def create_spelling_suggestion(self, query_string):
spelling_suggestion = None
sp = SpellChecker(self.storage)
cleaned_query = force_unicode(query_string)
if not query_string:
return spelling_suggestion
# Clean the string.
for rev_word in self.RESERVED_WORDS:
cleaned_query = cleaned_query.replace(rev_word, '')
for rev_char in self.RESERVED_CHARACTERS:
cleaned_query = cleaned_query.replace(rev_char, '')
# Break it down.
query_words = cleaned_query.split()
suggested_words = []
for word in query_words:
suggestions = sp.suggest(word, number=1)
if len(suggestions) > 0:
suggested_words.append(suggestions[0])
spelling_suggestion = ' '.join(suggested_words)
return spelling_suggestion
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
