+
+# 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 SearchResult(object):
+    def __init__(self, searcher, scoreDocs, score=None, highlight_query=None):
+        if score:
+            self.score = score
+        else:
+            self.score = scoreDocs.score
+
+        self.fragments = []
+        self.scores = {}
+        self.sections = []
+
+        stored = searcher.doc(scoreDocs.doc)
+        self.book_id = int(stored.get("book_id"))
+
+        fragment = stored.get("fragment_anchor")
+        if fragment:
+            self.fragments.append(fragment)
+            self.scores[fragment] = scoreDocs.score
+
+        header_type = stored.get("header_type")
+        if header_type:
+            sec = (header_type, int(stored.get("header_index")))
+            self.sections.append(sec)
+            self.scores[sec] = scoreDocs.score
+
+        self.snippets = []
+
+    def add_snippets(self, snippets):
+        self.snippets += snippets
+        return self
+
+    def get_book(self):
+        return catalogue.models.Book.objects.get(id=self.book_id)
+
+    book = property(get_book)
+
+    def get_parts(self):
+        book = self.book
+        parts = [{"header": s[0], "position": s[1], '_score_key': s} for s in self.sections] \
+            + [{"fragment": book.fragments.get(anchor=f), '_score_key':f} for f in self.fragments]
+
+        parts.sort(lambda a, b: cmp(self.scores[a['_score_key']], self.scores[b['_score_key']]))
+        print("bookid: %d parts: %s" % (self.book_id, parts))
+        return parts
+
+    parts = property(get_parts)
+
+    def merge(self, other):
+        if self.book_id != other.book_id:
+            raise ValueError("this search result is or book %d; tried to merge with %d" % (self.book_id, other.book_id))
+        self.fragments += other.fragments
+        self.sections += other.sections
+        self.snippets += other.snippets
+        self.scores.update(other.scores)
+        if other.score > self.score:
+            self.score = other.score
+        return self
+
+    def __unicode__(self):
+        return u'SearchResult(book_id=%d, score=%d)' % (self.book_id, self.score)
+
+    @staticmethod
+    def aggregate(*result_lists):
+        books = {}
+        for rl in result_lists:
+            for r in rl:
+                if r.book_id in books:
+                    books[r.book_id].merge(r)
+                    #print(u"already have one with score %f, and this one has score %f" % (books[book.id][0], found.score))
+                else:
+                    books[r.book_id] = r
+        return books.values()
+
+    def __cmp__(self, other):
+        return cmp(self.score, other.score)
+
+
+class MultiSearch(Search):
+    """Class capable of IMDb-like searching"""
+    def get_tokens(self, searched, field='content'):
+        """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 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())
+        return toks
+
+    def fuzziness(self, fuzzy):
+        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='content', slop=2, fuzzy=False):
+        if fuzzy:
+            phrase = MultiPhraseQuery()
+            for t in tokens:
+                term = Term(field, t)
+                fuzzterm = FuzzyTermEnum(self.searcher.getIndexReader(), term, self.fuzziness(fuzzy))
+                fuzzterms = []
+
+                while True:
+                    #                    print("fuzz %s" % unicode(fuzzterm.term()).encode('utf-8'))
+                    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, tokens, field='content', modal=BooleanClause.Occur.SHOULD, fuzzy=False):
+        q = BooleanQuery()
+        for t in tokens:
+            term = Term(field, t)
+            if fuzzy:
+                term = FuzzyQuery(term, self.fuzziness(fuzzy))
+            else:
+                term = TermQuery(term)
+            q.add(BooleanClause(term, modal))
+        return q
+
+    def content_query(self, query):
+        return BlockJoinQuery(query, self.parent_filter,
+                              BlockJoinQuery.ScoreMode.Total)
+
