When processing a new file conversion and text extraction is skipped
if detected to be already done. This prevents running expensive tasks
again after restarting/retrying. When explicitely reprocessing a file,
these tasks should run again and replace the existing results.
When reprocessing an item, the metadat of all *files* are replaced.
This change now also sets some metadat to an item, but only if the
item is not in state "confirmed". Confirmed items are not touched, but
the metadata of the files is updated.
This drops fomantic-ui as css toolkit and introduces tailwindcss. With
tailwind there are no predefined components, but it's very easy to
create those. So customizing the look&feel is much simpler, most of
the time no additional css is needed.
This requires a complete rewrite of the markup + styles. Luckily all
logic can be kept as is. The now old ui is not removed, it is still
available by using a request header `Docspell-Ui` with a value of `1`
for the old ui and `2` for the new ui.
Another addition is "dev mode", where docspell serves assets with a
no-cache header, to disable browser caching. This makes developing a
lot easier.
Mails are filtered once by using an imap search and then by some globs
to filter files and subjects. Imap can search by subject via a
string-contains, but not via globs or patterns (afaik). The subject
filter is applied to all downloaded mail headers. Now for post
processing (moving to some target folder or deleting), it can be
chosen to post-process all "seen" mails or only those that matched all
filters.
Classifier don't work on each attachment, but on all. So the results
must not be stored at an attachment. This reverts some previous
changes to put the classifier results for item entities into its own
table.
When re-indexing everything, skip intermediate populating the index
and do this as the very last step.
Parameterize adding new fields by their language.
Improves and reorganizes how nlp pipelines are setup. Now users can
choose from many options, depending on their hardware and usage
scenario.
This is the base to use more languages without depending on what
stanford-nlp supports. Support then is involves to text extraction and
simple regex-ner processing.
