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
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
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 .
By angela shi
Enterprise Document Intelligence [Vol. 1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph.
By Kezhan Shi
The article reports on a benchmark that reduced 1,000 Apache Iceberg files to just six, then measured how this consolidation affected query performance across three SQL workloads. It details the methodology and results of the experiment, highlighting changes in execution speed and resource usage. The findings illustrate the trade‑offs between file count and query efficiency in large data systems.
By Thomas Reid
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 .
By angela shi
Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.
By angela shi
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #7A] - Stop searching strings.
By angela shi
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.
By angela shi
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
By Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi