Add a database model for storing AI-predicted tags, and add a UI for browsing and searching these tags.
AI tags are generated by the Danbooru Autotagger (https://github.com/danbooru/autotagger). See that
repo for details about the model.
The database schema is `ai_tags (media_asset_id integer, tag_id integer, score smallint)`. This is
designed to be as space-efficient as possible, since in production we have over 300 million
AI-generated tags (6 million images and 50 tags per post). This amounts to over 10GB in size, plus
indexes.
You can search for AI tags using e.g. `ai:scenery`. You can do `ai:scenery -scenery` to find posts
where the scenery tag is potentially missing, or `scenery -ai:scenery` to find posts that are
potentially mistagged (or more likely where the AI missed the tag).
You can browse AI tags at https://danbooru.donmai.us/ai_tags. On this page you can filter by
confidence level. You can also search unposted media assets by AI tag.
To generate tags, use the `autotag` script from the Autotagger repo, something like this:
docker run --rm -v ~/danbooru/public/data/360x360:/images ghcr.io/danbooru/autotagger ./autotag -c -f /images | gzip > tags.csv.gz
To import tags, use the fix script in script/fixes/. Expect a Danbooru-size dataset to take
hours to days to generate tags, then 20-30 minutes to import. Currently this all has to be done by hand.
Add a new tag tag search parser that supports full boolean expressions, including `and`,
`or`, and `not` operators and parenthesized subexpressions.
This is only the parser itself, not the code for converting the search into SQL. The new
parser isn't used yet for actual searches. Searches still use the old parser.
Some example syntax:
* `1girl 1boy`
* `1girl and 1boy` (same as `1girl 1boy`)
* `1girl or 1boy`
* `~1girl ~1boy` (same as `1girl or 1boy`)
* `1girl and ((blonde_hair blue_eyes) or (red_hair green_eyes))`
* `1girl ~(blonde_hair blue_eyes) ~(red_hair green_eyes)` (same as above)
* `1girl -(blonde_hair blue_eyes)`
* `*_hair *_eyes`
* `*_hair or *_eyes`
* `user:evazion or fav:evazion`
* `~user:evazion ~fav:evazion`
Rules:
AND is implicit between terms, but may be written explicitly:
* `a b c` is `a and b and c`
AND has higher precedence (binds tighter) than OR:
* `a or b and c or d` is `a or (b and c) or d`
* `a or b c or d e` is `a or (b and c) or (d and e)`
All `~` operators in the same subexpression are combined into a single OR:
* `a b ~c ~d` is `a b (c or d)`
* `~a ~b and ~c ~d` is `(a or b) (c or d)`
* `(~a ~b) (~c ~d)` is `(a or b) (c or d)`
A single `~` operator in a subexpression by itself is ignored:
* `a ~b` is `a b`
* `~a and ~b` is `a and b`, which is `a b`
* `(~a) ~b` is `a ~b`, which is `a b`
The parser is written as a backtracking recursive descent parser built on top of
StringScanner and a handful of parser combinators. The parser generates an AST, which is
then simplified using Boolean algebra to remove redundant nodes and to convert the
expression to conjunctive normal form (that is, a product of sums, or an AND of ORs).
Standardize font sizes and heading tags (<h1>-<h6>) to be more
consistent across the site.
Changes:
* Introduce font size CSS variables and start replacing hardcoded font
sizes with standard sizes.
* Change header tags to use only one <h1> per page. One <h1> per page is
recommended for SEO purposes. Usually this is for the page title, like
in forum threads or wiki pages.
* Standardize on <h2> for section headers in sidebars and <h3> for
smaller subsection headers. Don't use <h4>-<h6>.
* In DText, make h1-h4 headers all the same size. Standard wiki style is
to ignore h1-h3 and start at h4.
* In DText, make h4-h6 the same size as the h1-h3 tags outside of DText.
* In the tag list, change the <h1> and <h2> tag category headers to <h3>.
* Make usernames in comments and forum posts smaller. Also change the
<h4> tag for the commenter name to <div class="author-name">.
* Make the tag list, paginator, and nav menu smaller on mobile.
* Change h1#app-name-header to a#app-name-header.
