Amazon Bedrock allows you to use of generative AI models in your workflows and applications in a variety of fully-managed and partially-managed ways. Knowledge Bases are a key feature of Amazon Bedrock, enabling you to create fully automated Retrieval Augmented Generative (RAG) models solutions using your own data. Knowledge Bases support using your own custom vector store, which can have many benefits including, cost, privacy, and performance optimizations.
Learning how to configure a vector store for use with Amazon Bedrock will benefit anyone looking to implement a RAG solution using their own data in the public AWS cloud.
In this hands-on lab, you will configure an Amazon RDS PostgreSQL cluster as a vector store for use with an Amazon Bedrock Knowledge Base, and you will create an Amazon Bedrock Knowledge Base that uses it.
Please note: This lab creates an Amazon RDS PostgreSQL cluster which can take up to ten minutes to provision. Please ensure you have enough time available before starting the lab.
Upon completion of this beginner-level lab, you will be able to:
Familiarity with the following will be beneficial but is not required:
The following content can be used to fulfill the prerequisites:
July 31st, 2025: Updated screenshots to reflect the latest UI
April 11th, 2025: Added indexing command for 'chunks' column; Latest UI screenshots