+# TokenStream tokenStream = analyzer.tokenStream(fieldName, reader);
+# OffsetAttribute offsetAttribute = tokenStream.getAttribute(OffsetAttribute.class);
+# CharTermAttribute charTermAttribute = tokenStream.getAttribute(CharTermAttribute.class);
+
+# while (tokenStream.incrementToken()) {
+# int startOffset = offsetAttribute.startOffset();
+# int endOffset = offsetAttribute.endOffset();
+# String term = charTermAttribute.toString();
+# }
+
+
+class MultiSearch(Search):
+ """Class capable of IMDb-like searching"""
+ def get_tokens(self, queryreader):
+ if isinstance(queryreader, str):
+ queryreader = StringReader(queryreader)
+ queryreader.reset()
+ tokens = self.analyzer.reusableTokenStream('content', queryreader)
+ toks = []
+ while tokens.incrementToken():
+ cta = tokens.getAttribute(CharTermAttribute.class_)
+ toks.append(cta.toString())
+ return toks
+
+ def make_phrase(self, tokens, field='content', joined=False, slop=2):
+ phrase = PhraseQuery()
+ phrase.setSlop(slop)
+ for t in tokens:
+ term = Term(field, t)
+ phrase.add(term)
+ if joined:
+ phrase = self.content_query(phrase)
+ return phrase
+
+ def make_term_query(self, tokens, field='content', modal=BooleanClause.Occur.SHOULD, joined=False):
+ q = BooleanQuery()
+ for t in tokens:
+ term = Term(field, t)
+ q.add(BooleanClause(TermQuery(term), modal))
+ if joined:
+ self.content_query(q)
+ return q
+
+ def content_query(self, query):
+ return BlockJoinQuery(query, self.parent_filter,
+ BlockJoinQuery.ScoreMode.Total)
+
+ def multisearch(self, query, max_results=50):
+ """
+ Search strategy:
+ - (phrase) OR -> content
+ -> title
+ -> author
+ - (keywords) -> author
+ -> motyw
+ -> tags
+ -> content
+ """
+ queryreader = StringReader(query)
+ tokens = self.get_tokens(queryreader)
+
+ top_level = BooleanQuery()
+ Should = BooleanClause.Occur.SHOULD
+
+ phrase_level = BooleanQuery()
+ phrase_level.setBoost(1.3)
+
+ p_content = self.make_phrase(tokens, joined=True)
+ p_title = self.make_phrase(tokens, 'title')
+ p_author = self.make_phrase(tokens, 'author')
+
+ phrase_level.add(BooleanClause(p_content, Should))
+ phrase_level.add(BooleanClause(p_title, Should))
+ phrase_level.add(BooleanClause(p_author, Should))
+
+ kw_level = BooleanQuery()
+
+ kw_level.add(self.make_term_query(tokens, 'author'), Should)
+ kw_level.add(self.make_term_query(tokens, 'themes', joined=True), Should)
+ kw_level.add(self.make_term_query(tokens, 'tags'), Should)
+ kw_level.add(self.make_term_query(tokens, joined=True), Should)
+
+ top_level.add(BooleanClause(phrase_level, Should))
+ top_level.add(BooleanClause(kw_level, Should))
+
+ print self.do_search(phrase_level)
+ print self.do_search(kw_level)
+ print self.do_search(top_level)
+
+ def do_search(self, query, max_results=50):
+ tops = self.searcher.search(query, max_results)
+ #tops = self.searcher.search(p_content, max_results)
+
+ bks = []
+ for found in tops.scoreDocs:
+ doc = self.searcher.doc(found.doc)
+ b = catalogue.models.Book.objects.get(id=doc.get("book_id"))
+ bks.append(b)
+ print "%s (%d) -> %f" % (b, b.id, found.score)
+ return (bks, tops.totalHits)