AI / RAG / Python

UChicago RAG System

The public README describes a RAG system for PDF and HTML documents, with CLI and Streamlit usage, vector retrieval parameters, and OpenAI API configuration requirements.

Public code / learning case / README-based description; not running on this site

README

Architecture reading map (based on repository description)

  1. 01PDF / HTML documents
  2. 02Chunking
  3. 03Embeddings
  4. 04Vector retrieval
  5. 05CLI or Streamlit answer

What to study

  • Document parsing quality affects answers
  • Similarity thresholds and top_k are tunable tradeoffs
  • A school name must not be read as official endorsement

Architecture review dimensions

  • Business problem: what user task does the README actually name?
  • Compute: which runtime boundary is stated, and which remains unknown?
  • Data: what source, retention, and evaluation evidence is public?
  • Network: what ingress, private access, or DNS claim is explicit versus absent?
  • Identity: where would RBAC, user isolation, and secrets be reviewed?
  • Observability and cost: what would need logs, alerts, budgets, and measured load before production?

Boundaries

No official UChicago approval is claimed; any performance numbers in the README represent repository self-description and were not re-tested here.