Baseline Enterprise RAG, From PDF to Highlighted Answer
The article discusses the development of a minimal version of a Retrieval-Augmented Generation (RAG) system that processes PDFs to provide highlighted answers. It outlines the four key components of the pipeline, including document parsing and question parsing, which work together to produce a sourced answer. The system is designed to be simple and efficient, avoiding complex frameworks while still delivering structured JSON outputs and annotated PDFs.
- ▪The RAG system processes PDFs to return sourced answers with highlighted text.
- ▪It consists of four main components: document parsing, question parsing, retrieval, and generation.
- ▪The system is built with minimal dependencies, using libraries like pymupdf and pandas for efficient processing.
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Story provenance
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/baseline-enterprise-rag-from-pdf-to-highlighted-answer-enterprise-document-intelligence-vol-1-1/ |
| Publication time | Fri, 29 May 2026 19:10:22 +0000 |
| Retrieval time | 2026-05-29T19:15:02.726Z |
| Last seen | 2026-05-29T19:15:02.726Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | J8vew6EpQ4Gy |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
LLM Applications Baseline Enterprise RAG, From PDF to Highlighted Answer Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. angela shi May 29, 2026 41 min read Share Photo by Curvd, via Unsplash The fastest way to understand what RAG is is to build the smallest version that actually works, run it on a real document, and look closely at what just happened. That’s this article. About a hundred lines of Python (no vector database, no framework, no agents) running on the Attention Is All You Need paper (Vaswani et al. 2017; arXiv non-exclusive distribution license, declared on the arXiv abstract page), returning a sourced answer with the exact source lines highlighted on the page.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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