* feat(parties): selection-step evaluation against document-anchored truth
Scores which similar keys are the same party without human labels: every
key in the set carries an org number OCR-read from a linked invoice, so two
keys are the same party exactly when the org numbers agree. Rules and the
Bedrock model both land at 0.91 pair precision; the residual false merges
are different legal entities sharing a trade name, which text cannot and
should not separate. Results and caveats in the README.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
* chore(parties): make the model selector opt-in in the selection eval
Sending voucher key text to the AI provider now requires --llm; the
default run scores the rules selector only and makes no network call.
Documents that the opt-in path uses the same configured provider the
production categorizer already sends the same text to.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---------
Co-authored-by: Jakob Wennberg <311770904+jakobwennberg-oss@users.noreply.github.com>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
Jakob Wennberg
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