Seeking feedback on my RAG project

I made a small project to make the context chunk selection human-comprehensible in a simple RAG model that uses Llama 3.2 that can operate on a local machine with only 8 GB of RAM! The code shows you the scores of various bits of context (it takes a few minutes to run) so you can "see" how the extra information to add to the prompt is actually chosen, and get an intuition for what the machine is "thinking". I'm wondering if anyone here is willing to try it out.

GitHub - ncole1/RAG_with_relevance_scores: A "white box" approach to a simple (vibe-coded in Cursor) RAG that includes, along with the text response, the Z-score associated with each "chunk" of context. The Z-score is the normalized relevance score.