The article demonstrates a minimal Retrieval‑Augmented Generation (RAG) pipeline that runs on a real PDF without any vector database or orchestration framework. About a hundred lines of Python parse the document, turn a user query into keywords, retrieve relevant pages, and generate a structured JSON answer with highlighted source lines. The implementation relies on pymupdf, pandas and OpenAI’s API.

Four modular bricks compose the workflow: document parsing produces a line‑level DataFrame with bounding boxes; question parsing yields a normalized query and keyword list; retrieval selects top‑k page numbers based on the embedded query; generation assembles the answer, confidence score and exact citations into a typed JSON. An optional rendering step writes rectangles onto the original PDF.

By exposing inputs and outputs as DataFrames, each brick can be rerun independently, simplifying debugging and audit trails. The approach proves that even a 15‑page research paper can be answered with line‑level citations without consuming large token budgets. It runs on any OpenAI‑compatible endpoint, making it portable across cloud providers. The author argues this baseline sets a reproducible reference for RAG in enterprise document intelligence.