Extract Structured Data from Any Document | Enterprise h2oGPTe

2026-03-31 · Source: H2O.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Novice, quick

Summary

Enterprise h2oGPTe features an "Extractor" tool designed to convert unstructured document content into structured JSON data. This capability is particularly useful for automating the retrieval of specific metrics from business documents such as SEC filings, invoices, or résumés. Extractors operate based on user-defined JSON schemas that specify the desired information. For instance, the platform can automatically extract key financial metrics from a lengthy report like Alphabet's Form 10-K, structuring the data for use in applications, reports, or workflows. The process involves creating a Collection for the document, defining the Extractor with a JSON schema specifying fields like "revenueGrowthRate" and "netProfitMargin", and then running an LLM, such as deepseek-ai/DeepSeek-R1-Shadeform, to perform the extraction.

Key takeaway

For data scientists or AI engineers needing to automate data extraction from complex business documents, Enterprise h2oGPTe's Extractor feature offers a direct solution. You can define precise JSON schemas to pull specific metrics, like financial ratios from a Form 10-K, transforming unstructured text into clean, usable JSON. This approach streamlines data integration into your applications and analytical workflows, significantly reducing manual effort and potential errors.

Key insights

Extractors in h2oGPTe convert unstructured document data into structured JSON using defined schemas.

Principles

• Schema-driven extraction ensures data consistency.
• Automated extraction reduces manual data entry.

Method

Upload document to a Collection, define an Extractor with a JSON schema specifying desired fields, select an LLM (e.g., deepseek-ai/DeepSeek-R1-Shadeform), and run the extraction job.

In practice

• Extract financial ratios from SEC filings.
• Automate data capture from invoices.
• Structure résumé information for HR systems.

Topics

• Enterprise h2oGPTe
• Document Extraction
• JSON Schema
• Financial Data Extraction
• Form 10-K Analysis

Best for: AI Engineer, Data Scientist, Software Engineer

Related on AIssential

• How to Build & Run Reusable Extractors | Enterprise h2oGPTe — Extractors in h2oGPTe provide a reusable, scalable method for converting unstructured data into structured JSON using LLMs and JSON schemas.
• Ai_Parse_Document in Databricks | Pulling Text and Tabular Data from PDFs — Databricks' AI Parse Document converts unstructured documents into semi-structured HTML/JSON for easier data extraction.
• ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images — ExStrucTiny is a new benchmark for structured information extraction from diverse, schema-variable document images.
• GliNER2: Extracting Structured Information from Text — GliNER2 unifies multiple NLP extraction tasks into a single, CPU-efficient, schema-driven framework.
• Automate PDF Data Extraction with n8n EASILY! (Open source) — Combine Unstruct and n8n to automate local, free, and open-source structured data extraction from documents.
• Source-Grounded Data Generation for Text-to-JSON Learning — STAGE generates high-quality, validated text-to-JSON training data using LLMs and spreadsheets, significantly improving extraction accuracy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by H2O.ai.