/usr/share/pyshared/xappy/searchconnection.py is in python-xappy 0.5-5.
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#
# Copyright (C) 2007 Lemur Consulting Ltd
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
r"""searchconnection.py: A connection to the search engine for searching.
"""
__docformat__ = "restructuredtext en"
import _checkxapian
import os as _os
import cPickle as _cPickle
import math
import xapian as _xapian
from datastructures import *
from fieldactions import *
import fieldmappings as _fieldmappings
import highlight as _highlight
import errors as _errors
import indexerconnection as _indexerconnection
import re as _re
from replaylog import log as _log
class SearchResult(ProcessedDocument):
"""A result from a search.
As well as being a ProcessedDocument representing the document in the
database, the result has several members which may be used to get
information about how well the document matches the search:
- `rank`: The rank of the document in the search results, starting at 0
(ie, 0 is the "top" result, 1 is the second result, etc).
- `weight`: A floating point number indicating the weight of the result
document. The value is only meaningful relative to other results for a
given search - a different search, or the same search with a different
database, may give an entirely different scale to the weights. This
should not usually be displayed to users, but may be useful if trying to
perform advanced reweighting operations on search results.
- `percent`: A percentage value for the weight of a document. This is
just a rescaled form of the `weight` member. It doesn't represent any
kind of probability value; the only real meaning of the numbers is that,
within a single set of results, a document with a higher percentage
corresponds to a better match. Because the percentage doesn't really
represent a probability, or a confidence value, it is probably unhelpful
to display it to most users, since they tend to place an over emphasis
on its meaning. However, it is included because it may be useful
occasionally.
"""
def __init__(self, msetitem, results):
ProcessedDocument.__init__(self, results._fieldmappings, msetitem.document)
self.rank = msetitem.rank
self.weight = msetitem.weight
self.percent = msetitem.percent
self._results = results
def _get_language(self, field):
"""Get the language that should be used for a given field.
Raises a KeyError if the field is not known.
"""
actions = self._results._conn._field_actions[field]._actions
for action, kwargslist in actions.iteritems():
if action == FieldActions.INDEX_FREETEXT:
for kwargs in kwargslist:
try:
return kwargs['language']
except KeyError:
pass
return 'none'
def summarise(self, field, maxlen=600, hl=('<b>', '</b>'), query=None):
"""Return a summarised version of the field specified.
This will return a summary of the contents of the field stored in the
search result, with words which match the query highlighted.
The maximum length of the summary (in characters) may be set using the
maxlen parameter.
The return value will be a string holding the summary, with
highlighting applied. If there are multiple instances of the field in
the document, the instances will be joined with a newline character.
To turn off highlighting, set hl to None. Each highlight will consist
of the first entry in the `hl` list being placed before the word, and
the second entry in the `hl` list being placed after the word.
Any XML or HTML style markup tags in the field will be stripped before
the summarisation algorithm is applied.
If `query` is supplied, it should contain a Query object, as returned
from SearchConnection.query_parse() or related methods, which will be
used as the basis of the summarisation and highlighting rather than the
query which was used for the search.
Raises KeyError if the field is not known.
"""
highlighter = _highlight.Highlighter(language_code=self._get_language(field))
field = self.data[field]
results = []
text = '\n'.join(field)
if query is None:
query = self._results._query
return highlighter.makeSample(text, query, maxlen, hl)
def highlight(self, field, hl=('<b>', '</b>'), strip_tags=False, query=None):
"""Return a highlighted version of the field specified.
This will return all the contents of the field stored in the search
result, with words which match the query highlighted.
The return value will be a list of strings (corresponding to the list
of strings which is the raw field data).
Each highlight will consist of the first entry in the `hl` list being
placed before the word, and the second entry in the `hl` list being
placed after the word.
If `strip_tags` is True, any XML or HTML style markup tags in the field
will be stripped before highlighting is applied.
If `query` is supplied, it should contain a Query object, as returned
from SearchConnection.query_parse() or related methods, which will be
used as the basis of the summarisation and highlighting rather than the
query which was used for the search.
Raises KeyError if the field is not known.
"""
highlighter = _highlight.Highlighter(language_code=self._get_language(field))
field = self.data[field]
results = []
if query is None:
query = self._results._query
for text in field:
results.append(highlighter.highlight(text, query, hl, strip_tags))
return results
def __repr__(self):
return ('<SearchResult(rank=%d, id=%r, data=%r)>' %
(self.rank, self.id, self.data))
class SearchResultIter(object):
"""An iterator over a set of results from a search.
"""
def __init__(self, results, order):
self._results = results
self._order = order
if self._order is None:
self._iter = iter(results._mset)
else:
self._iter = iter(self._order)
def next(self):
if self._order is None:
msetitem = self._iter.next()
else:
index = self._iter.next()
msetitem = self._results._mset.get_hit(index)
return SearchResult(msetitem, self._results)
def _get_significant_digits(value, lower, upper):
"""Get the significant digits of value which are constrained by the
(inclusive) lower and upper bounds.
If there are no significant digits which are definitely within the
bounds, exactly one significant digit will be returned in the result.
>>> _get_significant_digits(15,15,15)
15
>>> _get_significant_digits(15,15,17)
20
>>> _get_significant_digits(4777,208,6000)
5000
>>> _get_significant_digits(4777,4755,4790)
4800
>>> _get_significant_digits(4707,4695,4710)
4700
>>> _get_significant_digits(4719,4717,4727)
4720
>>> _get_significant_digits(0,0,0)
0
>>> _get_significant_digits(9,9,10)
9
>>> _get_significant_digits(9,9,100)
9
"""
assert(lower <= value)
assert(value <= upper)
diff = upper - lower
# Get the first power of 10 greater than the difference.
# This corresponds to the magnitude of the smallest significant digit.
if diff == 0:
pos_pow_10 = 1
else:
pos_pow_10 = int(10 ** math.ceil(math.log10(diff)))
# Special case for situation where we don't have any significant digits:
# get the magnitude of the most significant digit in value.
if pos_pow_10 > value:
if value == 0:
pos_pow_10 = 1
else:
pos_pow_10 = int(10 ** math.floor(math.log10(value)))
# Return the value, rounded to the nearest multiple of pos_pow_10
return ((value + pos_pow_10 // 2) // pos_pow_10) * pos_pow_10
class SearchResults(object):
"""A set of results of a search.
"""
def __init__(self, conn, enq, query, mset, fieldmappings, tagspy,
tagfields, facetspy, facetfields, facethierarchy,
facetassocs):
self._conn = conn
self._enq = enq
self._query = query
self._mset = mset
self._mset_order = None
self._fieldmappings = fieldmappings
self._tagspy = tagspy
if tagfields is None:
self._tagfields = None
else:
self._tagfields = set(tagfields)
self._facetspy = facetspy
self._facetfields = facetfields
self._facethierarchy = facethierarchy
self._facetassocs = facetassocs
self._numeric_ranges_built = {}
def _cluster(self, num_clusters, maxdocs, fields=None):
"""Cluster results based on similarity.
Note: this method is experimental, and will probably disappear or
change in the future.
The number of clusters is specified by num_clusters: unless there are
too few results, there will be exaclty this number of clusters in the
result.
"""
clusterer = _xapian.ClusterSingleLink()
xapclusters = _xapian.ClusterAssignments()
docsim = _xapian.DocSimCosine()
source = _xapian.MSetDocumentSource(self._mset, maxdocs)
if fields is None:
clusterer.cluster(self._conn._index, xapclusters, docsim, source, num_clusters)
else:
decider = self._make_expand_decider(fields)
clusterer.cluster(self._conn._index, xapclusters, docsim, source, decider, num_clusters)
newid = 0
idmap = {}
clusters = {}
for item in self._mset:
docid = item.docid
clusterid = xapclusters.cluster(docid)
if clusterid not in idmap:
idmap[clusterid] = newid
newid += 1
clusterid = idmap[clusterid]
if clusterid not in clusters:
clusters[clusterid] = []
clusters[clusterid].append(item.rank)
return clusters
def _reorder_by_clusters(self, clusters):
"""Reorder the mset based on some clusters.
"""
if self.startrank != 0:
raise _errors.SearchError("startrank must be zero to reorder by clusters")
reordered = False
tophits = []
nottophits = []
clusterstarts = dict(((c[0], None) for c in clusters.itervalues()))
for i in xrange(self.endrank):
if i in clusterstarts:
tophits.append(i)
else:
nottophits.append(i)
self._mset_order = tophits
self._mset_order.extend(nottophits)
def _make_expand_decider(self, fields):
"""Make an expand decider which accepts only terms in the specified
field.
"""
prefixes = {}
if isinstance(fields, basestring):
fields = [fields]
for field in fields:
try:
actions = self._conn._field_actions[field]._actions
except KeyError:
continue
for action, kwargslist in actions.iteritems():
if action == FieldActions.INDEX_FREETEXT:
prefix = self._conn._field_mappings.get_prefix(field)
prefixes[prefix] = None
prefixes['Z' + prefix] = None
if action in (FieldActions.INDEX_EXACT,
FieldActions.TAG,
FieldActions.FACET,):
prefix = self._conn._field_mappings.get_prefix(field)
prefixes[prefix] = None
prefix_re = _re.compile('|'.join([_re.escape(x) + '[^A-Z]' for x in prefixes.keys()]))
class decider(_xapian.ExpandDecider):
def __call__(self, term):
return prefix_re.match(term) is not None
return decider()
def _reorder_by_similarity(self, count, maxcount, max_similarity,
fields=None):
"""Reorder results based on similarity.
The top `count` documents will be chosen such that they are relatively
dissimilar. `maxcount` documents will be considered for moving around,
and `max_similarity` is a value between 0 and 1 indicating the maximum
similarity to the previous document before a document is moved down the
result set.
Note: this method is experimental, and will probably disappear or
change in the future.
"""
if self.startrank != 0:
raise _errors.SearchError("startrank must be zero to reorder by similiarity")
ds = _xapian.DocSimCosine()
ds.set_termfreqsource(_xapian.DatabaseTermFreqSource(self._conn._index))
if fields is not None:
ds.set_expand_decider(self._make_expand_decider(fields))
tophits = []
nottophits = []
full = False
reordered = False
sim_count = 0
new_order = []
end = min(self.endrank, maxcount)
for i in xrange(end):
if full:
new_order.append(i)
continue
hit = self._mset.get_hit(i)
if len(tophits) == 0:
tophits.append(hit)
continue
# Compare each incoming hit to tophits
maxsim = 0.0
for tophit in tophits[-1:]:
sim_count += 1
sim = ds.similarity(hit.document, tophit.document)
if sim > maxsim:
maxsim = sim
# If it's not similar to an existing hit, add to tophits.
if maxsim < max_similarity:
tophits.append(hit)
else:
nottophits.append(hit)
reordered = True
# If we're full of hits, append to the end.
if len(tophits) >= count:
for hit in tophits:
new_order.append(hit.rank)
for hit in nottophits:
new_order.append(hit.rank)
full = True
if not full:
for hit in tophits:
new_order.append(hit.rank)
for hit in nottophits:
new_order.append(hit.rank)
if end != self.endrank:
new_order.extend(range(end, self.endrank))
assert len(new_order) == self.endrank
if reordered:
self._mset_order = new_order
else:
assert new_order == range(self.endrank)
def __repr__(self):
return ("<SearchResults(startrank=%d, "
"endrank=%d, "
"more_matches=%s, "
"matches_lower_bound=%d, "
"matches_upper_bound=%d, "
"matches_estimated=%d, "
"estimate_is_exact=%s)>" %
(
self.startrank,
self.endrank,
self.more_matches,
self.matches_lower_bound,
self.matches_upper_bound,
self.matches_estimated,
self.estimate_is_exact,
))
def _get_more_matches(self):
# This check relies on us having asked for at least one more result
# than retrieved to be checked.
return (self.matches_lower_bound > self.endrank)
more_matches = property(_get_more_matches, doc=
"""Check whether there are further matches after those in this result set.
""")
def _get_startrank(self):
return self._mset.get_firstitem()
startrank = property(_get_startrank, doc=
"""Get the rank of the first item in the search results.
This corresponds to the "startrank" parameter passed to the search() method.
""")
def _get_endrank(self):
return self._mset.get_firstitem() + len(self._mset)
endrank = property(_get_endrank, doc=
"""Get the rank of the item after the end of the search results.
If there are sufficient results in the index, this corresponds to the
"endrank" parameter passed to the search() method.
""")
def _get_lower_bound(self):
return self._mset.get_matches_lower_bound()
matches_lower_bound = property(_get_lower_bound, doc=
"""Get a lower bound on the total number of matching documents.
