Enterprise Document Intelligence [Vol. 1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph.
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.
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
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 .
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Finding the right anchors for RAG: keyword, embedding, and TOC signals in parallel appeared first on Towards Data Science .
The article titled "Tables in PDFs for RAG: Don’t Flatten the Grid" discusses Enterprise Document Intelligence, presenting a diagnostic and five composable operations rather than a decision tree. It appears in the Vol.1 #B4 issue of the publication and was first posted on Towards Data Science.
The article discusses a case study in enterprise document intelligence where a single document type contains a million files. It outlines a workflow that takes about an hour with two people to extract six to ten structured fields, emphasizing the importance of identifying two key signals that distinguish a valid column from one that could break a filter later. The focus is on converting unstructured documents into a structured SQL table for Retrieval-Augmented Generation (RAG) queries.
Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.