Amazon Bedrock offers a powerful platform for developers to leverage generative AI with structured data storage. It provides access to pretrained models and enables the deployment and customization of pretrained foundational modes that utilize large-scale datasets. This Cloud Lab introduces Amazon Bedrock and Knowledge Base, essential for developers looking to enhance applications with advanced analytics and AI capabilities.
You will set up an Amazon Bedrock Knowledge Base, using Amazon S3 for data storage and Amazon Aurora PostgreSQL for the vector store. AWS Secrets Manager will securely store and manage the database credentials and a user’s secret, ensuring enhanced security and easy access. You’ll configure a service role, enable specific models for text generation and embeddings, and integrate these models to transform unstructured data into a queryable format. Next, you’ll create the knowledge base, configure data sources, select models, and test the knowledge base using different prompts.
By the end of this Cloud Lab, you will understand how to implement the Amazon Bedrock Knowledge Base using Amazon Aurora as a vector store. This will significantly improve your ability to develop scalable, AI-driven applications, potentially advancing your career in cloud-based machine learning technologies.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
