Towards Data Science August 23, 2026 By angela shi

Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File

Read the original on Towards Data Science →

The article discusses how Enterprise Document Intelligence should begin by parsing the folder structure rather than just PDFs, emphasizing that the index must reflect the case type’s requirements before any folder is accessed. It highlights that the two key questions to develop are not retrieval questions but rather focus on the relational tables needed for Retrieval-Augmented Generation (RAG) in a case file context.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

Towards Data Science

Aug 22

Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline

The article discusses a method for handling a folder of unrelated PDFs as a single long document with a nested outline. It highlights that without shared fields, an index cannot be built, so the approach uses one summary line per file and each file’s own table of contents, with retrieval routes extending down two levels. This structure enables retrieval-augmented generation (RAG) across multiple documents.

By angela shi

rag

Towards Data Science

Jun 13

Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload

Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.

By Kezhan Shi

rag computer-vision

Towards Data Science

Jul 7

A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers

Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.

By angela shi

rag

Towards Data Science

Jun 10

Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality

Enterprise Document Intelligence [Vol. 1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .

By Kezhan Shi

rag

Towards Data Science

Jun 11

Stop Returning Flat Text from a PDF: The Relational Tables RAG Needs

Enterprise Document Intelligence [Vol. 1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Tables RAG Needs appeared first on Towards Data Science .

By Kezhan Shi

rag

Towards Data Science

Jun 20

Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All

Enterprise Document Intelligence [Vol. 1 #5sexies] - image_df tells you where every picture is.

By Kezhan Shi

rag

One email a morning, machine-written

Related stories

Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline

Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload

A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers

Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality

Stop Returning Flat Text from a PDF: The Relational Tables RAG Needs

Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All