eikek a7b74bd5ae Allow to search in soft-deleted items
A new query/request parameter can be used to apply a search to only
soft-deleted items.

The query expression `Trashed` has been introduced which selects only
items with state `Deleted`. This is another option an analog to
`ValidItemStates` (both cannot be used together as they would select
no items). This new query node is not added to the parser, because
users may not use it in their own queries - it must be part of the
"fixed" query so the application can control in which subset to search
(it would otherwise be possible to select any items).
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Docspell - Document Management System (DMS)

Docspell is a personal document organizer. Or sometimes called a "Document Management System" (DMS). You'll need a scanner to convert your papers into files. Docspell can then assist in organizing the resulting mess 😉. It can unify your files from scanners, emails and other sources. It is targeted for home use, i.e. families, households and also for smaller groups/companies.

You can associate tags, set correspondends and lots of other predefined and custom metadata. If your documents are associated with such meta data, you can quickly find them later using the search feature. But adding this manually is a tedious task. Docspell can help by suggesting correspondents, guessing tags or finding dates using machine learning. It can learn metadata from existing documents and find things using NLP. This makes adding metadata to your documents a lot easier. For machine learning, it relies on the free (GPL) Stanford Core NLP library.

Docspell also runs OCR (if needed) on your documents, can provide fulltext search and has great e-mail integration. Everything is accessible via a REST/HTTP api. A mobile friendly SPA web application is the default user interface. An Android app exists for conveniently uploading files from your phone/tablet and a cli. The feature overview lists some more points.

Impressions

Checkout the short demo videos (<1min), present on the project page. Here are some screenshots:

screenshot-1 screenshot-2 screenshot-3 screenshot-4 screenshot-5 screenshot-5 screenshot-5

Try it

Docspell consists of several components. The probably quickest way to get started is to use the docker setup as described in the get started page. This is only three commands away:

git clone https://github.com/eikek/docspell
cd docspell/docker
DOCSPELL_HEADER_VALUE="my-secret-123" docker-compose up

Then go to http://localhost:7880, sign up and login. Use the same name for collective and user for now. More on that can be found here.

Other ways are documented here:

Documentation

The project page has lots of information on how to use and setup docspell.

Contributions/Support

Feedback and other contributions are very welcome! There is a gitter room for chatting and questions. You can also open an issue for questions, problems and other feedback; or make a mail to info [at] docspell.org. I'll try to answer in time. You may want to check Contributing.md for some starting points, too.

If you find this project any useful and want to support it via other means, giving a is always encouraging and much appreciated. You can also support activity by donating via liberapay or paypal.

Thank you so much!

Tech Stack

Backend

The servers is written in Scala in a pure functional style, based on libraries from the typelevel stack: Cats, FS2, Doobie, Http4s, Circe and Pureconfig.

There are more libraries and technologies used, of course. Docspell is only a orchestration of great tools and libs. One important is the Stanford-NLP, that provides the ML features. Furthermore, file processing relies on external tools like tesseract, unoconv and ocrmypdf. All dependencies can be looked up in project/Dependencies.scala.

Frontend

The web frontend is a SPA written in Elm. The UI framework in use is tailwind.

License

Docspell is free software, distributed under the GPLv3 or later.

Description
Assist in organizing your piles of documents, resulting from scanners, e-mails and other sources with miminal effort.
Readme 89 MiB
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