self.addAnalyzer("source_name", simple)
self.addAnalyzer("publisher", simple)
self.addAnalyzer("authors", simple)
+ self.addAnalyzer("title", simple)
+
self.addAnalyzer("is_book", keyword)
# shouldn't the title have two forms? _pl and simple?
class SearchResult(object):
def __init__(self, searcher, scoreDocs, score=None, how_found=None, snippets=None):
- self.snippets = []
-
if score:
self.score = score
else:
self.score = scoreDocs.score
- self.hits = []
+ self._hits = []
+ self.hits = None # processed hits
stored = searcher.doc(scoreDocs.doc)
self.book_id = int(stored.get("book_id"))
fragment = stored.get("fragment_anchor")
- hit = (sec + (header_span,), fragment, scoreDocs.score, {'how_found': how_found, 'snippets': [snippets]})
+ hit = (sec + (header_span,), fragment, scoreDocs.score, {'how_found': how_found, 'snippets': snippets and [snippets] or []})
- self.hits.append(hit)
+ self._hits.append(hit)
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.hits += other.hits
+ self._hits += other._hits
if other.score > self.score:
self.score = other.score
return self
book = property(get_book)
def process_hits(self):
- frags = filter(lambda r: r[1] is not None, self.hits)
- sect = filter(lambda r: r[1] is None, self.hits)
+ POSITION = 0
+ FRAGMENT = 1
+ POSITION_INDEX = 1
+ POSITION_SPAN = 2
+ SCORE = 2
+ OTHER = 3
+
+ # to sections and fragments
+ frags = filter(lambda r: r[FRAGMENT] is not None, self._hits)
+ sect = filter(lambda r: r[FRAGMENT] is None, self._hits)
sect = filter(lambda s: 0 == len(filter(
- lambda f: s[0][1] >= f[0][1] and s[0][1] < f[0][1] + f[0][2],
+ lambda f: s[POSITION][POSITION_INDEX] >= f[POSITION][POSITION_INDEX]
+ and s[POSITION][POSITION_INDEX] < f[POSITION][POSITION_INDEX] + f[POSITION][POSITION_SPAN],
frags)), sect)
hits = []
+ # remove duplicate fragments
+ fragments = {}
+ for f in frags:
+ fid = f[FRAGMENT]
+ if fid in fragments:
+ if fragments[fid][SCORE] >= f[SCORE]:
+ continue
+ fragments[fid] = f
+ frags = fragments.values()
+
+ # remove duplicate sections
+ sections = {}
+
for s in sect:
- m = {'score': s[2],
- 'header_index': s[0][1]
+ si = s[POSITION][POSITION_INDEX]
+ # skip existing
+ if si in sections:
+ if sections[si]['score'] >= s[SCORE]:
+ continue
+
+ m = {'score': s[SCORE],
+ 'header_index': s[POSITION][POSITION_INDEX]
}
- m.update(s[3])
- hits.append(m)
+ m.update(s[OTHER])
+ sections[si] = m
+
+ hits = sections.values()
for f in frags:
- frag = catalogue.models.Fragment.objects.get(anchor=f[1])
- m = {'score': f[2],
+ frag = catalogue.models.Fragment.objects.get(anchor=f[FRAGMENT])
+ m = {'score': f[SCORE],
'fragment': frag,
'themes': frag.tags.filter(category='theme')
}
- m.update(f[3])
+ m.update(f[OTHER])
hits.append(m)
hits.sort(lambda a, b: cmp(a['score'], b['score']), reverse=True)
- print("--- %s" % hits)
+ self.hits = hits
- return hits
+ return self
def __unicode__(self):
return u'SearchResult(book_id=%d, score=%d)' % (self.book_id, self.score)
lst = self.book_tags.get(t.category, [])
lst.append(t)
self.book_tags[t.category] = lst
- if t.category in ['theme']:
+ if t.category in ['theme', 'theme_pl']:
self.part_tags.append(t)
def tag_filter(self, tags, field='tags'):
books.append(SearchResult(self.searcher, found))
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.searcher, found))
+
+ return books
+
def search_perfect_parts(self, searched, max_results=20, fuzzy=False, hint=None):
"""
Search for book parts which containt a phrase perfectly matching (with a slop of 2, default for make_phrase())
# content only query : themes x content
q = BooleanQuery()
- tokens = self.get_tokens(searched)
- if hint is None or hint.just_search_in(['themes_pl']) != []:
- q.add(BooleanClause(self.make_term_query(tokens, field='themes_pl',
+ tokens_pl = self.get_tokens(searched, field='content')
+ tokens = self.get_tokens(searched, field='SIMPLE')
+
+ # only search in themes when we do not already filter by themes
+ if hint is None or hint.just_search_in(['themes']) != []:
+ q.add(BooleanClause(self.make_term_query(tokens_pl, field='themes_pl',
fuzzy=fuzzy), BooleanClause.Occur.MUST))
- q.add(BooleanClause(self.make_term_query(tokens, field='content',
+ q.add(BooleanClause(self.make_term_query(tokens_pl, field='content',
fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
topDocs = self.searcher.search(q, only_in, max_results)
for found in topDocs.scoreDocs:
books.append(SearchResult(self.searcher, found))
+ print "* %s theme x content: %s" % (searched, books[-1]._hits)
# query themes/content x author/title/tags
q = BooleanQuery()
- # in_meta = BooleanQuery()
in_content = BooleanQuery()
+ in_meta = BooleanQuery()
+
+ for fld in ['themes_pl', 'content']:
+ in_content.add(BooleanClause(self.make_term_query(tokens_pl, field=fld, fuzzy=False), BooleanClause.Occur.SHOULD))
+
+ for fld in ['tags', 'authors', 'title']:
+ in_meta.add(BooleanClause(self.make_term_query(tokens, field=fld, fuzzy=False), BooleanClause.Occur.SHOULD))
- for fld in ['themes', 'content', 'tags', 'authors', 'title']:
- in_content.add(BooleanClause(self.make_term_query(tokens, field=fld, fuzzy=False), BooleanClause.Occur.SHOULD))
+ q.add(BooleanClause(in_content, BooleanClause.Occur.MUST))
+ q.add(BooleanClause(in_meta, BooleanClause.Occur.SHOULD))
topDocs = self.searcher.search(q, only_in, max_results)
for found in topDocs.scoreDocs:
books.append(SearchResult(self.searcher, found))
+ print "* %s scatter search: %s" % (searched, books[-1]._hits)
return books
tokenStream = TokenSources.getAnyTokenStream(self.searcher.getIndexReader(), scoreDoc.doc, field, self.analyzer)
# highlighter.getBestTextFragments(tokenStream, text, False, 10)
- # import pdb; pdb.set_trace()
snip = highlighter.getBestFragments(tokenStream, text, 3, "...")
return snip
fnp / wolnelektury.git / blobdiff