How to Build & Run Reusable Extractors | Enterprise h2oGPTe

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

Summary

This content describes the full workflow for using "Extractors" within Enterprise h2oGPTe, a system designed to convert unstructured documents into structured JSON output. An Extractor combines a JSON schema, which defines the desired output structure (fields, metrics, tables), with a Large Language Model (LLM) that interprets document content and maps it to that structure. The process involves navigating to the Extractors workspace, creating a new Extractor, assigning it a name and description, selecting an LLM (with associated token costs), and defining the JSON schema. Schemas can be built using a UI-based builder for individual fields (String, Number, Boolean) or by directly pasting a valid JSON schema in code mode. Once created, an Extractor can be run on a selected document collection to produce consistent, scalable structured data.

Key takeaway

For MLOps Engineers or Data Engineers tasked with automating data extraction from diverse unstructured sources, h2oGPTe's Extractors offer a standardized, scalable solution. You should define your desired output structure using JSON schemas and pair it with an appropriate LLM to consistently transform document collections into structured JSON, significantly reducing manual effort and improving data pipeline efficiency.

Key insights

Extractors in h2oGPTe provide a reusable, scalable method for converting unstructured data into structured JSON using LLMs and JSON schemas.

Principles

• JSON schemas define output structure.
• LLMs map content to schema intent.
• Extractors are reusable components.

Method

Create an Extractor by naming it, selecting an LLM, and defining a JSON schema via UI builder or code. Then, run it on a target document collection.

In practice

• Define schemas for specific data points.
• Use code mode for existing schemas.
• Apply Extractors across document collections.

Topics

• Enterprise h2oGPTe
• Extractors Workflow
• JSON Schema
• Large Language Models
• Structured Data Extraction

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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