# Copyright 2007 Matt Chaput. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY MATT CHAPUT ``AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
# EVENT SHALL MATT CHAPUT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# The views and conclusions contained in the software and documentation are
# those of the authors and should not be interpreted as representing official
# policies, either expressed or implied, of Matt Chaput.
from __future__ import division
import copy
from whoosh import matching
from whoosh.analysis import Token
from whoosh.compat import u
from whoosh.query import qcore, terms, compound
class Sequence(compound.CompoundQuery):
"""Matches documents containing a list of sub-queries in adjacent
positions.
This object has no sanity check to prevent you from using queries in
different fields.
"""
JOINT = " NEAR "
intersect_merge = True
def __init__(self, subqueries, slop=1, ordered=True, boost=1.0):
"""
:param subqueries: a list of :class:`whoosh.query.Query` objects to
match in sequence.
:param slop: the maximum difference in position allowed between the
subqueries.
:param ordered: if True, the position differences between subqueries
must be positive (that is, each subquery in the list must appear
after the previous subquery in the document).
:param boost: a boost factor to add to the score of documents matching
this query.
"""
compound.CompoundQuery.__init__(self, subqueries, boost=boost)
self.slop = slop
self.ordered = ordered
def __eq__(self, other):
return (other and type(self) is type(other)
and self.subqueries == other.subqueries
and self.boost == other.boost)
def __repr__(self):
return "%s(%r, slop=%d, boost=%f)" % (self.__class__.__name__,
self.subqueries, self.slop,
self.boost)
def __hash__(self):
h = hash(self.slop) ^ hash(self.boost)
for q in self.subqueries:
h ^= hash(q)
return h
def normalize(self):
# Because the subqueries are in sequence, we can't do the fancy merging
# that CompoundQuery does
return self.__class__([q.normalize() for q in self.subqueries],
self.slop, self.ordered, self.boost)
def _and_query(self):
return compound.And(self.subqueries)
def estimate_size(self, ixreader):
return self._and_query().estimate_size(ixreader)
def estimate_min_size(self, ixreader):
return self._and_query().estimate_min_size(ixreader)
def _matcher(self, subs, searcher, context):
from whoosh.query.spans import SpanNear
# Tell the sub-queries this matcher will need the current match to get
# spans
context = context.set(needs_current=True)
m = self._tree_matcher(subs, SpanNear.SpanNearMatcher, searcher,
context, None, slop=self.slop,
ordered=self.ordered)
return m
class Ordered(Sequence):
"""Matches documents containing a list of sub-queries in the given order.
"""
JOINT = " BEFORE "
def _matcher(self, subs, searcher, context):
from whoosh.query.spans import SpanBefore
return self._tree_matcher(subs, SpanBefore._Matcher, searcher,
context, None)
class Phrase(qcore.Query):
"""Matches documents containing a given phrase."""
def __init__(self, fieldname, words, slop=1, boost=1.0, char_ranges=None):
"""
:param fieldname: the field to search.
:param words: a list of words (unicode strings) in the phrase.
:param slop: the number of words allowed between each "word" in the
phrase; the default of 1 means the phrase must match exactly.
:param boost: a boost factor that to apply to the raw score of
documents matched by this query.
:param char_ranges: if a Phrase object is created by the query parser,
it will set this attribute to a list of (startchar, endchar) pairs
corresponding to the words in the phrase
"""
self.fieldname = fieldname
self.words = words
self.slop = slop
self.boost = boost
self.char_ranges = char_ranges
def __eq__(self, other):
return (other and self.__class__ is other.__class__
and self.fieldname == other.fieldname
and self.words == other.words
and self.slop == other.slop
and self.boost == other.boost)
def __repr__(self):
return "%s(%r, %r, slop=%s, boost=%f)" % (self.__class__.__name__,
self.fieldname, self.words,
self.slop, self.boost)
def __unicode__(self):
return u('%s:"%s"') % (self.fieldname, u(" ").join(self.words))
__str__ = __unicode__
def __hash__(self):
h = hash(self.fieldname) ^ hash(self.slop) ^ hash(self.boost)
for w in self.words:
h ^= hash(w)
return h
def has_terms(self):
return True
def terms(self, phrases=False):
if phrases and self.field():
for word in self.words:
yield (self.field(), word)
def tokens(self, boost=1.0):
char_ranges = self.char_ranges
startchar = endchar = None
for i, word in enumerate(self.words):
if char_ranges:
startchar, endchar = char_ranges[i]
yield Token(fieldname=self.fieldname, text=word,
boost=boost * self.boost, startchar=startchar,
endchar=endchar, chars=True)
def normalize(self):
if not self.words:
return qcore.NullQuery
if len(self.words) == 1:
t = terms.Term(self.fieldname, self.words[0])
if self.char_ranges:
t.startchar, t.endchar = self.char_ranges[0]
return t
words = [w for w in self.words if w is not None]
return self.__class__(self.fieldname, words, slop=self.slop,
boost=self.boost, char_ranges=self.char_ranges)
def replace(self, fieldname, oldtext, newtext):
q = copy.copy(self)
if q.fieldname == fieldname:
for i, word in enumerate(q.words):
if word == oldtext:
q.words[i] = newtext
return q
def _and_query(self):
return compound.And([terms.Term(self.fieldname, word)
for word in self.words])
def estimate_size(self, ixreader):
return self._and_query().estimate_size(ixreader)
def estimate_min_size(self, ixreader):
return self._and_query().estimate_min_size(ixreader)
def matcher(self, searcher, context=None):
from whoosh.query import Term, SpanNear2
fieldname = self.fieldname
if fieldname not in searcher.schema:
return matching.NullMatcher()
field = searcher.schema[fieldname]
if not field.format or not field.format.supports("positions"):
raise qcore.QueryError("Phrase search: %r field has no positions"
% self.fieldname)
terms = []
# Build a list of Term queries from the words in the phrase
reader = searcher.reader()
for word in self.words:
try:
word = field.to_bytes(word)
except ValueError:
return matching.NullMatcher()
if (fieldname, word) not in reader:
# Shortcut the query if one of the words doesn't exist.
return matching.NullMatcher()
terms.append(Term(fieldname, word))
# Create the equivalent SpanNear2 query from the terms
q = SpanNear2(terms, slop=self.slop, ordered=True, mindist=1)
# Get the matcher
m = q.matcher(searcher, context)
if self.boost != 1.0:
m = matching.WrappingMatcher(m, boost=self.boost)
return m