def name_query(q):
"""
Returns a boolean should query `elasticsearch_dsl.query.Bool` given a
query string.
"""
should = []
rules = {
query.Match: {
'query': q,
'boost': 3,
'analyzer': 'standard'
},
query.Match: {
'query': q,
'boost': 4,
'type': 'phrase',
'slop': 1
},
query.Prefix: {
'value': q,
'boost': 1.5
}
}
# Only add fuzzy queries if q is a single word. It doesn't make sense to do
# a fuzzy query for multi-word queries.
if ' ' not in q:
rules[query.Fuzzy] = {'value': q, 'boost': 2, 'prefix_length': 1}
for k, v in rules.iteritems():
for field in ('name', 'app_slug', 'author'):
should.append(k(**{field: v}))
# Exact matches need to be queried against a non-analyzed field. Let's do a
# term query on `name_sort` for an exact match against the app name and
# give it a good boost since this is likely what the user wants.
should.append(query.Term(name_sort={'value': q, 'boost': 10}))
analyzer = _get_locale_analyzer()
if analyzer:
should.append(
query.Match(**{'name_%s' % analyzer: {
'query': q,
'boost': 2.5
}}))
# Add searches on the description field.
should.append(
query.Match(description={
'query': q,
'boost': 0.8,
'type': 'phrase'
}))
analyzer = _get_locale_analyzer()
if analyzer:
should.append(
query.Match(
**{
'description_%s' % analyzer: {
'query': q,
'boost': 0.6,
'type': 'phrase',
'analyzer': get_custom_analyzer(analyzer)
}
}))
# Add searches on tag field.
should.append(query.Match(tags={'query': q}))
if ' ' not in q:
should.append(query.Fuzzy(tags={'value': q, 'prefix_length': 1}))
return query.Bool(should=should)
def test_fuzzy_to_dict():
assert {"fuzzy": {"f": "value"}} == query.Fuzzy(f='value').to_dict()
def filter_queryset(self, request, queryset, view):
q = request.GET.get('q', '').lower()
lang = translation.get_language()
analyzer = self._get_locale_analyzer(lang)
if not q:
return queryset
should = []
rules = [
(query.Match, {
'query': q,
'boost': 3,
'analyzer': 'standard'
}),
(query.Match, {
'query': q,
'boost': 4,
'type': 'phrase',
'slop': 1
}),
(query.Prefix, {
'value': q,
'boost': 1.5
}),
]
# Only add fuzzy queries if q is a single word. It doesn't make sense
# to do a fuzzy query for multi-word queries.
if ' ' not in q:
rules.append((query.Fuzzy, {
'value': q,
'boost': 2,
'prefix_length': 1
}))
# Apply rules to search on few base fields. Some might not be present
# in every document type / indexes.
for k, v in rules:
for field in ('app_slug', 'author', 'name', 'short_name', 'slug',
'title', 'url_tokenized'):
should.append(k(**{field: v}))
# Exact matches need to be queried against a non-analyzed field. Let's
# do a term query on `name.raw` for an exact match against the item
# name and give it a good boost since this is likely what the user
# wants.
# FIXME: we should also do that on translations and slug/app_slug, but
# we don't store a raw version for them at the moment.
should.append(query.Term(**{'name.raw': {'value': q, 'boost': 10}}))
# Do the same for GUID searches.
should.append(query.Term(**{'guid': {'value': q, 'boost': 10}}))
# If query is numeric, check if it is an ID.
if q.isnumeric():
should.append(query.Term(**{'id': {'value': q, 'boost': 10}}))
if analyzer:
should.append(
query.Match(
**{'name_l10n_%s' % analyzer: {
'query': q,
'boost': 2.5
}}))
should.append(
query.Match(**{
'short_name_l10n_%s' % analyzer: {
'query': q,
'boost': 2.5
}
}))
# Add searches on the description field.
should.append(
query.Match(description={
'query': q,
'boost': 0.8,
'type': 'phrase'
}))
if analyzer:
desc_field = 'description_l10n_%s' % analyzer
desc_analyzer = ('%s_analyzer' % analyzer
if analyzer in mkt.STEMMER_MAP else analyzer)
should.append(
query.Match(
**{
desc_field: {
'query': q,
'boost': 0.6,
'type': 'phrase',
'analyzer': desc_analyzer
}
}))
# Add searches on tag field.
should.append(query.Term(tags={'value': q}))
if ' ' not in q:
should.append(query.Fuzzy(tags={'value': q, 'prefix_length': 1}))
# The list of functions applied to our `function_score` query.
functions = [
query.SF('field_value_factor', field='boost'),
]
# Add a boost for the preferred region, if it exists.
region = get_region_from_request(request)
if region:
functions.append({
'filter': {
'term': {
'preferred_regions': region.id
}
},
# TODO: When we upgrade to Elasticsearch 1.4, change this
# to 'weight'.
'boost_factor': 4,
})
return queryset.query('function_score',
query=query.Bool(should=should),
functions=functions)