""")
def _get_upper_bound(self):
return self._mset.get_matches_upper_bound()
matches_upper_bound = property(_get_upper_bound, doc=
"""Get an upper bound on the total number of matching documents.
""")
def _get_human_readable_estimate(self):
lower = self._mset.get_matches_lower_bound()
upper = self._mset.get_matches_upper_bound()
est = self._mset.get_matches_estimated()
return _get_significant_digits(est, lower, upper)
matches_human_readable_estimate = property(_get_human_readable_estimate,
doc=
"""Get a human readable estimate of the number of matching documents.
This consists of the value returned by the "matches_estimated" property,
rounded to an appropriate number of significant digits (as determined by
the values of the "matches_lower_bound" and "matches_upper_bound"
properties).
""")
def _get_estimated(self):
return self._mset.get_matches_estimated()
matches_estimated = property(_get_estimated, doc=
"""Get an estimate for the total number of matching documents.
""")
def _estimate_is_exact(self):
return self._mset.get_matches_lower_bound() == \
self._mset.get_matches_upper_bound()
estimate_is_exact = property(_estimate_is_exact, doc=
"""Check whether the estimated number of matching documents is exact.
If this returns true, the estimate given by the `matches_estimated`
property is guaranteed to be correct.
If this returns false, it is possible that the actual number of matching
documents is different from the number given by the `matches_estimated`
property.
""")
def get_hit(self, index):
"""Get the hit with a given index.
"""
if self._mset_order is None:
msetitem = self._mset.get_hit(index)
else:
msetitem = self._mset.get_hit(self._mset_order[index])
return SearchResult(msetitem, self)
__getitem__ = get_hit
def __iter__(self):
"""Get an iterator over the hits in the search result.
The iterator returns the results in increasing order of rank.
"""
return SearchResultIter(self, self._mset_order)
def __len__(self):
"""Get the number of hits in the search result.
Note that this is not (usually) the number of matching documents for
the search. If startrank is non-zero, it's not even the rank of the
last document in the search result. It's simply the number of hits
stored in the search result.
It is, however, the number of items returned by the iterator produced
by calling iter() on this SearchResults object.
"""
return len(self._mset)
def get_top_tags(self, field, maxtags):
"""Get the most frequent tags in a given field.
- `field` - the field to get tags for. This must have been specified
in the "gettags" argument of the search() call.
- `maxtags` - the maximum number of tags to return.
Returns a sequence of 2-item tuples, in which the first item in the
tuple is the tag, and the second is the frequency of the tag in the
matches seen (as an integer).
"""
if 'tags' in _checkxapian.missing_features:
raise errors.SearchError("Tags unsupported with this release of xapian")
if self._tagspy is None or field not in self._tagfields:
raise _errors.SearchError("Field %r was not specified for getting tags" % field)
prefix = self._conn._field_mappings.get_prefix(field)
return self._tagspy.get_top_terms(prefix, maxtags)
def get_suggested_facets(self, maxfacets=5, desired_num_of_categories=7,
required_facets=None):
"""Get a suggested set of facets, to present to the user.
This returns a list, in descending order of the usefulness of the
facet, in which each item is a tuple holding:
- fieldname of facet.
- sequence of 2-tuples holding the suggested values or ranges for that
field:
For facets of type 'string', the first item in the 2-tuple will
simply be the string supplied when the facet value was added to its
document. For facets of type 'float', it will be a 2-tuple, holding
floats giving the start and end of the suggested value range.
The second item in the 2-tuple will be the frequency of the facet
value or range in the result set.
If required_facets is not None, it must be a field name, or a sequence
of field names. Any field names mentioned in required_facets will be
returned if there are any facet values at all in the search results for
that field. The facet will only be omitted if there are no facet
values at all for the field.
The value of maxfacets will be respected as far as possible; the
exception is that if there are too many fields listed in
required_facets with at least one value in the search results, extra
facets will be returned (ie, obeying the required_facets parameter is
considered more important than the maxfacets parameter).
If facet_hierarchy was indicated when search() was called, and the
query included facets, then only subfacets of those query facets and
top-level facets will be included in the returned list. Furthermore
top-level facets will only be returned if there are remaining places
in the list after it has been filled with subfacets. Note that
required_facets is still respected regardless of the facet hierarchy.
