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search.py
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search.py
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import urllib
from django.db.models import Case, When, IntegerField
from elasticsearch_dsl import FacetedSearch, Q
from pydash import compact, get
from core.common.utils import is_url_encoded_string
class CustomESFacetedSearch(FacetedSearch):
def __init__(self, query=None, filters={}, sort=(), exact_match=False): # pylint: disable=dangerous-default-value
self.exact_match = exact_match
super().__init__(query=query, filters=filters, sort=sort)
def format_search_str(self, search_str):
if self.exact_match:
return search_str.replace('*', '')
return f"{search_str}*".replace('**', '*')
def query(self, search, query):
if query:
search_str = self.format_search_str(query)
if self.fields:
return search.filter('query_string', fields=self.fields, query=search_str)
return search.query('multi_match', query=search_str)
return search
def params(self, **kwargs):
self._s = self._s.params(**kwargs)
class CustomESSearch:
def __init__(self, dsl_search):
self._dsl_search = dsl_search
self.queryset = None
self.max_score = None
self.scores = {}
self.highlights = {}
self.score_stats = None
self.score_distribution = None
self.total = 0
@staticmethod
def get_wildcard_search_string(_str):
return f"{_str}*".replace('**', '*')
@staticmethod
def get_search_string(search_str, lower=True, decode=True):
if lower:
search_str = search_str.lower()
if decode:
search_str = search_str.replace('**', '*')
starts_with_asterisk = search_str.startswith('*')
ends_with_asterisk = search_str.endswith('*')
if starts_with_asterisk:
search_str = search_str[1:]
if ends_with_asterisk:
search_str = search_str[:-1]
search_str = search_str if is_url_encoded_string(search_str) else urllib.parse.quote_plus(search_str)
if starts_with_asterisk:
search_str = f'*{search_str}'
if ends_with_asterisk:
search_str = f'{search_str}*'
return search_str
@staticmethod
def get_fuzzy_match_criterion(search_str, fields, boost_divide_by=10, expansions=5):
criterion = None
for attr, meta in fields.items():
criteria = CustomESSearch.fuzzy_criteria(search_str, attr, meta['boost'] / boost_divide_by, expansions)
if criterion is None:
criterion = criteria
else:
criterion |= criteria
return criterion
@staticmethod
def get_wildcard_match_criterion(search_str, fields):
def get_query(_str):
query = None
for attr, meta in fields.items():
decode = meta['decode'] if 'decode' in meta else True
lower = meta['lower'] if 'lower' in meta else True
_search_str = CustomESSearch.get_wildcard_search_string(
CustomESSearch.get_search_string(search_str, decode=decode, lower=lower)
)
criteria = CustomESSearch.get_wildcard_criteria(attr, _search_str, meta['boost'])
if query is None:
query = criteria
else:
query |= criteria
return query
if not search_str:
return get_query(search_str)
words = search_str.split()
criterion = get_query(words[0])
for word in words[1:]:
criterion |= get_query(word)
return criterion
@staticmethod
def get_exact_match_criterion(
search_str, match_phrase_fields_list, match_word_fields_map):
criterion = None
if match_phrase_fields_list:
criterion = CustomESSearch.get_match_phrase_criteria(match_phrase_fields_list[0], search_str, 5)
for attr in match_phrase_fields_list[1:]:
criterion |= CustomESSearch.get_match_phrase_criteria(attr, search_str, 5)
for field, meta in match_word_fields_map.items():
criteria = CustomESSearch.get_match_criteria(field, search_str, meta['boost'])
if criterion is None:
criterion = criteria
criterion |= criteria
return criterion
@staticmethod
def get_match_phrase_criteria(field, search_str, boost):
criteria = CustomESSearch.get_term_match_criteria(field, search_str, boost)
if field == 'external_id':
return criteria
return criteria | CustomESSearch.get_prefix_criteria(
field, search_str, boost
) | Q('match_phrase', **{field: {'query': search_str, 'boost': boost}})
@staticmethod
def get_term_match_criteria(field, search_str, boost):
