def __init__(self, query, **kwargs):
# self.query = [token.text for token in sa(query)]
self.query = query
if "max_length" in kwargs:
self.max_length = int(kwargs["max_length"])
if "html_tag" in kwargs:
self.html_tag = kwargs["html_tag"]
if "css_class" in kwargs:
self.css_class = kwargs["css_class"]
# self.query_words = set(
# [word.lower() for word in self.query.split() if not word.startswith("-")]
# )
sa = StemmingAnalyzer()
self.query_words = set(
[token.text.lower() for token in sa(query) if token.text.replace(" ",'') != '']
)
def _process_results(
self,
raw_page,
highlight=False,
query_string="",
spelling_query=None,
result_class=None,
):
from haystack import connections
results = []
# It's important to grab the hits first before slicing. Otherwise, this
# can cause pagination failures.
hits = len(raw_page)
if result_class is None:
result_class = SearchResult
facets = {}
spelling_suggestion = None
unified_index = connections[self.connection_alias].get_unified_index()
indexed_models = unified_index.get_indexed_models()
for doc_offset, raw_result in enumerate(raw_page):
score = raw_page.score(doc_offset) or 0
app_label, model_name = raw_result[DJANGO_CT].split(".")
additional_fields = {}
model = haystack_get_model(app_label, model_name)
if model and model in indexed_models:
for key, value in raw_result.items():
index = unified_index.get_index(model)
string_key = str(key)
if string_key in index.fields and hasattr(
index.fields[string_key], "convert"):
# Special-cased due to the nature of KEYWORD fields.
if index.fields[string_key].is_multivalued:
if value is None or len(value) is 0:
additional_fields[string_key] = []
else:
additional_fields[string_key] = value.split(
",")
else:
additional_fields[string_key] = index.fields[
string_key].convert(value)
else:
additional_fields[string_key] = self._to_python(value)
del (additional_fields[DJANGO_CT])
del (additional_fields[DJANGO_ID])
if highlight:
sa = StemmingAnalyzer()
formatter = WhooshHtmlFormatter("em")
terms = [token.text for token in sa(query_string)]
whoosh_result = whoosh_highlight(
additional_fields.get(self.content_field_name),
terms,
sa,
ContextFragmenter(),
formatter,
)
additional_fields["highlighted"] = {
self.content_field_name: [whoosh_result]
}
result = result_class(app_label, model_name,
raw_result[DJANGO_ID], score,
**additional_fields)
results.append(result)
else:
hits -= 1
if self.include_spelling:
if spelling_query:
spelling_suggestion = self.create_spelling_suggestion(
spelling_query)
else:
spelling_suggestion = self.create_spelling_suggestion(
query_string)
return {
"results": results,
"hits": hits,
"facets": facets,
"spelling_suggestion": spelling_suggestion,
}
def build_schema(self, fields):
schema_fields = {
ID: WHOOSH_ID(stored=True, unique=True),
DJANGO_CT: WHOOSH_ID(stored=True),
DJANGO_ID: WHOOSH_ID(stored=True),
}
# Grab the number of keys that are hard-coded into Haystack.
# We'll use this to (possibly) fail slightly more gracefully later.
initial_key_count = len(schema_fields)
content_field_name = ""
for field_name, field_class in fields.items():
if field_class.is_multivalued:
if field_class.indexed is False:
schema_fields[field_class.index_fieldname] = IDLIST(
stored=True, field_boost=field_class.boost)
else:
schema_fields[field_class.index_fieldname] = KEYWORD(
stored=True,
commas=True,
scorable=True,
field_boost=field_class.boost,
)
elif field_class.field_type in ["date", "datetime"]:
schema_fields[field_class.index_fieldname] = DATETIME(
stored=field_class.stored, sortable=True)
elif field_class.field_type == "integer":
schema_fields[field_class.index_fieldname] = NUMERIC(
stored=field_class.stored,
numtype=int,
field_boost=field_class.boost,
)
elif field_class.field_type == "float":
schema_fields[field_class.index_fieldname] = NUMERIC(
stored=field_class.stored,
numtype=float,
field_boost=field_class.boost,
)
elif field_class.field_type == "boolean":
# Field boost isn't supported on BOOLEAN as of 1.8.2.
schema_fields[field_class.index_fieldname] = BOOLEAN(
stored=field_class.stored)
elif field_class.field_type == "ngram":
schema_fields[field_class.index_fieldname] = NGRAM(
minsize=3,
maxsize=15,
stored=field_class.stored,
field_boost=field_class.boost,
)
elif field_class.field_type == "edge_ngram":
schema_fields[field_class.index_fieldname] = NGRAMWORDS(
minsize=2,
maxsize=15,
at="start",
stored=field_class.stored,
field_boost=field_class.boost,
)
else:
schema_fields[field_class.index_fieldname] = TEXT(
stored=True,
analyzer=StemmingAnalyzer(),
field_boost=field_class.boost,
sortable=True,
)
if field_class.document is True:
content_field_name = field_class.index_fieldname
schema_fields[field_class.index_fieldname].spelling = True
# Fail more gracefully than relying on the backend to die if no fields
# are found.
if len(schema_fields) <= initial_key_count:
raise SearchBackendError(
"No fields were found in any search_indexes. Please correct this before attempting to search."
)
return (content_field_name, Schema(**schema_fields))