+    def search_perfect_book(self, searched, max_results=20, fuzzy=False):
+        qrys = [self.make_phrase(self.get_tokens(searched, field=fld), field=fld, fuzzy=fuzzy) for fld in ['author', 'title']]
+
+        books = []
+        for q in qrys:
+            top = self.searcher.search(q, max_results)
+            for found in top.scoreDocs:
+                books.append(SearchResult(self.searcher, found))
+        return books
+
+    def search_perfect_parts(self, searched, max_results=20, fuzzy=False):
+        qrys = [self.make_phrase(self.get_tokens(searched), field=fld, fuzzy=fuzzy) for fld in ['content']]
+
+        books = []
+        for q in qrys:
+            top = self.searcher.search(q, max_results)
+            for found in top.scoreDocs:
+                books.append(SearchResult(self.searcher, found).add_snippets(self.get_snippets(found, q)))
+
+        return books
+
+    def search_everywhere(self, searched, max_results=20, fuzzy=False):
+        books = []
+
+        # content only query : themes x content
+        q = BooleanQuery()
+
+        tokens = self.get_tokens(searched)
+        q.add(BooleanClause(self.make_term_query(tokens, field='themes', fuzzy=fuzzy), BooleanClause.Occur.MUST))
+        q.add(BooleanClause(self.make_term_query(tokens, field='content', fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
+
+        topDocs = self.searcher.search(q, max_results)
+        for found in topDocs.scoreDocs:
+            books.append(SearchResult(self.searcher, found))
+
+        # joined query themes/content x author/title/epochs/genres/kinds
+        q = BooleanQuery()
+        in_meta = BooleanQuery()
+        in_content = BooleanQuery()
+
+        for fld in ['themes', 'content']:
+            in_content.add(BooleanClause(self.make_term_query(tokens, field=fld, fuzzy=False), BooleanClause.Occur.SHOULD))
+
+        in_meta.add(BooleanClause(self.make_term_query(self.get_tokens(searched, field='author'), field='author', fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
+
+        for fld in ['title', 'epochs', 'genres', 'kinds']:
+            in_meta.add(BooleanClause(self.make_term_query(tokens, field=fld, fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
+
+        q.add(BooleanClause(in_meta, BooleanClause.Occur.MUST))
+        in_content_join = self.content_query(in_content)
+        q.add(BooleanClause(in_content_join, BooleanClause.Occur.MUST))
+        #        import pdb; pdb.set_trace()
+        collector = BlockJoinCollector(Sort.RELEVANCE, 100, True, True)
+
+        self.searcher.search(q, collector)
+
+        top_groups = collector.getTopGroups(in_content_join, Sort.RELEVANCE, 0, max_results, 0, True)
+        if top_groups:
+            for grp in top_groups.groups:
+                for part in grp.scoreDocs:
+                    books.append(SearchResult(self.searcher, part, score=grp.maxScore))
+        return books
+
+    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)
+        # j_themes = self.make_term_query(tokens, 'themes', joined=True)
+        # kw_level.add(j_themes, Should)
+        # kw_level.add(self.make_term_query(tokens, 'tags'), Should)
+        # j_con = self.make_term_query(tokens, joined=True)
+        # kw_level.add(j_con, Should)
+
+        # top_level.add(BooleanClause(phrase_level, Should))
+        # top_level.add(BooleanClause(kw_level, Should))
+
+        return None
+
+
+    def do_search(self, query, max_results=50, collector=None):
+        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)
+
+    def get_snippets(self, scoreDoc, query, field='content'):
+        htmlFormatter = SimpleHTMLFormatter()
+        highlighter = Highlighter(htmlFormatter, QueryScorer(query))
+
+        stored = self.searcher.doc(scoreDoc.doc)
+        text = stored.get(field)
+        tokenStream = TokenSources.getAnyTokenStream(self.searcher.getIndexReader(), scoreDoc.doc, field, self.analyzer)
+        #  highlighter.getBestTextFragments(tokenStream, text, False, 10)
+        snip = highlighter.getBestFragments(tokenStream, text, 3, "...")
+        print('snips: %s' % snip)
+
+        return [snip]