Tutorial

Turn complex lab reports into clean, structured data ready for analysis and dashboards using zero-shot parsing and schema-guided extraction

Agenda Introduction Public companies in the United States must file an annual Form 10‑K with the U.S. Securities and Exchange Commission (SEC). The 10‑K is a comprehensive financial report that includes a company’s history, organizational structure, financial statements, earnings per share and other disclosures[1]. These filings provide investors with a detailed snapshot of a company’s operations […]

Overview: What You’ll Learn Complete code for the tutorial is available on GitHub — follow along and run the example app yourself👨🏼‍💻. Figure 1: A simple “Chat with PDF” app. The user has uploaded a PDF and the Agentic Document Extraction Python library has precomputed structured data for each page. Figure 2: The app’s response […]

Introduction Modern Large Language Models (LLMs) have revolutionized text analysis—until they encounter the complexities of PDFs. PDFs often feature intricate layouts, visual elements, flowcharts, images, and tables with interdependent contexts and relationships. This is where Agentic Document Extraction truly stands out. In Part 1, we demonstrated examples where traditional LLMs struggled, while Agentic Document Extraction […]

Introduction If you’ve ever tried to extract meaningful data from PDFs—especially documents with complex layouts like tables, charts, or forms—you’ve likely run into OCR’s limitations. OCR is great for raw text, but it ignores structural relationships critical for true comprehension. Enter Agentic Document Extraction: Instead of flattening everything into text, it retains visual and spatial […]