KeywordAnalyzer, NumericRangeQuery, BooleanQuery, \
BlockJoinQuery, BlockJoinCollector, TermsFilter, \
HashSet, BooleanClause, Term, CharTermAttribute, \
- PhraseQuery, StringReader
+ PhraseQuery, StringReader, TermQuery, BlockJoinQuery, \
+ Sort, Integer
# KeywordAnalyzer
import sys
import os
from librarian.parser import WLDocument
import catalogue.models
from multiprocessing.pool import ThreadPool
+from threading import current_thread
import atexit
def index_book(self, book, overwrite=True):
if overwrite:
self.remove_book(book)
-
+
doc = self.extract_metadata(book)
parts = self.extract_content(book)
'wywiad'
]
- skip_header_tags = ['autor_utworu', 'nazwa_utworu']
+ skip_header_tags = ['autor_utworu', 'nazwa_utworu', 'dzielo_nadrzedne']
def create_book_doc(self, book):
"""
def extract_metadata(self, book):
book_info = dcparser.parse(book.xml_file)
+ print("extract metadata for book %s id=%d, thread%d" % (book.slug, book.id, current_thread().ident))
+
doc = self.create_book_doc(book)
doc.add(Field("slug", book.slug, Field.Store.NO, Field.Index.ANALYZED_NO_NORMS))
doc.add(Field("tags", ','.join([t.name for t in book.tags]), Field.Store.NO, Field.Index.ANALYZED))
doc.add(NumericField("header_index", Field.Store.YES, True).setIntValue(position))
doc.add(Field("header_type", header.tag, Field.Store.YES, Field.Index.NOT_ANALYZED))
content = u' '.join([t for t in header.itertext()])
- doc.add(Field("content", content, Field.Store.NO, Field.Index.ANALYZED))
+ doc.add(Field("content", content, Field.Store.YES, Field.Index.ANALYZED))
header_docs.append(doc)
def walker(node):
Field.Store.YES, Field.Index.NOT_ANALYZED))
doc.add(Field("content",
u' '.join(filter(lambda s: s is not None, frag['content'])),
- Field.Store.NO, Field.Index.ANALYZED))
+ Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("themes",
u' '.join(filter(lambda s: s is not None, frag['themes'])),
Field.Store.NO, Field.Index.ANALYZED))
if ReusableIndex.index is not None:
self.index = ReusableIndex.index
else:
+ print("opening index")
ReusableIndex.pool = ThreadPool(threads)
ReusableIndex.pool_jobs = []
Index.open(self, analyzer)
@staticmethod
def close_reusable():
- import pdb; pdb.set_trace()
if ReusableIndex.index is not None:
+ print("closing index")
for job in ReusableIndex.pool_jobs:
job.wait()
ReusableIndex.pool.close()
class MultiSearch(Search):
"""Class capable of IMDb-like searching"""
def get_tokens(self, queryreader):
- if isinstance(queryreader, str):
+ if isinstance(queryreader, str) or isinstance(queryreader, unicode):
queryreader = StringReader(queryreader)
queryreader.reset()
tokens = self.analyzer.reusableTokenStream('content', queryreader)
toks = []
while tokens.incrementToken():
cta = tokens.getAttribute(CharTermAttribute.class_)
- toks.append(cta)
+ toks.append(cta.toString())
return toks
- def make_phrase(self, tokens, field='content', joined=False):
+ def make_phrase(self, tokens, field='content', 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):
+ def make_term_query(self, tokens, field='content', modal=BooleanClause.Occur.SHOULD):
q = BooleanQuery()
for t in tokens:
term = Term(field, t)
- q.add(BooleanClause(term, modal))
- if joined:
- self.content_query(q)
+ q.add(BooleanClause(TermQuery(term), modal))
return q
def content_query(self, query):
return BlockJoinQuery(query, self.parent_filter,
BlockJoinQuery.ScoreMode.Total)
- def multiseach(self, query, max_results=50):
+ def search_perfect(self, tokens, max_results=20):
+ qrys = [self.make_phrase(tokens, field=fld) for fld in ['author', 'title', 'content']]
+
+ books = []
+ for q in qrys:
+ top = self.searcher.search(q, max_results)
+ for found in top.scoreDocs:
+ book_info = self.searcher.doc(found.doc)
+ books.append((found.score, catalogue.models.Book.objects.get(id=book_info.get("book_id")), []))
+ return books
+
+ def search_everywhere(self, tokens, max_results=20):
+ q = BooleanQuery()
+ in_meta = BooleanQuery()
+ in_content = BooleanQuery()
+
+ for fld in ['themes', 'content']:
+ in_content.add(BooleanClause(self.make_term_query(tokens, field=fld), BooleanClause.Occur.SHOULD))
+
+ for fld in ['author', 'title', 'epochs', 'genres', 'kinds']:
+ in_meta.add(BooleanClause(self.make_term_query(tokens, field=fld), 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))
+
+ collector = BlockJoinCollector(Sort.RELEVANCE, 100, True, True)
+
+ self.searcher.search(q, collector)
+
+ books = []
+
+ top_groups = collector.getTopGroups(in_content_join, Sort.RELEVANCE, 0, max_results, 0, True)
+ if top_groups:
+ for grp in top_groups.groups:
+ doc_id = Integer.cast_(grp.groupValue).intValue()
+ book_data = self.searcher.doc(doc_id)
+ book = catalogue.models.Book.objects.get(id=book_data.get("book_id"))
+ parts = []
+ for part in grp.scoreDocs:
+ part_data = self.searcher.doc(part.doc)
+ header_type = part_data.get("header_type")
+ if header_type:
+ parts.append((part.score, {"header": header_type, "position": int(part_data.get("header_index"))}))
+ fragment = part_data.get("fragment_anchor")
+ if fragment:
+ fragment = book.fragments.get(anchor=fragment)
+ parts.append((part.score, {"fragment": fragment}))
+ books.append((grp.maxScore, book, parts))
+
+ return books
+
+
+ def multisearch(self, query, max_results=50):
"""
Search strategy:
- (phrase) OR -> content
-> tags
-> content
"""
- queryreader = StringReader(query)
- tokens = self.get_tokens(queryreader)
+ # queryreader = StringReader(query)
+ # tokens = self.get_tokens(queryreader)
- top_level = BooleanQuery()
- Should = BooleanClause.Occur.SHOULD
+ # top_level = BooleanQuery()
+ # Should = BooleanClause.Occur.SHOULD
- phrase_level = BooleanQuery()
+ # 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')
+ # 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))
+ # 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 = 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)
+ # 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))
+ # 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)
- tops = self.searcher.search(top_level, max_results)
bks = []
for found in tops.scoreDocs:
doc = self.searcher.doc(found.doc)
- bks.append(catalogue.models.Book.objects.get(id=doc.get("book_id")))
+ 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)
fnp / wolnelektury.git / blobdiff