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Best Practices for Training Custom Invoice Templates in SAP Document AI (Base Version)

yathampavansap
Explorer
182

📘 Summary:

Looking for best practices on how to handle schema updates and reuse trained templates in SAP Document Information Extraction (DOX) Base Version. After adding new fields through schema versioning, previous training and mappings are not retained. Need guidance on whether retraining is required or if there’s a migration/reuse option.

Scenario:

I’ve been working on a custom invoice extraction setup using SAP Document Information Extraction (DOX) – Base Version (non-generative).
The use case involves 500 invoices with different layouts.

I created:

  • One custom schema

  • Around 100 templates, each trained manually with “Setup Type = Manual” for all fields

  • Training done successfully for ~100 invoices

 New Requirement:

Later, we received a request to add 5 new fields to the existing schema.
However, DOX doesn’t allow adding fields to an active schema version that already has templates.
To add new fields, we must:

  1. Deactivate and delete the existing templates, or

  2. Create a new schema version

I proceeded with creating a new schema version and added the new fields.

 Challenge:

When uploading invoices using the new schema version, the existing training/mappings from version 1 are not available.
It behaves as a completely new schema — none of the previously trained fields or templates are reused.

 Question / Clarification Needed:

Do I need to retrain all 500 invoices again for the new schema version, or
Is there any recommended approach or migration method to reuse the field mappings from the previous version?


🚀 Discussion Points / Best Practices (to confirm with SAP team):

  • Is there any way to migrate or copy field mappings between schema versions?

  • What’s the best practice when new fields need to be added to a schema with multiple templates?

  • Should we plan all required fields before starting training to avoid versioning issues?

  • Does SAP DOX Premium (Generative AI) handle such cases more flexibly?

 

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