import custom
import operator
+log = logging.getLogger('search')
class SolrIndex(object):
def __init__(self, mode=None):
Removes all tags from index, then index them again.
Indexed fields include: id, name (with and without polish stems), category
"""
+ log.debug("Indexing tags")
remove_only = kw.get('remove_only', False)
# first, remove tags from index.
if tags:
self.remove_book(book, remove_snippets=False)
book_doc = self.create_book_doc(book)
- meta_fields = self.extract_metadata(book, book_info, dc_only=['source_name', 'authors', 'title'])
+ meta_fields = self.extract_metadata(book, book_info, dc_only=['source_name', 'authors', 'translators', 'title'])
# let's not index it - it's only used for extracting publish date
if 'source_name' in meta_fields:
del meta_fields['source_name']
'authors': meta_fields['authors'],
'published_date': meta_fields['published_date']
}
+
if 'translators' in meta_fields:
book_fields['translators'] = meta_fields['translators']
header_span = header_span is not None and int(header_span) or 1
fragment = doc.get("fragment_anchor", None)
snippets_pos = (doc['snippets_position'], doc['snippets_length'])
- snippets_rev = doc['snippets_revision']
+ snippets_rev = doc.get('snippets_revision', None)
hit = (sec + (header_span,), fragment, self._score, {
'how_found': how_found,
self._hits.append(hit)
def __unicode__(self):
- return u"<SR id=%d %d(%d) hits score=%f %d snippets" % \
+ return u"<SR id=%d %d(%d) hits score=%f %d snippets>" % \
(self.book_id, len(self._hits), self._processed_hits and len(self._processed_hits) or -1, self._score, len(self.snippets))
def __str__(self):
def __init__(self, default_field="text"):
super(Search, self).__init__(mode='r')
- # def get_tokens(self, searched, field='text', cached=None):
- # """returns tokens analyzed by a proper (for a field) analyzer
- # argument can be: StringReader, string/unicode, or tokens. In the last case
- # they will just be returned (so we can reuse tokens, if we don't change the analyzer)
- # """
- # if cached is not None and field in cached:
- # return cached[field]
-
- # if isinstance(searched, str) or isinstance(searched, unicode):
- # searched = StringReader(searched)
- # elif isinstance(searched, list):
- # return searched
-
- # searched.reset()
- # tokens = self.analyzer.reusableTokenStream(field, searched)
- # toks = []
- # while tokens.incrementToken():
- # cta = tokens.getAttribute(CharTermAttribute.class_)
- # toks.append(cta.toString())
-
- # if cached is not None:
- # cached[field] = toks
-
- # return toks
-
- # @staticmethod
- # def fuzziness(fuzzy):
- # """Helper method to sanitize fuzziness"""
- # if not fuzzy:
- # return None
- # if isinstance(fuzzy, float) and fuzzy > 0.0 and fuzzy <= 1.0:
- # return fuzzy
- # else:
- # return 0.5
-
- # def make_phrase(self, tokens, field='text', slop=2, fuzzy=False):
- # """
- # Return a PhraseQuery with a series of tokens.
- # """
- # if fuzzy:
- # phrase = MultiPhraseQuery()
- # for t in tokens:
- # term = Term(field, t)
- # fuzzterm = FuzzyTermEnum(self.searcher.getIndexReader(), term, self.fuzziness(fuzzy))
- # fuzzterms = []
-
- # while True:
- # ft = fuzzterm.term()
- # if ft:
- # fuzzterms.append(ft)
- # if not fuzzterm.next(): break
- # if fuzzterms:
- # phrase.add(JArray('object')(fuzzterms, Term))
- # else:
- # phrase.add(term)
- # else:
- # phrase = PhraseQuery()
- # phrase.setSlop(slop)
- # for t in tokens:
- # term = Term(field, t)
- # phrase.add(term)
- # return phrase
def make_term_query(self, query, field='text', modal=operator.or_):
"""
modal - applies to boolean query
fuzzy - should the query by fuzzy.
"""
+ if query is None: query = ''
q = self.index.Q()
q = reduce(modal, map(lambda s: self.index.Q(**{field: s}),
query.split(r" ")), q)
res = query.execute()
return [SearchResult(found, how_found='search_some', query_terms=query_terms) for found in res]
- # def search_perfect_book(self, searched, max_results=20, fuzzy=False, hint=None):
- # """
- # Search for perfect book matches. Just see if the query matches with some author or title,
- # taking hints into account.
