Guidance For Generative Ai Shopping Assistant Using Agents For Amazon Bedrock

Guidance for Generative AI Shopping Assistant using Agents for Amazon Bedrock

Table of Contents

1. Overview
   • Solution Overview
   • Architecture
   • Cost
2. Prerequisites
   • Operating System
   • Amazon Bedrock Model Access
3. Deployment Steps
4. Deployment Validation
5. Running the Guidance
6. Testing app locally on your machine
7. Next Steps
8. Cleanup
9. FAQ, known issues, additional considerations, and limitations
10. Notices
11. Authors

Overview

This Guidance demonstrates how to create powerful, generative AI-driven shopping assistant that enhances customer experience and drives sales through context-aware, AI-powered interactions. The solution offers features like personalized and related product recommendations, dynamic contextual product comparisons, seamless cart management, and automated order & email confirmation. It seamlessly integrates with your existing company systems and data securely and minimizes the time and complexity needed for building sophisticated LLM (Large Language Model) Agents.

Solution Overview

This Guidance deploys a Streamlit Python web application on Amazon ECS Fargate, which calls the [Amazon Bedrock Agent API](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) for natural language conversations. The Shopping Agent is built using [Agents for Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) which utilizes Anthropic's Claude 3 Sonnet model to interpret user queries. The agent orchestrates multi-step tasks for finding relevant products using [Knowledges Bases for Amazon Bedrock](https://docs.aws.amaz