Structured PDF-to-JSON: A Guide to Open-Source Extraction Models in 2026

In this tutorial, we build an end-to-end accounts-payable extraction pipeline with lift-pdf, using synthetic invoice PDFs as controlled test documents and a structured JSON schema as the target output format. Instead of treating invoice parsing as a simple OCR task, we frame it as schema-guided document understanding: we generate realistic invoices, define fields such as […] The post Designing a Schema-Guided Invoice Intelligence Pipeline with lift-pdf for Accounts-Payable Extraction, Validation, and Ledger Generation appeared first on MarkTechPost.

We build a practical GLM-5.2 workflow using its hosted, OpenAI-compatible API instead of running the model locally. We set up multiple providers, load the API key securely, and create a reusable chat wrapper. We then test thinking-effort control, streamed reasoning, function calling, a tool-using agent, structured JSON output, and long-context retrieval. We close with token and cost accounting so every demo stays measurable. The post GLM-5.2 OpenAI-Compatible API: A Hands-On Guide to Reasoning Effort, Function Calling, and Long-Context Retrieval appeared first on MarkTechPost.

Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appeared first on Towards Data Science.

In this tutorial, we build a complete Crawlee for Python workflow from setup to AI-ready output. We generate a local demo website, then crawl it with BeautifulSoupCrawler, ParselCrawler, and PlaywrightCrawler. We extract titles, metadata, product fields, and JavaScript-rendered cards, and capture full-page screenshots. We then normalize the data, build a link graph, and export JSON, CSV, and RAG-ready JSONL chunks. The post Crawlee for Python: Build a Web Crawling Pipeline with Robots Handling, Link Graphs, and RAG Chunk Export appeared first on MarkTechPost.

Enterprise Document Intelligence [Vol.1 #5sexies] - image_df tells you where every picture is. Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science.

In this tutorial, we build a workflow that uses Docling Parse to analyze PDF documents at a detailed structural level. We prepare a stable Python environment, handle common Colab dependency issues, and generate a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then extract words, characters, and lines with page-level coordinates, render visual overlays, and save results into structured JSON and CSV. We see how low-level parsing supports layout analysis, reading-order reconstruction, and retrieval-ready document preparation. The post How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence appeared first on MarkTechPost.

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 Shape RAG Needs appeared first on Towards Data Science.

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.