RAG, Vector Database and RBAC

Recently, I talked to a coworker who is working with a client adopting a RAG Architecture. She told me about a technical question that she had. She was creating an AI chat assistant on AWS using a RAG architecture, she was wondering what was the best approach to ensure that the strict permissions from their documentation wiki were kept when the data was moved to a vector database for use by the AI.

I find it an interesting topic to explore. I want to understand the best approaches available and their trade-offs.

I decided to look into this issue more and share my findings. This blog will explain how RBAC (Role-Based Access Control) can work well with RAG architecture in AWS.

Let’s start from the basic

What is a Vector Database?

Vector databases are specialized databases designed to store and query vector embeddings generated by machine learning models.

Vector embeddings are a way of converting non-numeric data, like words or images, into a numeric form so that they can be processed by mach…