Access control for vector stores using metadata filtering with Amazon Bedrock Knowledge Bases

This article discusses how to implement access control and ensure data privacy and security in applications that use Retrieval Augmented Generation (RAG) with Knowledge Bases for Amazon Bedrock. It demonstrates how to leverage metadata filtering to restrict the search and retrieval of data based on user roles, departments, or data sensitivity levels.

Specifically, the article covers:

• The benefits of metadata filtering for access control in RAG applications
• A healthcare provider use case where doctors can only access transcripts of their own patient interactions
• Setting up user authentication with Amazon Cognito and associating doctors with patient IDs in Amazon DynamoDB
• Structuring the dataset with transcript files and corresponding metadata JSON files
• Creating a knowledge base and using metadata filters in the Amazon Bedrock console
• Querying the knowledge base programmatically with metadata filters using the AWS SDK
• Building a Streamlit app as a user interface for doctors to interact with the knowledge base
• Cleaning up and deleting the deployed resources