Core concepts

Preprocessing

For use cases that work with more complex documents, combining VLMs with preprocessing techniques has proven quite successful. Under Configuration > Generative AI, toggle on Preprocess document. Document preprocessing essentially runs traditional OCR (Optical Character Recognition) on the document, then passes that output in addition to the document page itself to a VLM, giving the model more context to successfully analyze the document.

Evaluations

For each run of an extraction strategy, you can choose to view a qualitative rubric that leverages your selected VLM as a judge. We fine-tuned the prompt to rank various area from 1 (worst) to 5 (best), including how well a given strategy extracted tables, headers, and more. Evaluations allow you to quickly iterate and make judgments as you test different prompts and strategies.

Deployment paths

Once you are satisfied with a particular strategy, you can deploy it in a batch pipeline to run it over your wider dataset. Currently, we only support a Python transform deployment path.

Python transform

You can export your strategy to a Python transform repository template that is fully dynamic; the dataset RID/path, model RID/path, custom prompt, and selected configuration are all automatically configured. We recommend you verify this work before triggering a build.

Learn more about the features of AIP Document Intelligence and how to get started.