacted and replaced with a chosen character (e.g., *). Regular expressions can also be employed to remove identifiers with specific formats.
5. Finally, the Lambda function generates two distinct files:
   • A sanitized data document in a format compatible with Amazon Q Business for parsing and generating chat responses.
   • A JSON metadata file for each document, containing additional details to customize chat results for end-users and implement boosting techniques to enhance user experience.

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