View only. Sign in to run this demo. You can also run it with your own model key, without an account.

Sign in

Vision Extractor

Turn PDFs and images into validated, structured data in a single model call

How to use the Vision Extractor

  1. 1Add the documentsUpload PDFs and images, one or several. Or use one of the ready-made documents.
  2. 2Say what to extractDescribe the fields in words. The schema is generated for you to check.
  3. 3Choose the modelPick the model and options. Confidence scores per field are optional.
  4. 4Check the sourcesEach value shows the text it was read from, highlighted on its page.
  • One model call sees every file at once, so it can match values across documents, for example an invoice against its purchase order.
  • Nothing you enter is saved. A schema generated for your own task lives in this session only.

Start from a ready-made document

1. The documents
2. What to extract

A schema is generated from this text at the first run. Name the fields and their types.

Each value comes with its quote, highlighted in the PDF. Uses more output tokens.

No extraction yet

Every field of the result appears here, with the place in the document it was read from.

GAIK Toolkit

Generative AI building blocks for knowledge capture, access and synthesis.

Co-funded by the European Union

Demos

Use CasesSoftware ModulesSoftware ComponentsNo-code Assets

Resources

GitHubDocsPyPI

Project

gaik.aiRequest AccessPrivacy

Disclaimer: The demo website uses Microsoft Azure APIs for OpenAI's and Anthropic's models, and Google's Vertex AI for Gemini models. No user data is retained, and neither Azure nor Vertex AI uses any user data for model training.

© 2026 GAIK ProjectThe gaik package is open source under the MIT licence.
GAIK