AI Suggestions previously invented near-duplicate metadata because the classification
prompt had no knowledge of the installation's own taxonomy. This surfaces
a small, ranked, permission-filtered set of existing tags/document
types/correspondents/storage paths - drawn from the document's RAG
neighbors plus its own already-assigned metadata - so the model prefers
reusing what already exists.
The LLM response schema now returns existing_ids (IDs of reused
candidates) separately from new_names (genuinely new suggestions).
Only new_names goes through localization and fuzzy name-matching;
existing_ids is resolved deterministically and never touched by the
localization pass, so exact matches can no longer be silently
corrupted by translation.
* Feature: Allow configuring the compression type and compression levels during export
Building on the zip export improvements, this now allows users to further configure the
zip to fit their needs. A simple stored zip for speed, or a high compression zstd for
the smallest archive. Full validation of the method and levels at the command line
Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
Added a new --url argument to specify the base URL of the Paperless instance, allowing matched documents to be displayed as clickable links. Updated the logic to fetch document titles based on the presence of the base URL.
* Have the remote parser respect the provided produce_archive_file setting, as already determined via the consumer checks
* Updates the documentation to be correct about the respecting now
* merge conflict fixing
In tracemalloc based profiling, not materializing the whole Document list
reduced memory to approximately 20% of the baseline, with a peak memory
that scaled with the library size. Now, the lazt queryset is used and only
the needed pk value is actually contributing to memory
shamoonandGitHub
GitHub Actions
Trenton HandGitHub
Max TruxaandGitHub
JaydenandGitHub