return Q('term', **{field: {'value': search_str, 'boost': boost + 100}})
@staticmethod
def get_prefix_criteria(field, search_str, boost):
return Q('prefix', **{field: {'value': search_str, 'boost': boost + 95}})
@staticmethod
def get_match_criteria(field, search_str, boost):
return Q('match', **{field: {'query': search_str, 'boost': boost}})
@staticmethod
def get_wildcard_criteria(field, search_str, boost):
return Q("wildcard", **{field: {'value': search_str, 'boost': boost, 'case_insensitive': True}})
@staticmethod
def fuzzy_criteria(search_str, field, boost=0, max_expansions=10):
criterion = CustomESSearch.__fuzzy_criteria(boost, field, max_expansions, search_str)
words = compact(search_str.split())
if len(words) > 1:
for word in words:
criterion |= CustomESSearch.__fuzzy_criteria(boost, field, max_expansions, word)
return criterion
@staticmethod
def __fuzzy_criteria(boost, field, max_expansions, word):
return Q(
{'fuzzy': {field: {'value': word, 'boost': boost, 'fuzziness': 'AUTO', 'max_expansions': max_expansions}}})
def apply_aggregation_score_histogram(self):
self._dsl_search.aggs.bucket(
"distribution", "histogram", script="_score", interval=1, min_doc_count=1)
def apply_aggregation_score_stats(self):
self._dsl_search.aggs.bucket("score", "stats", script="_score")
def to_queryset(self, keep_order=True):
"""
This method return a django queryset from the an elasticsearch result.
It cost a query to the sql db.
"""
s, hits = self.__get_response()
for result in hits.hits:
_id = get(result, '_id')
self.scores[int(_id)] = get(result, '_score')
highlight = get(result, 'highlight')
if highlight:
self.highlights[int(_id)] = highlight.to_dict()
pks = [result.meta.id for result in s]
qs = self._dsl_search._model.objects.filter(pk__in=pks) # pylint: disable=protected-access
if keep_order:
preserved_order = Case(
*[When(pk=pk, then=pos) for pos, pk in enumerate(pks)],
output_field=IntegerField()
)
qs = qs.order_by(preserved_order)
self.queryset = qs
self.total = hits.total.value
def get_aggregations(self, verbose=False, raw=False):
s, _ = self.__get_response()
result = s.aggs.to_dict()
if raw:
return result
self.max_score = result['score']['max']
return self._get_score_buckets(
self.max_score, result['distribution']['buckets'], verbose)
@staticmethod
def _get_score_buckets(max_score, buckets, verbose=False):
high_threshold = max_score * 0.8
low_threshold = max_score * 0.5
def get_confidence(threshold):
return round((threshold/max_score) * 100, 2)
def build_confidence(_bucket):
scores = _bucket['scores']
if scores:
_bucket['confidence'] = f"~{get_confidence(sum(scores) / len(scores))}%"
if not verbose:
_bucket = {k: v for k, v in _bucket.items() if k in ['name', 'threshold', 'total', 'confidence']}
return _bucket
def build_bucket(name, confidence_threshold, threshold=None, confidence_prefix='>='):
threshold = threshold or confidence_threshold
return {
'name': name,
'threshold': round(threshold, 2),
'scores': [],
'doc_counts': [],
'confidence': f"{confidence_prefix}{get_confidence(confidence_threshold)}%",
'total': 0
}
def append_to_bucket(_bucket, _score, count):
_bucket['scores'].append(_score)
_bucket['doc_counts'].append(count)
_bucket['total'] += count
high = build_bucket('high', high_threshold)
medium = build_bucket('medium', low_threshold)
low = build_bucket('low', low_threshold, 0.01, '<')
for bucket in buckets:
score = bucket['key']
doc_count = bucket['doc_count']
if score >= high_threshold:
append_to_bucket(high, score, doc_count)
elif score < low_threshold:
append_to_bucket(low, score, doc_count)
else:
append_to_bucket(medium, score, doc_count)
return [build_confidence(high), build_confidence(medium), build_confidence(low)]
def __get_response(self):
# Do not query again if the es result is already cached
if not hasattr(self._dsl_search, '_response'):
# We only need the meta fields with the models ids
s = self._dsl_search.source(excludes=['*'])
s = s.execute()
hits = s.hits
self.max_score = hits.max_score
return s, hits
return self._dsl_search, None