def update(self, index, iterable, commit=True):
if not self.setup_complete:
self.setup()
self.index = self.index.refresh()
writer = AsyncWriter(self.index)
for obj in iterable:
doc = index.full_prepare(obj)
# Really make sure it's unicode, because Whoosh won't have it any
# other way.
for key in doc:
doc[key] = self._from_python(doc[key])
writer.update_document(**doc)
if len(iterable) > 0:
# For now, commit no matter what, as we run into locking issues otherwise.
writer.commit()
# If spelling support is desired, add to the dictionary.
if getattr(settings, 'HAYSTACK_INCLUDE_SPELLING', False) is True:
sp = SpellChecker(self.storage)
sp.add_field(self.index, self.content_field_name)
def update(self, index, iterable, commit=True):
if not self.setup_complete:
self.setup()
self.index = self.index.refresh()
writer = self.index.writer()
for obj in iterable:
doc = {}
doc['id'] = force_unicode(self.get_identifier(obj))
doc['django_ct'] = force_unicode("%s.%s" % (obj._meta.app_label, obj._meta.module_name))
doc['django_id'] = force_unicode(obj.pk)
other_data = index.prepare(obj)
# Really make sure it's unicode, because Whoosh won't have it any
# other way.
for key in other_data:
other_data[key] = self._from_python(other_data[key])
doc.update(other_data)
writer.update_document(**doc)
# For now, commit no matter what, as we run into locking issues otherwise.
writer.commit()
# If spelling support is desired, add to the dictionary.
if getattr(settings, 'HAYSTACK_INCLUDE_SPELLING', False) is True:
sp = SpellChecker(self.storage)
sp.add_field(self.index, self.content_field_name)
def update(self, documents, commit=True):
writer = self.index.writer()
for doc in documents:
writer.update_document(**doc)
if commit is True:
writer.commit()
# If spelling support is desired, add to the dictionary.
if self.include_spelling is True:
sp = SpellChecker(self.storage)
sp.add_field(self.index, self.content_field_name)
def run_query(query, index):
"""
Queries the index for data with the given text query
@param query The text query to perform on the indexed data
@return A list of HTMl string snippets to return
"""
# Create a searcher object for this index
searcher = index.searcher()
# Create a query parser that will parse multiple fields of the documents
field_boosts = {
'content': 1.0,
'title': 3.0
}
query_parser = MultifieldParser(['content', 'title'], schema=index_schema, fieldboosts=field_boosts, group=OrGroup)
# Build a query object from the query string
query_object = query_parser.parse(query)
# Build a spell checker in this index and add the "content" field to the spell checker
spell_checker = SpellChecker(index.storage)
spell_checker.add_field(index, 'content')
spell_checker.add_field(index, 'title')
# Extract the 'terms' that were found in the query string. This data can be used for highlighting the results
search_terms = [text for fieldname, text in query_object.all_terms()]
# Remove terms that are too short
for search_term in search_terms:
if len(search_term) <= 3:
search_terms.remove(search_term)
# Perform the query itself
search_results = searcher.search(query_object)
# Get an analyzer for analyzing the content of each page for highlighting
analyzer = index_schema['content'].format.analyzer
# Build the fragmenter object, which will automatically split up excerpts. This fragmenter will split up excerpts
# by 'context' in the content
fragmenter = ContextFragmenter(frozenset(search_terms))
# Build the formatter, which will dictate how to highlight the excerpts. In this case, we want to use HTML to
# highlight the results
formatter = HtmlFormatter()
# Iterate through the search results, highlighting and counting the results
result_count = 0
results = []
for search_result in search_results:
# Collect this search result
results.append({
'content': highlight(search_result['content'], search_terms, analyzer, fragmenter, formatter),
'url': search_result['url'],
'title': search_result['title']
})
result_count += 1
# Build a list of 'suggest' words using the spell checker
suggestions = []
for term in search_terms:
suggestions.append(spell_checker.suggest(term))
# Return the list of web pages along with the terms used in the search
return results, search_terms, suggestions, result_count
def run_query(query, index):
"""
Queries the index for data with the given text query
@param query The text query to perform on the indexed data
@return A list of HTMl string snippets to return
"""
# Create a searcher object for this index
searcher = index.searcher()
# Create a query parser that will parse multiple fields of the documents
field_boosts = {'content': 1.0, 'title': 3.0}
query_parser = MultifieldParser(['content', 'title'],
schema=index_schema,
fieldboosts=field_boosts,
group=OrGroup)
# Build a query object from the query string
query_object = query_parser.parse(query)
# Build a spell checker in this index and add the "content" field to the spell checker
spell_checker = SpellChecker(index.storage)
spell_checker.add_field(index, 'content')
spell_checker.add_field(index, 'title')
# Extract the 'terms' that were found in the query string. This data can be used for highlighting the results
search_terms = [text for fieldname, text in query_object.all_terms()]
# Remove terms that are too short
for search_term in search_terms:
if len(search_term) <= 3:
search_terms.remove(search_term)
# Perform the query itself
search_results = searcher.search(query_object)
# Get an analyzer for analyzing the content of each page for highlighting
analyzer = index_schema['content'].format.analyzer
# Build the fragmenter object, which will automatically split up excerpts. This fragmenter will split up excerpts
# by 'context' in the content
fragmenter = ContextFragmenter(frozenset(search_terms))
# Build the formatter, which will dictate how to highlight the excerpts. In this case, we want to use HTML to
# highlight the results
formatter = HtmlFormatter()
# Iterate through the search results, highlighting and counting the results
result_count = 0
results = []
for search_result in search_results:
# Collect this search result
results.append({
'content':
highlight(search_result['content'], search_terms, analyzer,
fragmenter, formatter),
'url':
search_result['url'],
'title':
search_result['title']
})
result_count += 1
# Build a list of 'suggest' words using the spell checker
suggestions = []
for term in search_terms:
suggestions.append(spell_checker.suggest(term))
# Return the list of web pages along with the terms used in the search
return results, search_terms, suggestions, result_count