If a query type was specified when search() was called, and the query
included facets, then facets with an association of Never to the
query type are never returned, even if mentioned in required_facets.
Facets with an association of Preferred are listed before others in
the returned list.
"""
if 'facets' in _checkxapian.missing_features:
raise errors.SearchError("Facets unsupported with this release of xapian")
if self._facetspy is None:
raise _errors.SearchError("Facet selection wasn't enabled when the search was run")
if isinstance(required_facets, basestring):
required_facets = [required_facets]
scores = []
facettypes = {}
for field, slot, kwargslist in self._facetfields:
type = None
for kwargs in kwargslist:
type = kwargs.get('type', None)
if type is not None: break
if type is None: type = 'string'
if type == 'float':
if field not in self._numeric_ranges_built:
self._facetspy.build_numeric_ranges(slot, desired_num_of_categories)
self._numeric_ranges_built[field] = None
facettypes[field] = type
score = self._facetspy.score_categorisation(slot, desired_num_of_categories)
scores.append((score, field, slot))
# Sort on whether facet is top-level ahead of score (use subfacets first),
# and on whether facet is preferred for the query type ahead of anything else
if self._facethierarchy:
# Note, tuple[-2] is the value of 'field' in a scores tuple
scores = [(tuple[-2] not in self._facethierarchy,) + tuple for tuple in scores]
if self._facetassocs:
preferred = _indexerconnection.IndexerConnection.FacetQueryType_Preferred
scores = [(self._facetassocs.get(tuple[-2]) != preferred,) + tuple for tuple in scores]
scores.sort()
if self._facethierarchy:
index = 1
else:
index = 0
if self._facetassocs:
index += 1
if index > 0:
scores = [tuple[index:] for tuple in scores]
results = []
required_results = []
for score, field, slot in scores:
# Check if the facet is required
required = False
if required_facets is not None:
required = field in required_facets
# If we've got enough facets, and the field isn't required, skip it
if not required and len(results) + len(required_results) >= maxfacets:
continue
# Get the values
values = self._facetspy.get_values_as_dict(slot)
if field in self._numeric_ranges_built:
if '' in values:
del values['']
# Required facets must occur at least once, other facets must occur
# at least twice.
if required:
if len(values) < 1:
continue
else:
if len(values) <= 1:
continue
newvalues = []
if facettypes[field] == 'float':
# Convert numbers to python numbers, and number ranges to a
# python tuple of two numbers.
for value, frequency in values.iteritems():
if len(value) <= 9:
value1 = _log(_xapian.sortable_unserialise, value)
value2 = value1
else:
value1 = _log(_xapian.sortable_unserialise, value[:9])
value2 = _log(_xapian.sortable_unserialise, value[9:])
newvalues.append(((value1, value2), frequency))
else:
for value, frequency in values.iteritems():
newvalues.append((value, frequency))
newvalues.sort()
if required:
required_results.append((score, field, newvalues))
else:
results.append((score, field, newvalues))
# Throw away any excess results if we have more required_results to
# insert.
maxfacets = maxfacets - len(required_results)
if maxfacets <= 0:
results = required_results
else:
results = results[:maxfacets]
results.extend(required_results)
results.sort()
# Throw away the scores because they're not meaningful outside this
# algorithm.
results = [(field, newvalues) for (score, field, newvalues) in results]
return results
class SearchConnection(object):
"""A connection to the search engine for searching.
The connection will access a view of the database.
"""
_qp_flags_base = _xapian.QueryParser.FLAG_LOVEHATE
_qp_flags_phrase = _xapian.QueryParser.FLAG_PHRASE
_qp_flags_synonym = (_xapian.QueryParser.FLAG_AUTO_SYNONYMS |
_xapian.QueryParser.FLAG_AUTO_MULTIWORD_SYNONYMS)
_qp_flags_bool = _xapian.QueryParser.FLAG_BOOLEAN
_index = None
def __init__(self, indexpath):
"""Create a new connection to the index for searching.
There may only an arbitrary number of search connections for a
particular database open at a given time (regardless of whether there
is a connection for indexing open as well).
If the database doesn't exist, an exception will be raised.
