RAGViz: From Black Box to Glass Box

Today, we explore a novel tool called RAGViz. It's an open-source project that helps people understand and visualize how RAG systems work.

In this article, I'll walk through how it works, analyze its source code, and share my commentary.

Vivid Description

Existing RAG systems are like a chef who uses a recipe (retrieved documents) to prepare a dish (generated answers). However, users cannot see how each ingredient (document) influences the dish or even determine which ingredient caused a bad taste.

RAGViz acts as a “flavor analyzer,” pinpointing the contribution of each ingredient to the final dish and helping optimize the cooking process.

Overview

Current RAG systems have two major shortcomings:

• Lack of Transparency and Explainability: Users cannot clearly see which retrieved documents contributed to the generated response.

• Difficulty in Debugging: When incorrect answers are generated, it is hard to identify whether the issue lies in the retrieved documents or the model itself.