Simulations have become crucial instruments for organizations aiming to forecast outcomes, assess risks, and make informed choices. They offer insights that propel strategic decision-making across various domains, from optimizing supply chains to exploring design alternatives for products and processes.
However, managing and analyzing simulations can be labor-intensive, often necessitating specialized teams of data scientists, analysts, and subject matter experts. In manufacturing, there is a high demand for these professionals to model and refine complex production processes. This often results in backlogs and delays in accessing vital insights. In the healthcare sector, teams of epidemiologists and statisticians conduct simulations for infectious disease modeling, essential for public health officials’ decision-making. The limited capacity of these specialists creates bottlenecks and inefficiencies, hindering timely, data-driven responses to emerging health crises.
In this article, we will explore a generative AI-based “Simulation Assistant” demonstration application constructed with LangChain Agents and Anthropic’s recently launched Claude V3 large language model (LLM) on Amazon Bedrock. By exploiting the latest advancements in LLMs and AWS technology, we will demonstrate how to simplify and democratize your simulation workflows through a scalable, serverless architecture featuring a chatbot-style interface. This setup will enable users to initiate and interact with simulations using natural language prompts.
How Does This Benefit Experts?
The Simulation Assistant demo provides a framework for delivering significant benefits to organizations in two primary ways:
- Democratizes Simulation-Driven Problem Solving: While regulated sectors may still necessitate certified personnel for final approvals, this solution illustrates a method to extend simulation usage beyond specialist teams. By allowing knowledgeable personnel across various roles—such as analysts, managers, and decision-makers—to initiate and analyze simulations with expert guidance, organizations can enhance the utilization of simulation capabilities and relieve the workload of their simulation experts.
- Improves Efficiency for Simulation Experts: Enabling a broader user base to run routine simulations allows experts to concentrate on higher-value tasks, such as performance tuning or developing new simulations. Streamlined, automated workflows accessed through a unified chatbot interface boost productivity, standardize processes, facilitate knowledge sharing, and enhance result reliability.
Whether you are a business analyst, product manager, researcher, or simulation expert, this demonstration provides an intuitive, efficient way to leverage the power of simulations by utilizing generative AI through Amazon Bedrock and the scalability of AWS—promoting innovation and operational excellence across various industries.
Solution Overview
The architecture of the Simulation Assistant application is depicted in Figure 1. A containerized Streamlit web app is deployed via a load-balanced AWS Fargate service. The web app invokes an LLM-based agent with access to several tools. The retriever tool provides retrieval-augmented generation (RAG) capabilities using Amazon Kendra. Other tools enable the agent to execute a custom mathematical transformation, invoke a simple inflation simulation through AWS Lambda, run a series of containerized investment portfolio simulations via AWS Batch that store results in an Amazon DynamoDB table, and analyze a batch of simulation results by generating plots.
The web application, crafted using Streamlit, serves as the user interface. This open-source Python library allows the creation of interactive web applications tailored for machine learning and data science. We have containerized this app using Docker and stored it in an Amazon Elastic Container Registry (ECR) repository.
The containerized web application is deployed as a load-balanced AWS Fargate Service within an Amazon Elastic Container Service (ECS) cluster. AWS Fargate is a serverless compute engine that allows you to run containers without the hassle of managing servers or clusters. Utilizing Fargate enables the Simulation Assistant application to automatically adjust its compute resources based on incoming traffic, ensuring optimal performance and cost-effectiveness.
The web application is fronted by an Application Load Balancer (ALB) that distributes incoming traffic across multiple targets, such as Fargate tasks. This load-balancing mechanism ensures efficient handling of user requests even during peak traffic periods by dynamically routing requests to available container instances.
Lifecycle of a Request
When a user accesses the Simulation Assistant application, their request is captured by the ALB, which then directs the request to one of the healthy Fargate tasks running the Streamlit web application. This serverless deployment approach, paired with the load-balancing capabilities of the ALB, offers a highly available and scalable architecture for the Simulation Assistant, allowing it to accommodate varying levels of user traffic without manual server management.
The Streamlit web application serves as the central hub, orchestrating interactions between different AWS services to facilitate seamless simulation capabilities for users. Within the Streamlit app, we utilize Amazon Bedrock to process user queries by leveraging advanced language models. Bedrock is a fully-managed service that provides access to foundational models from both Amazon and leading AI startups via a unified API, simplifying model management complexities.
For basic simulations, like price inflation scenarios, the Streamlit app integrates with AWS Lambda functions. These serverless functions encapsulate lightweight simulation logic, allowing efficient execution and scalability without needing to provision dedicated servers.
Furthermore, we leverage Amazon Kendra, an intelligent search service, to enable retrieval augmented generation (RAG). Amazon Kendra indexes and searches through documents stored in an Amazon S3 bucket, acting as a source repository. This integration empowers the application to deliver relevant information from existing documents, enhancing simulation capabilities and supporting more informed decision-making.
For more computationally intensive simulations, such as conducting sets of investment portfolio simulations in a Monte Carlo-style approach, the Simulation Assistant utilizes AWS Batch. AWS Batch is a fully managed service that efficiently runs batch computing workloads across AWS resources. The Simulation Assistant submits jobs to AWS Batch, which dynamically provisions the compute resources necessary to execute them in parallel, enabling quicker execution times and scalability.
Upon completion of the simulations, the results are stored in an Amazon DynamoDB database, a fully managed NoSQL database service. DynamoDB offers rapid and predictable performance with seamless scalability, making it ideal for efficiently storing and retrieving simulation data. Additionally, the application integrates with Amazon EventBridge, a serverless event bus service. Once a simulation batch concludes, EventBridge triggers a notification sent to the user via email using Amazon Simple Notification Service (SNS). This notification system keeps users updated on the completion of their simulation requests, allowing them to promptly access and analyze the results.
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