There is consistent customer feedback that AI assistants are the most useful when users can interface with them within the productivity tools they already use on a daily basis, to avoid switching applications and context. Web applications like Amazon Q Business and Slack have become essential environments for modern AI assistant deployment. This post explores how diverse interfaces enhance user interaction, improve accessibility, and cater to varying preferences.
By offering seamless experiences across environments, organizations can increase user satisfaction and adoption rates. The assistant employs Retrieval Augmented Generation (RAG), a technique that integrates credible and authoritative sources within responses across these interfaces, bolstering trustworthiness and educational value. This multi-interface, RAG-powered approach not only strives to meet the flexibility demands of modern users, but also fosters a more informed and engaged user base, ultimately maximizing the assistant’s effectiveness and reach. By combining RAG with multiple interfaces, the assistant delivers consistent, accurate, and contextually relevant information regardless of the user’s preferred environment and productivity tools.
Solution overview
The following diagram illustrates the application’s architectural design.
You can find the complete code and the steps to deploy the solution in the GitHub repository.
Click here to open the AWS console and follow along.
Prerequisites
You must have the following prerequisites:
- Active AWS account
- Docker installed
- The AWS Command Line Interface (AWS CLI) installed
- The AWS CDK
- Slack workspace with the following:
- Parent channel – A public Slack channel where users will be interacting with the Slack assistant
- Child channel – A private Slack channel where metrics report will be sent to
Deploy the solution
For the set-up steps, refer to the README in the GitHub repo.
Solution components
In this section, we discuss two key components to the solution: the data sources and vector database.
Data sources
We use Spack documentation RST (ReStructured Text) files uploaded in an Amazon Simple Storage Service (Amazon S3) bucket. Whenever the assistant returns it as a source, it will be a link in the specific portion of the Spack documentation and not the top of a source page. For example, Spack images on Docker Hub.
Spack is a versatile package manager for supercomputers, Linux, and macOS that revolutionizes scientific software installation by allowing multiple versions, configurations, environments, and compilers to coexist on a single machine. Developed by Todd Gamblin at the Lawrence Livermore National Laboratory in 2013, Spack addresses the limitations of traditional package managers in high-performance computing (HPC) environments. Brian Weston, Cloud Transformation for Mission Science Program Lead at LLNL, advised in the development of this assistant.
Additionally, we use text files uploaded to an S3 bucket that is accessible through an Amazon CloudFront link. There is also an automated ingestion job from Slack conversation data to the S3 bucket powered by an AWS Lambda function. This enables the assistant to also use previous conversations from users to answer questions and cite its sources. We opted to use CloudFront links as opposed to using Slack links because when this source is cited in Amazon Q, the user might not have access to the Slack data. There is also an alternative to this methodology using the Slack connector for Amazon Kendra.
This solution could support other data types such as PDFs, Word documents, and more as long as their text can be extracted and fed into the vector database with some code changes. Their raw files can be served in a CloudFront distribution.
The following screenshot illustrates a sample CloudFront URL.
Upon deployment, existing data is automatically uploaded into an S3 bucket and processed to be used by the assistant. The solution also includes automatic daily ingestion of data from Slack into the application using Amazon EventBridge.
Vector database
This solution uses Amazon Kendra as its vector database, offering significant advantages in simplicity and cost-effectiveness. As a fully managed AWS service, Amazon Kendra reduces both development and maintenance costs. Amazon Q, which supports two types of retrievers (native retriever and Amazon Kendra), is seamlessly integrated into this setup. By using Amazon Kendra, the solution efficiently employs the same retriever for both the Amazon Q and Slack interfaces. This approach not only streamlines the overall architecture but also provides a more consistent user experience across both environments. The result is a cohesive, cost-efficient system that maintains uniformity in information retrieval and presentation, regardless of the user’s chosen interface.
Amazon Kendra also supports the use of metadata for each source file, which enables both UIs to provide a link to its sources, whether it is the Spack documentation website or a CloudFront link. Furthermore, Amazon Kendra supports relevance tuning, enabling boosting certain data sources. For this solution, we boosted the results for the Spack documentation.
User interfaces
In this section, we discuss the UIs used in this solution.
Amazon Q Business
Amazon Q Business uses RAG to offer a secure, knowledge-enhanced AI assistant tailored to your organization. As an AWS native solution, it seamlessly integrates with other AWS services and features its own user-friendly interface. This integration, combined with its straightforward setup and deployment process, provides a smooth implementation experience. By fusing generative AI capabilities with intelligent information retrieval from your enterprise systems, Amazon Q Business delivers precise, context-aware responses firmly rooted in your organization’s specific data and documents, enhancing its relevance and accuracy.
The following screenshot is an example of the Amazon Q Business UI.
Slack
Slack is a popular collaboration service that has become an integral part of many organizations’ communication forums. Its versatility extends beyond team messaging to serve as an effective interface for assistants. By integrating AI-powered assistants into Slack, companies can use its familiar environment to provide users with instant access to information.
The following screenshot shows an example of the Slack UI with a message thread.
Monitoring
Amazon Q has a built-in feature for an analytics dashboard that provides insights into user engagement within a specific Amazon Q Business application environment. It offers valuable data on usage patterns, conversation dynamics, user feedback, and query trends, allowing you to analyze and optimize your AI assistant’s performance and user interaction.
For Slack, we are collecting user feedback, as shown in the preceding screenshot of the UI. Users can add a “thumbs up” or a “thumbs down” to the assistant response to keep track of its performance. Furthermore, we have built a custom solution that uses an Amazon CloudWatch dashboard to mimic the Amazon Q analytics dashboard to further align the experience between the two applications.
The following screenshot shows an example of the Slack CloudWatch dashboard.
In addition, there is a daily scheduled Slack message that summarizes the Slackbot data for the past day, as shown in the following screenshot.
Clean up
To avoid incurring ongoing charges, clean up the resources you created as part of this post with the command mentioned in the readme.
Conclusion
The implementation of a multi-interface AI assistant using RAG represents a leap in AI-driven organizational communication. By integrating Amazon Q Business and Slack interfaces with a robust backend powered by Amazon Kendra, this solution offers seamless, environment-agnostic access to accurate, context-aware information. The architecture’s strengths lie in its consistency across environments, automatic data ingestion processes, and comprehensive monitoring capabilities. This approach not only enhances user engagement and productivity, but also positions organizations to adapt swiftly to evolving communication needs in an increasingly AI-centric landscape, marking a pivotal step towards more efficient and intelligent information management systems.
To learn more about the AWS services used in this solution, refer to the Amazon Q User Guide, Deploy a Slack gateway for Amazon Bedrock, and the Amazon Kendra Developer Guide.
About the Authors
Nick Biso is a Machine Learning Engineer at AWS Professional Services. He solves complex organizational and technical challenges using data science and engineering. In addition, he builds and deploys AI/ML models on the AWS Cloud. His passion extends to his proclivity for travel and diverse cultural experiences.
Dr. Ian Lunsford is an Aerospace Cloud Consultant at AWS Professional Services. He integrates cloud services into aerospace applications. Additionally, Ian focuses on building AI/ML solutions using AWS services.