- # """
- # fields_to_search = ['authors', 'title']
- # only_in = None
- # if hint:
- # if not hint.should_search_for_book():
- # return []
- # fields_to_search = hint.just_search_in(fields_to_search)
- # only_in = hint.book_filter()
-
- # qrys = [self.make_phrase(self.get_tokens(searched, field=fld), field=fld, fuzzy=fuzzy) for fld in fields_to_search]
-
- # books = []
- # for q in qrys:
- # top = self.searcher.search(q,
- # self.chain_filters([only_in, self.term_filter(Term('is_book', 'true'))]),
- # max_results)
- # for found in top.scoreDocs:
- # books.append(SearchResult(self, found, how_found="search_perfect_book"))
- # return books
-
- # def search_book(self, searched, max_results=20, fuzzy=False, hint=None):
- # fields_to_search = ['tags', 'authors', 'title']
-
- # only_in = None
- # if hint:
- # if not hint.should_search_for_book():
- # return []
- # fields_to_search = hint.just_search_in(fields_to_search)
- # only_in = hint.book_filter()
-
- # tokens = self.get_tokens(searched, field='SIMPLE')
-
- # q = BooleanQuery()
-
- # for fld in fields_to_search:
- # q.add(BooleanClause(self.make_term_query(tokens, field=fld,
- # fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
-
- # books = []
- # top = self.searcher.search(q,
- # self.chain_filters([only_in, self.term_filter(Term('is_book', 'true'))]),
- # max_results)
- # for found in top.scoreDocs:
- # books.append(SearchResult(self, found, how_found="search_book"))
-
- # return books
-
- # def search_perfect_parts(self, searched, max_results=20, fuzzy=False, hint=None):
- # """
- # Search for book parts which contains a phrase perfectly matching (with a slop of 2, default for make_phrase())
- # some part/fragment of the book.
- # """
- # qrys = [self.make_phrase(self.get_tokens(searched), field=fld, fuzzy=fuzzy) for fld in ['text']]
-
- # flt = None
- # if hint:
- # flt = hint.part_filter()
-
- # books = []
- # for q in qrys:
- # top = self.searcher.search(q,
- # self.chain_filters([self.term_filter(Term('is_book', 'true'), inverse=True),
- # flt]),
- # max_results)
- # for found in top.scoreDocs:
- # books.append(SearchResult(self, found, snippets=self.get_snippets(found, q), how_found='search_perfect_parts'))
-
- # return books
def search_everywhere(self, searched, query_terms=None):
"""
Searches for Book objects using query
"""
bks = []
+ bks_found = set()
+ query = query.query(is_book=True)
res = self.apply_filters(query, filters).field_limit(['book_id'])
for r in res:
try:
- bks.append(catalogue.models.Book.objects.get(id=r['book_id']))
+ bid = r['book_id']
+ if not bid in bks_found:
+ bks.append(catalogue.models.Book.objects.get(id=bid))
+ bks_found.add(bid)
except catalogue.models.Book.DoesNotExist: pass
return bks
- # def make_prefix_phrase(self, toks, field):
- # q = MultiPhraseQuery()
- # for i in range(len(toks)):
- # t = Term(field, toks[i])
- # if i == len(toks) - 1:
- # pterms = Search.enum_to_array(PrefixTermEnum(self.searcher.getIndexReader(), t))
- # if pterms:
- # q.add(pterms)
- # else:
- # q.add(t)
- # else:
- # q.add(t)
- # return q
-
- # @staticmethod
- # def term_filter(term, inverse=False):
- # only_term = TermsFilter()
- # only_term.addTerm(term)
-
- # if inverse:
- # neg = BooleanFilter()
- # neg.add(FilterClause(only_term, BooleanClause.Occur.MUST_NOT))
- # only_term = neg
-
- # return only_term
-
-
@staticmethod
def apply_filters(query, filters):
for f in filters:
query = query.query(f)
return query
-
- # def filtered_categories(self, tags):
- # """
- # Return a list of tag categories, present in tags list.
- # """
- # cats = {}
- # for t in tags:
- # cats[t.category] = True
- # return cats.keys()
-
- # def hint(self):
- # return Hint(self)
fnp / wolnelektury.git / blobdiff