Amazon Bedrock enables developers to integrate powerful Generative AI models without the complexity of model training, infrastructure management, or scaling. By leveraging Bedrock, you can build intelligent applications that process and respond to user queries efficiently using advanced AI techniques.
In this Cloud Lab, you’ll begin by creating a knowledge base using the Amazon embedding model, which transforms input data into vector representations. You will then store these embeddings in an Amazon Aurora PostgreSQL database, ensuring structured and efficient storage for easy retrieval. Next, you’ll set up Bedrock Guardrails to ensure responsible AI output filtering and integrate these with the Bedrock Agent, which uses the Amazon Premier model for processing user queries.
Once your data and models are in place, you’ll create a Lambda function to interact with the Bedrock Agent. This function will fetch relevant context from your database when a user query is made. You will configure an API Gateway to invoke this Lambda function, passing the query and receiving the processed response. Finally, you’ll integrate the API with a React web application hosted on AWS Amplify, establishing a seamless connection between the frontend and backend services, enabling the full AI-powered question-answering experience.
After completing this Cloud Lab, you’ll have the skills to leverage Amazon Bedrock for creating and managing knowledge bases, integrating it with AWS services like Lambda, API Gateway, and Aurora to build serverless GenAI applications. You’ll also gain experience deploying a React web application on AWS Amplify, seamlessly connecting the frontend to the backend.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
