The aim is to show off how to play a bit with searching text in Django (assuming Postgres as a db)

Levenshtein distance based search

What is Levenshtein distance?

(…) the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.(…) From: https://en.wikipedia.org/wiki/Levenshtein_distance

Why use it?

The most common goal is to enhance searching quality by providing user even if he misspell the search term.

Basic postgres Example

better_spots=# select levenshtein(name, 'Kfaka'), name from core_spot;

 levenshtein |          name
-------------+-------------------------
           6 | Szwejk
          22 | Charlotte. Chleb i Wino
          12 | Cafe Kulturalna
           2 | Kafka
           5 | Jeffs
          12 | Pardon to tu
           9 | Cafe Próżna
          17 | Drukarnia Jazz Club

In first collumn is the distance (number of changes required to get acctual term from Kfaka term). One can easily create filters by setting in WHERE clause that these distance is e.g <= 3.

Extension needed fuzzystrmatch

To run it you neeed adding postgres extension to your db in psql: CREATE EXTENSION fuzzystrmatch;

To add it in ansible role:

  - name: Add fuzzystrmatch extension
    become_user: postgres
    become: yes
    postgresql_ext:
      name: fuzzystrmatch
      db: better_spots

Getting there in Django:

Lets define custom function with wich we can annotate queryset. It just take one argument the search_term and uses postgres levenshtein function (see docs):

from django.db.models import Func

class Levenshtein(Func):
    template = "%(function)s(%(expressions)s, '%(search_term)s')"
    function = "levenshtein"

    def __init__(self, expression, search_term, **extras):
        super(Levenshtein, self).__init__(
            expression,
            search_term=search_term,
            **extras
        )

then in any other place in project we just import defined Levenshtein and F to pass the django field.

from django.db.models import F

Spot.objects.annotate(
    lev_dist=Levenshtein(F('name'), 'Kfaka')
).filter(
    lev_dist__lte=2
)

Disclaimer: Django offers since 1.10 trigram-similarity

Worth notice that Django offers since 1.10 trigram-similarity it may be fair enough for your needs.

Spot.objects.filter(name__trigram_similar="Kawka")

https://docs.djangoproject.com/en/2.0/ref/contrib/postgres/lookups/#trigram-similarity

it requires:

adding trigram extension to postgres CREATE EXTENSION pg_trgm;
having 'django.contrib.postgres' in your INSTALLED_APPS