Searching/filtering data

Alfred gives you the option of letting it filter your results against the user’s query. Alfred uses “word starts with” matching, and can handle many tens of thousands of results.

Workflow.filter() provides a more sophisticated algorithm, similar to the way Alfred matches applications with its default search (e.g. of will match OmniFocus, which doesn’t work with “Alfred filters results”). However, due to being written in Python (a much slower language than Objective-C) and the more complex algorithm, Workflow.filter() becomes noticeably sluggish with 1500–2500 items, depending on which options you’ve specified and the speed of the Mac running the worklow.

If you have a very large dataset (20,000+ items) and/or need more sophisticated matching than Alfred offers, I strongly recommend using sqlite and its fulltext search capability to store and search your data. It is smarter than Workflow.filter() and much faster than “Alfred filters results”, handling hundreds of thousands of items with ease.

Using “Alfred filters results”

“Alfred filters results” filters items based on their title field. Alfred 3.5 introduced the match field, which if present, will be used for filtering instead of title.

You can set this field via the match parameter of the Workflow3.add_item() method.

Fuzzy filtering with Alfred-Workflow

Workflow.filter() provides a “fuzzy” search algorithm for filtering your workflow’s data. By default, Workflow.filter() will try to match your search query via CamelCase, substring, initials and all characters, applying different weightings to the various kind of matches (see Workflow.filter() for a detailed description of the algorithm and match flags).

Best practice is to do the following:

def main(wf):

    query = None  # Ensure `query` is initialised

    # Set `query` if a value was passed (it may be an empty string)
    if len(wf.args):
        query = wf.args[0]

    items = load_my_items_from_somewhere()  # Load data from blah

    # If `query` is `None` or an empty string, all items are returned
    items = wf.filter(query, items)

    # Show error if there are no results. Otherwise, Alfred will show
    # its fallback searches (i.e. "Search Google for 'XYZ'")
    if not items:
        wf.add_item('No matches', icon=ICON_WARNING)

    # Generate list of results. If `items` is an empty list nothing happens
    for item in items:
        wf.add_item(item['title'], ...)

    wf.send_feedback()  # Send results to Alfred via STDOUT

This is by no means essential (wf.args[0] will always be set if the script is called from Alfred via python thescript.py "$1"), but it won’t work from the command line unless called with an empty string (python thescript.py ""), and it’s good to be aware of when you’re dealing with unset/empty variables.

Note

By default, Workflow.filter() will match and return anything that contains all the characters in query in the same order, regardless of case. Not only can this lead to unacceptable performance when working with thousands of items, but it’s also very likely that you’ll want to set the standard a little higher.

See Restricting results for info on how to do that.

To use Workflow.filter(), pass it a query, a list of items to filter and sort, and if your list contains items other than strings, a key function that generates a string search key for each item:

from workflow import Workflow

names = ['Bob Smith', 'Carrie Jones', 'Harry Johnson', 'Sam Butterkeks']

wf = Workflow()

hits = wf.filter('bs', names)

Which returns:

['Bob Smith', 'Sam Butterkeks']

(bs are Bob Smith’s initials and Butterkeks contains both letters in that order.)

If your data are not strings:

from workflow import Workflow

books = [
    {'title': 'A damn fine afternoon', 'author': 'Bob Smith'},
    {'title': 'My splendid adventure', 'author': 'Carrie Jones'},
    {'title': 'Bollards and other street treasures', 'author': 'Harry Johnson'},
    {'title': 'The horrors of Tuesdays', 'author': 'Sam Butterkeks'}
]


def key_for_book(book):
    return '{} {}'.format(book['title'], book['author'])

wf = Workflow()

hits = wf.filter('bot', books, key_for_book)

Which returns:

[{'author': 'Harry Johnson', 'title': 'Bollards and other street treasures'},
 {'author': 'Bob Smith', 'title': 'A damn fine afternoon'}]

Restricting results

Chances are, you would not want bot to match Bob Smith A damn fine afternoon at all, or indeed any of the other books. Indeed, they have very low scores:

hits = wf.filter('bot', books, key_for_book, include_score=True)

produces:

[({'author': 'Bob Smith', 'title': 'A damn fine afternoon'},
  11.11111111111111,
  64),
 ({'author': 'Harry Johnson', 'title': 'Bollards and other street treasures'},
  3.3333333333333335,
  64),
 ({'author': 'Sam Butterkeks', 'title': 'The horrors of Tuesdays'}, 3.125, 64)]

(64 is the rule that matched, MATCH_ALLCHARS, which matches if all the characters in query appear in order in the search key, regardless of case).

Tip

rules in filter() results are returned as integers. To see the name of the corresponding rule, see Matching rules.

If we filter {'author': 'Brienne of Tarth', 'title': 'How to beat up men'} and {'author': 'Zoltar', 'title': 'Battle of the Planets'}, which we probably would want to match bot, we get:

[({'author': 'Zoltar', 'title': 'Battle of the Planets'}, 98.0, 8),
 ({'author': 'Brienne of Tarth', 'title': 'How to beat up men'}, 90.0, 16)]

(The ranking would be reversed if key_for_book() returned author title instead of title author.)

So in all likelihood, you’ll want to pass a min_score argument to Workflow.filter():

hits = wf.filter('bot', books, key_for_book, min_score=20)

and/or exclude some of the matching rules:

from workflow import Workflow, MATCH_ALL, MATCH_ALLCHARS

# [...]

hits = wf.filter('bot', books, key_for_book, match_on=MATCH_ALL ^ MATCH_ALLCHARS)

You can set match rules using bitwise operators, so | to combine them or ^ to remove them from MATCH_ALL:

# match only CamelCase and initials
match_on=MATCH_CAPITALS | MATCH_INITIALS

# match everything but all-characters-in-item and substring
match_on=MATCH_ALL ^ MATCH_ALLCHARS ^ MATCH_SUBSTRING

Warning

MATCH_ALLCHARS is particularly slow and provides the worst matches. You should consider excluding it, especially if you’re calling Workflow.filter() with more than a few hundred items or expect multi-word queries.

Diacritic folding

By default, Workflow.filter() will fold non-ASCII characters to approximate ASCII equivalents (e.g. é > e, ü > u) if query contains only ASCII characters. This behaviour can be turned off by passing fold_diacritics=False to Workflow.filter().

Note

To keep the library small, only a subset of European languages are supported. The Unidecode library should be used for comprehensive support of non-European alphabets.

Users may override a Workflow’s default settings via workflow:folding… magic arguments.

“Smart” punctuation

The default diacritic folding only alters letters, not punctuation. If your workflow also works with text that contains so-called “smart” (i.e. curly) quotes or n- and m-dashes, you can use the Workflow.dumbify_punctuation() method to replace smart quotes and dashes with normal quotes and hyphens respectively.

Matching rules

Here are the MATCH_* constants from workflow and their numeric values.

For a detailed description of the rules see Workflow.filter().

Name	Value
MATCH_STARTSWITH	1
MATCH_CAPITALS	2
MATCH_ATOM	4
MATCH_INITIALS_STARTSWITH	8
MATCH_INITIALS_CONTAIN	16
MATCH_INITIALS	24
MATCH_SUBSTRING	32
MATCH_ALLCHARS	64
MATCH_ALL	127