def name_only_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
This sets up various queries with boosting against the name field in the
elasticsearch index.
"""
d = {}
rules = {
'term': {'value': q, 'boost': 10}, # Exact match.
'text': {'query': q, 'boost': 3, 'analyzer': 'standard'},
'text': {'query': q, 'boost': 4, 'type': 'phrase'},
'fuzzy': {'value': q, 'boost': 2, 'prefix_length': 4},
'startswith': {'value': q, 'boost': 1.5}
}
for k, v in rules.iteritems():
for field in ('name', 'app_slug', 'author'):
d['%s__%s' % (field, k)] = v
analyzer = _get_locale_analyzer()
if analyzer:
d['name_%s__text' % analyzer] = {
'query': q, 'boost': 2.5,
'analyzer': get_custom_analyzer(analyzer)}
return d
def name_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
Note: This is marketplace specific. See apps/search/views.py for AMO.
"""
more = {
'description__match': {
'query': q,
'boost': 0.8,
'type': 'phrase'
},
}
analyzer = _get_locale_analyzer()
if analyzer:
more['description_%s__match' % analyzer] = {
'query': q,
'boost': 0.6,
'type': 'phrase',
'analyzer': get_custom_analyzer(analyzer)
}
more['tags__match'] = {'query': q}
if ' ' not in q:
more['tags__fuzzy'] = {'value': q, 'prefix_length': 1}
return dict(more, **name_only_query(q))
def name_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
Note: This is marketplace specific. See apps/search/views.py for AMO.
"""
more = {
'description__text': {
'query': q,
'boost': 0.8,
'type': 'phrase'
},
}
analyzer = _get_locale_analyzer()
if analyzer:
more['description_%s__text' % analyzer] = {
'query': q,
'boost': 0.6,
'type': 'phrase',
'analyzer': analyzer
}
more['tags__text'] = {'query': q}
return dict(more, **name_only_query(q))
def name_only_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
This sets up various queries with boosting against the name field in the
elasticsearch index.
"""
d = {}
rules = {
'match': {
'query': q,
'boost': 3,
'analyzer': 'standard'
},
'match': {
'query': q,
'boost': 4,
'type': 'phrase',
'slop': 1
},
'startswith': {
'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['fuzzy'] = {'value': q, 'boost': 2, 'prefix_length': 1}
for k, v in rules.iteritems():
for field in ('name', 'app_slug', 'author'):
d['%s__%s' % (field, k)] = 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.
d['name_sort__term'] = {'value': q, 'boost': 10}
analyzer = _get_locale_analyzer()
if analyzer:
d['name_%s__match' % analyzer] = {
'query': q,
'boost': 2.5,
'analyzer': get_custom_analyzer(analyzer)
}
return d
def name_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
Note: This is marketplace specific. See apps/search/views.py for AMO.
"""
more = {'description__text': {'query': q, 'boost': 0.3, 'type': 'phrase'}}
analyzer = _get_locale_analyzer()
if analyzer:
more['description_%s__text' % analyzer] = {'query': q,
'boost': 0.1,
'type': 'phrase',
'analyzer': analyzer}
return dict(more, **name_only_query(q))
def name_only_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
This sets up various queries with boosting against the name field in the
elasticsearch index.
"""
d = {}
rules = {
'term': {
'value': q,
'boost': 10
}, # Exact match.
'text': {
'query': q,
'boost': 3,
'analyzer': 'standard'
},
'text': {
'query': q,
'boost': 4,
'type': 'phrase'
},
'fuzzy': {
'value': q,
'boost': 2,
'prefix_length': 4
},
'startswith': {
'value': q,
'boost': 1.5
}
}
for k, v in rules.iteritems():
for field in ('name', 'app_slug', 'author'):
d['%s__%s' % (field, k)] = v
analyzer = _get_locale_analyzer()
if analyzer:
d['name_%s__text' % analyzer] = {
'query': q,
'boost': 2.5,
'analyzer': analyzer
}
return d
def name_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
Note: This is marketplace specific. See apps/search/views.py for AMO.
"""
more = {
'description__match': {'query': q, 'boost': 0.8, 'type': 'phrase'},
}
analyzer = _get_locale_analyzer()
if analyzer:
more['description_%s__match' % analyzer] = {
'query': q, 'boost': 0.6, 'type': 'phrase',
'analyzer': get_custom_analyzer(analyzer)}
more['tags__match'] = {'query': q}
if ' ' not in q:
more['tags__fuzzy'] = {'value': q, 'prefix_length': 1}
return dict(more, **name_only_query(q))
def name_only_query(q):
"""
Returns a dictionary with field/value mappings to pass to elasticsearch.
This sets up various queries with boosting against the name field in the
elasticsearch index.
"""
d = {}
rules = {
'match': {'query': q, 'boost': 3, 'analyzer': 'standard'},
'match': {'query': q, 'boost': 4, 'type': 'phrase'},
'startswith': {'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['fuzzy'] = {'value': q, 'boost': 2, 'prefix_length': 1}
for k, v in rules.iteritems():
for field in ('name', 'app_slug', 'author'):
d['%s__%s' % (field, k)] = 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.
d['name_sort__term'] = {'value': q, 'boost': 10}
analyzer = _get_locale_analyzer()
if analyzer:
d['name_%s__match' % analyzer] = {
'query': q, 'boost': 2.5,
'analyzer': get_custom_analyzer(analyzer)}
return d