import errno
from librarian import dcparser
from librarian.parser import WLDocument
+from lxml import etree
import catalogue.models
from multiprocessing.pool import ThreadPool
from threading import current_thread
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?
for tag in catalogue.models.Tag.objects.all():
doc = Document()
- doc.add(NumericField("tag_id", Field.Store.YES, True).setIntValue(tag.id))
+ doc.add(NumericField("tag_id", Field.Store.YES, True).setIntValue(int(tag.id)))
doc.add(Field("tag_name", tag.name, Field.Store.NO, Field.Index.ANALYZED))
doc.add(Field("tag_name_pl", tag.name, Field.Store.NO, Field.Index.ANALYZED))
doc.add(Field("tag_category", tag.category, Field.Store.NO, Field.Index.NOT_ANALYZED))
Create a lucene document referring book id.
"""
doc = Document()
- doc.add(NumericField("book_id", Field.Store.YES, True).setIntValue(book.id))
+ doc.add(NumericField("book_id", Field.Store.YES, True).setIntValue(int(book.id)))
if book.parent is not None:
- doc.add(NumericField("parent_id", Field.Store.YES, True).setIntValue(book.parent.id))
+ doc.add(NumericField("parent_id", Field.Store.YES, True).setIntValue(int(book.parent.id)))
return doc
def remove_book(self, book):
if header.tag in self.skip_header_tags:
continue
+ if header.tag is etree.Comment:
+ continue
- content = u' '.join([t for t in header.itertext()])
- content = fix_format(content)
-
- doc = add_part(snippets, header_index=position, header_type=header.tag, content=content)
-
- self.index.addDocument(doc)
+ # section content
+ content = []
for start, end in walker(header):
+ # handle fragments and themes.
if start is not None and start.tag == 'begin':
fid = start.attrib['id'][1:]
fragments[fid] = {'content': [], 'themes': [], 'start_section': position, 'start_header': header.tag}
- fragments[fid]['content'].append(start.tail)
+
elif start is not None and start.tag == 'motyw':
fid = start.attrib['id'][1:]
if start.text is not None:
fragments[fid]['themes'] += map(str.strip, map(give_me_utf8, start.text.split(',')))
- fragments[fid]['content'].append(start.tail)
+
elif start is not None and start.tag == 'end':
fid = start.attrib['id'][1:]
if fid not in fragments:
continue # a broken <end> node, skip it
+ # import pdb; pdb.set_trace()
frag = fragments[fid]
if frag['themes'] == []:
continue # empty themes list.
themes=frag['themes'])
self.index.addDocument(doc)
+
+ # Collect content.
elif start is not None:
for frag in fragments.values():
frag['content'].append(start.text)
+ content.append(start.text)
elif end is not None:
for frag in fragments.values():
frag['content'].append(end.tail)
+ content.append(end.tail)
+
+ # in the end, add a section text.
+ doc = add_part(snippets, header_index=position, header_type=header.tag,
+ content=fix_format(u' '.join(filter(lambda s: s is not None, content))))
+
+ self.index.addDocument(doc)
+
finally:
snippets.close()
class SearchResult(object):
- def __init__(self, searcher, scoreDocs, score=None, how_found=None, snippets=None):
- self.snippets = []
-
+ def __init__(self, searcher, scoreDocs, score=None, how_found=None, snippets=None, searched=None, tokens_cache=None):
+ if tokens_cache is None: tokens_cache = {}
+
if score:
- self.score = score
+ self._score = score
else:
- self.score = scoreDocs.score
+ self._score = scoreDocs.score
+
+ self.boost = 1.0
- 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]})
+ if snippets:
+ snippets = snippets.replace("/\n", "\n")
+ hit = (sec + (header_span,), fragment, scoreDocs.score, {'how_found': how_found, 'snippets': snippets and [snippets] or []})
+
+ self._hits.append(hit)
+
+ self.searcher = searcher
+ self.searched = searched
+ self.tokens_cache = tokens_cache
- self.hits.append(hit)
+ @property
+ def score(self):
+ return self._score * self.boost
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],
+ 'section_number': s[POSITION][POSITION_INDEX] + 1,
}
- 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],
+ try:
+ frag = catalogue.models.Fragment.objects.get(anchor=f[FRAGMENT])
+ except catalogue.models.Fragment.DoesNotExist:
+ # stale index
+ continue
+
+ # Figure out if we were searching for a token matching some word in theme name.
