AWS Machine Learning Blog 02月08日
Accelerate your Amazon Q implementation: starter kits for SMBs
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Amazon Q Business入门套件为中小型企业提供了一个快速部署和使用生成式AI和智能问答功能的解决方案。该套件简化了Amazon Q的设置过程,通过自动化部署和与关键数据源的集成,企业能够迅速从其数据中获取洞察力并提高生产力。该方案通过AWS CloudFormation 模板自动部署,连接Amazon S3和Web爬虫数据源,并与AWS IAM Identity Center集成进行身份验证。用户可以通过简单的步骤,如配置S3存储桶和IAM Identity Center,即可开始使用Amazon Q,体验企业级问答带来的变革性益处。

🚀 Amazon Q Business是一款由生成式AI驱动的助手,能够基于企业系统中的数据和信息,回答问题、提供摘要、生成内容并安全地完成任务,旨在提升员工的创造力、数据驱动能力、效率、准备度和生产力。

🗂️ 该解决方案通过连接Amazon S3和Web爬虫数据源,构建知识库。用户通过AWS IAM Identity Center进行身份验证后,可以在Web UI中提问,Amazon Q从索引中检索相关信息,并使用其内部大型语言模型(LLM)生成回复。

⚙️ 通过AWS CloudFormation模板部署解决方案,需要配置IAM Identity Center ARN、Amazon Q Business应用程序名称、S3数据源存储桶名称和Web爬虫数据源URL等参数。部署后,用户可以同步数据源,并将用户添加到Amazon Q应用程序中。

✅ 部署完成后,需要进行测试,包括上传测试文件到S3存储桶,验证文件是否被成功摄取和处理,以及检查Web爬虫是否能够从网站检索和摄取数据,确保数据在Amazon Q Web UI中正确显示。

Whether you’re a small or medium-sized business (SMB) or a managed service provider at the beginning of your cloud journey, you might be wondering how to get started. Questions like “Am I following best practices?”, “Am I optimizing my cloud costs?”, and “How difficult is the learning curve?” are quite common. AWS is here to provide a concept called starter kits.

Starter kits are complete, deployable solutions that address common, repeatable business problems. They deploy the services that make up a solution according to best practices, helping you optimize costs and become familiar with these kinds of architectural patterns without a large investment in training. Most of all, starter kits save you time—time that can be better spent on your business or with your customers.

In this post, we showcase a starter kit for Amazon Q Business. If you have a repository of documents that you need to turn into a knowledge base quickly, or simply want to test out the capabilities of Amazon Q Business without a large investment of time at the console, then this solution is for you.

This deployment guide covers the steps to set up an Amazon Q solution that connects to Amazon Simple Storage Service (Amazon S3) and a web crawler data source, and integrates with AWS IAM Identity Center for authentication. An AWS CloudFormation template automates the deployment of this solution.

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. It empowers employees to be more creative, data-driven, efficient, prepared, and productive.

Solution overview

The following diagram illustrates the solution architecture.

The workflow involves the following steps:

    The user authenticates using an AWS Identity and Access Management (IAM) identity user name and password before accessing the Amazon Q web application. Upon successful authentication, the user can access the Amazon Q web UI and ask a question. Amazon Q retrieves relevant information from its index, which is populated using data from the connected data sources (Amazon S3 and a web crawler). Amazon Q then generates a response using its internal large language model (LLM) and presents it to the user through the Amazon Q web UI. The user can provide feedback on the response through the Amazon Q web UI.

Prerequisites

Before deploying the solution, make sure you have the following in place:

Deploy the solution using AWS CloudFormation

Complete the following steps to deploy the CloudFormation template:

    Sign in to the AWS Management Console. Choose one of the following Launch Stack options for your desired AWS Region to open the AWS CloudFormation console and create a new stack. Please note that this stack will default to us-east-1.
    For Stack name, enter a name for your application (for example, AMAZON-Q-STARTER-KIT).
    In the Parameters section, for IAMIdentityCenterARN, enter the ARN of your IAM Identity Center instance. For QBusinessApplicationName, enter a name for the Amazon Q Business application. For S3DataSourceBucket, enter the name of the S3 bucket you created earlier. For WebCrawlerDataSourceUrl, enter the URL of the web crawler data source. Choose Next.

    On the Configure stack options page, leave everything as default, select I acknowledge that AWS CloudFormation might create IAM resources and and choose Next.

    On the Review and create page, choose Submit.
    On the Amazon Q Business console, you will see the new application you created. Choose the new Amazon Q Business application, and in the Data sources section, select the data source s3_datasource and choose Sync now. Select the data source webpage-datasource and choose Sync now.
    To add groups and users to your Amazon Q application, refer to instructions.

Test the solution

To validate the Amazon Q solution is functioning as expected, perform the following tests:

    Test data ingestion:
      Upload a test file to the S3 bucket. Verify that the file is successfully ingested and processed by Amazon Q. Check the Amazon Q web experience UI for the processed data.
    Test web crawler functionality: Verify that the web crawler is able to retrieve and ingest the data from the website. Make sure the data is displayed correctly in the Amazon Q web experience UI.

Clean up

To clean up, delete the CloudFormation stack and the S3 bucket you created.

Conclusion

The Amazon Q starter kit provides a streamlined solution for SMBs to use the power of generative AI and intelligent question-answering. By automating the deployment and integration with key data sources, this kit eases the complexity of setting up Amazon Q, empowering businesses to quickly unlock insights and improve productivity.

If your SMB has a repository of documents that need to be transformed into a valuable knowledge base, or you simply want to explore the capabilities of Amazon Q, we encourage you to take advantage of this starter kit. Get started today and experience the transformative benefits of enterprise-grade question-answering tailored for your business needs, and let us know what you think in the comments. To explore more generative AI use cases, refer to AI Use Case Explorer.


About the Authors

Nneoma Okoroafor is a Partner Solutions Architect focused on AI/ML and generative AI. Nneoma is passionate about providing guidance to AWS Partners on using the latest technologies and techniques to deliver innovative solutions to customers.

Joshua Amah is a Partner Solutions Architect with Amazon Web Services. He primarily serves consulting partners, providing architectural guidance and recommendations for new and existing workloads. Outside of work, he enjoys playing soccer, golf, and spending time with family and friends.

Jason Brown is a Partner Solutions Architect focused on helping AWS Distribution Partners and their Seller Partners build and grow their AWS practices. Jason is passionate about building solutions for MSPs and VARs in the small business space. Outside the office, Jason is an avid traveler and enjoys offshore fishing.

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Amazon Q Business 生成式AI 知识库 AWS CloudFormation
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