Django's haystack provides autocomplete functionality. To do autocomplete effectively, the search backend(elasticsearch in this case) uses n-grams (essentially a small window passed over the string). Because this alters the way your data needs to be stored. We had two choices: NgramField and EdgeNgramField used as n-grams in search backend. The major drawback of the n-grams is that they take minimum of 3 letters in the search query. Still EdgeNgramField or NgramField fields won't produce consistent results(For Eg: . By customising the Haystack's built in elastcisearch engine backend we can achieve single letter query with Charfield itself.
In your search_indexes.py
from haystack import indexes
from myapp.models import Book
class BookIndex(indexes.SearchIndex, indexes.Indexable):
text = indexes.CharField(document=True, use_template=True)
title = indexes.CharField(model_attr='title')
pub_date = indexes.DateTimeField(model_attr='pub_date')
def get_model(self):
return Book
Create backends.py in your app. And this file will contain the actual customized elasticsearch search engine backend.
from django.conf import settings
from haystack.backends.elasticsearch_backend import ElasticsearchSearchBackend
from haystack.backends.elasticsearch_backend import ElasticsearchSearchEngine
from haystack.backends.elasticsearch_backend import ElasticsearchSearchQuery
from haystack.constants import VALID_FILTERS, FILTER_SEPARATOR, DEFAULT_ALIAS
from haystack.inputs import Clean, Exact, PythonData, Raw
from django.utils import six
class CustomElasticsearchSearchQuery(ElasticsearchSearchQuery):
def __init__(self, using=DEFAULT_ALIAS):
super(CustomElasticsearchSearchQuery, self).__init__(using=DEFAULT_ALIAS)
def build_query_fragment(self, field, filter_type, value):
from haystack import connections
query_frag = ''
if not hasattr(value, 'input_type_name'):
# Handle when we've got a ``ValuesListQuerySet``...
if hasattr(value, 'values_list'):
value = list(value)
if isinstance(value, six.string_types):
# It's not an ``InputType``. Assume ``Clean``.
value = Clean(value)
else:
value = PythonData(value)
# Prepare the query using the InputType.
prepared_value = value.prepare(self)
if not isinstance(prepared_value, (set, list, tuple)):
# Then convert whatever we get back to what pysolr wants if needed.
prepared_value = self.backend._from_python(prepared_value)
# 'content' is a special reserved word, much like 'pk' in
# Django's ORM layer. It indicates 'no special field'.
if field == 'content':
index_fieldname = ''
else:
index_fieldname = u'%s:' % connections[self._using].get_unified_index().get_index_fieldname(field)
filter_types = {
'contains': u'*%s*',
'startswith': u'%s*',
'exact': u'%s',
'gt': u'{%s TO *}',
'gte': u'[%s TO *]',
'lt': u'{* TO %s}',
'lte': u'[* TO %s]',
}
if value.post_process is False:
query_frag = prepared_value
else:
if filter_type in ['contains', 'startswith']:
if value.input_type_name == 'exact':
query_frag = prepared_value
else:
# Iterate over terms & incorportate the converted form of each into the query.
terms = []
if isinstance(prepared_value, six.string_types):
for possible_value in prepared_value.split(' '):
terms.append(filter_types[filter_type] % self.backend._from_python(possible_value))
else:
terms.append(filter_types[filter_type] % self.backend._from_python(prepared_value))
if len(terms) == 1:
query_frag = terms[0]
else:
query_frag = u"(%s)" % " AND ".join(terms)
elif filter_type == 'in':
in_options = []
for possible_value in prepared_value:
in_options.append(u'"%s"' % self.backend._from_python(possible_value))
query_frag = u"(%s)" % " OR ".join(in_options)
elif filter_type == 'range':
start = self.backend._from_python(prepared_value[0])
end = self.backend._from_python(prepared_value[1])
query_frag = u'["%s" TO "%s"]' % (start, end)
elif filter_type == 'exact':
if value.input_type_name == 'exact':
query_frag = prepared_value
else:
prepared_value = Exact(prepared_value).prepare(self)
query_frag = filter_types[filter_type] % prepared_value
else:
if value.input_type_name != 'exact':
prepared_value = Exact(prepared_value).prepare(self)
query_frag = filter_types[filter_type] % prepared_value
if len(query_frag) and not isinstance(value, Raw):
if not query_frag.startswith('(') and not query_frag.endswith(')'):
query_frag = "(%s)" % query_frag
return u"%s%s" % (index_fieldname, query_frag)
class ConfigurableElasticSearchEngine(ElasticsearchSearchEngine):
query = CustomElasticsearchSearchQuery
In the above snippet 'contains': u'*%s*' is doing the job for us to allow single letter querying which provides better results. Add the ConfigurableElasticSearchEngine in your settings.py
HAYSTACK_CONNECTIONS = {
'default': {
'ENGINE': 'myapp.backends.ConfigurableElasticSearchEngine',
'URL': 'http://localhost:9200/',
'INDEX_NAME': 'my_index',
},
}
Now we are done with the customization. The customised searchengine's example search would look like:
SearchQuerySet().models(Book).filter_and(title=<query_string>) Eg1: SearchQuerySet().models(Book).filter_and(title='m') # Will give results of books with title 'mcrop', 'micropyramid', 'mycropyramid' and all other titles which are having letter 'm' in them. Eg2: SearchQuerySet().models(Book).filter_and(title='mi') # Will give results of books with title 'micropyramid'.
Note: Here __contains is optional as Haystack2.X added __contains as default for filtering the data.