--- title: "Baseline Enterprise RAG, From PDF to Highlighted Answer" url: https://daily.dev/posts/baseline-enterprise-rag-from-pdf-to-highlighted-answer-cwljrasn1 source_url: https://towardsdatascience.com/baseline-enterprise-rag-from-pdf-to-highlighted-answer-enterprise-document-intelligence-vol-1-1 type: article source: "Towards Data Science" published: 2026-05-29T19:42:04.532Z updated: 2026-05-29T19:42:28.426Z tags: ["python", "openai", "rag", "pydantic"] reading_time: 41 upvotes: 2 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Baseline Enterprise RAG, From PDF to Highlighted Answer **[Towards Data Science](https://daily.dev/sources/tds)** · 41 min read · 2 upvotes · 0 comments ## Summary A hands-on walkthrough of a minimal enterprise RAG pipeline built in ~100 lines of Python with no vector database or framework. The pipeline has four bricks: document parsing (PDF to line-level DataFrame with bounding boxes via PyMuPDF), question parsing (LLM-extracted keywords), retrieval (keyword matching vs. embeddings with a detailed comparison of their failure modes), and generation (structured JSON answer with page/line citations via a Pydantic schema). The final step optionally annotates the source PDF by drawing rectangles around cited lines. The article uses the 'Attention Is All You Need' paper and a World Bank commodity report as test documents, demonstrating correct answers, clean 'not found' handling, and honest discussion of where each component breaks. It serves as the first installment of a longer series on enterprise document intelligence. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited](https://daily.dev/posts/one-rag-pipeline-four-very-different-pdfs-same-four-bricks-every-answer-typed-and-cited-usztm5tae) · Towards Data Science · 1 upvotes · 0 comments - [A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers](https://daily.dev/posts/a-production-rag-pipeline-for-pdfs-relational-parsing-toc-retrieval-typed-answers-vuwase0nh) · Towards Data Science · 1 upvotes · 0 comments - [Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale](https://daily.dev/posts/enterprise-document-intelligence-a-series-on-building-rag-brick-by-brick-from-minimal-to-corpus-sc-qyl6soyo4) · Towards Data Science · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#openai](https://daily.dev/tags/openai), [#rag](https://daily.dev/tags/rag), [#pydantic](https://daily.dev/tags/pydantic) [View this post on daily.dev](https://daily.dev/posts/baseline-enterprise-rag-from-pdf-to-highlighted-answer-cwljrasn1)