+ themes = frag.tags.filter(category='theme')
+ themes_hit = []
+ if self.searched is not None:
+ tokens = self.searcher.get_tokens(self.searched, 'POLISH', tokens_cache=self.tokens_cache)
+ for theme in themes:
+ name_tokens = self.searcher.get_tokens(theme.name, 'POLISH')
+ for t in tokens:
+ if name_tokens.index(t):
+ if not theme in themes_hit:
+ themes_hit.append(theme)
+ break
+
+ m = {'score': f[SCORE],
'fragment': frag,
- 'themes': frag.tags.filter(category='theme')
+ 'section_number': f[POSITION][POSITION_INDEX] + 1,
+ 'themes': themes,
+ 'themes_hit': themes_hit
}
- 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'):
bks.append(catalogue.models.Book.objects.get(id=doc.get("book_id")))
return (bks, tops.totalHits)
- def get_tokens(self, searched, field='content'):
+ def get_tokens(self, searched, field='content', 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):
while tokens.incrementToken():
cta = tokens.getAttribute(CharTermAttribute.class_)
toks.append(cta.toString())
+
+ if cached is not None:
+ cached[field] = toks
+
return toks
def fuzziness(self, fuzzy):
q.add(BooleanClause(term, modal))
return q
- # def content_query(self, query):
- # return BlockJoinQuery(query, self.parent_filter,
- # BlockJoinQuery.ScoreMode.Total)
+ def search_phrase(self, searched, field, book=True, max_results=20, fuzzy=False,
+ filters=None, tokens_cache=None, boost=None):
+ if filters is None: filters = []
+ if tokens_cache is None: tokens_cache = {}
+
+ tokens = self.get_tokens(searched, field, cached=tokens_cache)
+
+ query = self.make_phrase(tokens, field=field, fuzzy=fuzzy)
+ if book:
+ filters.append(self.term_filter(Term('is_book', 'true')))
+ top = self.searcher.search(query, self.chain_filters(filters), max_results)
+
+ return [SearchResult(self.searcher, found) for found in top.scoreDocs]
+
+ def search_some(self, searched, fields, book=True, max_results=20, fuzzy=False,
+ filters=None, tokens_cache=None, boost=None):
+ if filters is None: filters = []
+ if tokens_cache is None: tokens_cache = {}
+
+ if book:
+ filters.append(self.term_filter(Term('is_book', 'true')))
+
+ query = BooleanQuery()
+
+ for fld in fields:
+ tokens = self.get_tokens(searched, fld, cached=tokens_cache)
+
+ query.add(BooleanClause(self.make_term_query(tokens, field=fld,
+ fuzzy=fuzzy), BooleanClause.Occur.SHOULD))
+
+ top = self.searcher.search(query, self.chain_filters(filters), max_results)
+
+ return [SearchResult(self.searcher, found, searched=searched, tokens_cache=tokens_cache) for found in top.scoreDocs]
def search_perfect_book(self, searched, max_results=20, fuzzy=False, hint=None):
"""
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))
+ books.append(SearchResult(self.searcher, 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.searcher, 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 containt a phrase perfectly matching (with a slop of 2, default for make_phrase())
+ 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 ['content']]
flt]),
max_results)
for found in top.scoreDocs:
- books.append(SearchResult(self.searcher, found, snippets=self.get_snippets(found, q)))
+ books.append(SearchResult(self.searcher, found, snippets=self.get_snippets(found, q), how_found='search_perfect_parts'))
return books
- def search_everywhere(self, searched, max_results=20, fuzzy=False, hint=None):
+ def search_everywhere(self, searched, max_results=20, fuzzy=False, hint=None, tokens_cache=None):
"""
Tries to use search terms to match different fields of book (or its parts).
E.g. one word can be an author survey, another be a part of the title, and the rest
are some words from third chapter.
"""
+ if tokens_cache is None: tokens_cache = {}
books = []
only_in = None
# 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', cached=tokens_cache)
+ tokens = self.get_tokens(searched, field='SIMPLE', cached=tokens_cache)
+
+ # 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))
+ books.append(SearchResult(self.searcher, found, how_found='search_everywhere_themesXcontent'))
+ 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', 'content', 'tags', 'authors', 'title']:
- in_content.add(BooleanClause(self.make_term_query(tokens, field=fld, fuzzy=False), BooleanClause.Occur.SHOULD))
+ 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))
+
+ 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))
+ books.append(SearchResult(self.searcher, found, how_found='search_everywhere'))
+ print "* %s scatter search: %s" % (searched, books[-1]._hits)
return books
# return None
-
def get_snippets(self, scoreDoc, query, field='content'):
"""
Returns a snippet for found scoreDoc.
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
Chains a filter list together
"""
filters = filter(lambda x: x is not None, filters)
- if not filters:
+ if not filters or filters is []:
return None
chf = ChainedFilter(JArray('object')(filters, Filter), op)
return chf
fnp / wolnelektury.git / blobdiff