"""
self._index = _log(_xapian.Database, indexpath)
self._indexpath = indexpath
# Read the actions.
self._load_config()
self._close_handlers = []
def __del__(self):
self.close()
def append_close_handler(self, handler, userdata=None):
"""Append a callback to the list of close handlers.
These will be called when the SearchConnection is closed. This happens
when the close() method is called, or when the SearchConnection object
is deleted. The callback will be passed two arguments: the path to the
SearchConnection object, and the userdata supplied to this method.
The handlers will be called in the order in which they were added.
The handlers will be called after the connection has been closed, so
cannot prevent it closing: their return value will be ignored. In
addition, they should not raise any exceptions.
"""
self._close_handlers.append((handler, userdata))
def _get_sort_type(self, field):
"""Get the sort type that should be used for a given field.
"""
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
for action, kwargslist in actions.iteritems():
if action == FieldActions.SORT_AND_COLLAPSE:
for kwargs in kwargslist:
return kwargs['type']
def _load_config(self):
"""Load the configuration for the database.
"""
# Note: this code is basically duplicated in the IndexerConnection
# class. Move it to a shared location.
assert self._index is not None
config_str = _log(self._index.get_metadata, '_xappy_config')
if len(config_str) == 0:
self._field_actions = {}
self._field_mappings = _fieldmappings.FieldMappings()
self._facet_hierarchy = {}
self._facet_query_table = {}
return
try:
(self._field_actions, mappings, self._facet_hierarchy, self._facet_query_table, self._next_docid) = _cPickle.loads(config_str)
except ValueError:
# Backwards compatibility - configuration used to lack _facet_hierarchy and _facet_query_table
(self._field_actions, mappings, self._next_docid) = _cPickle.loads(config_str)
self._facet_hierarchy = {}
self._facet_query_table = {}
self._field_mappings = _fieldmappings.FieldMappings(mappings)
def reopen(self):
"""Reopen the connection.
This updates the revision of the index which the connection references
to the latest flushed revision.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
self._index.reopen()
# Re-read the actions.
self._load_config()
def close(self):
"""Close the connection to the database.
It is important to call this method before allowing the class to be
garbage collected to ensure that the connection is cleaned up promptly.
No other methods may be called on the connection after this has been
called. (It is permissible to call close() multiple times, but
only the first call will have any effect.)
If an exception occurs, the database will be closed, but changes since
the last call to flush may be lost.
"""
if self._index is None:
return
# Remember the index path
indexpath = self._indexpath
# There is currently no "close()" method for xapian databases, so
# we have to rely on the garbage collector. Since we never copy
# the _index property out of this class, there should be no cycles,
# so the standard python implementation should garbage collect
# _index straight away. A close() method is planned to be added to
# xapian at some point - when it is, we should call it here to make
# the code more robust.
self._index = None
self._indexpath = None
self._field_actions = None
self._field_mappings = None
# Call the close handlers.
for handler, userdata in self._close_handlers:
try:
handler(indexpath, userdata)
except Exception, e:
import sys, traceback
print >>sys.stderr, "WARNING: unhandled exception in handler called by SearchConnection.close(): %s" % traceback.format_exception_only(type(e), e)
def get_doccount(self):
"""Count the number of documents in the database.
This count will include documents which have been added or removed but
not yet flushed().
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
return self._index.get_doccount()
OP_AND = _xapian.Query.OP_AND
OP_OR = _xapian.Query.OP_OR
def query_composite(self, operator, queries):
"""Build a composite query from a list of queries.
The queries are combined with the supplied operator, which is either
SearchConnection.OP_AND or SearchConnection.OP_OR.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
return _log(_xapian.Query, operator, list(queries))
def query_multweight(self, query, multiplier):
"""Build a query which modifies the weights of a subquery.
This produces a query which returns the same documents as the subquery,
and in the same order, but with the weights assigned to each document
multiplied by the value of "multiplier". "multiplier" may be any floating
point value, but negative values will be clipped to 0, since Xapian
doesn't support negative weights.
This can be useful when producing queries to be combined with
query_composite, because it allows the relative importance of parts of
the query to be adjusted.
"""
return _log(_xapian.Query, _xapian.Query.OP_SCALE_WEIGHT, query, multiplier)
def query_filter(self, query, filter, exclude=False):
"""Filter a query with another query.
If exclude is False (or not specified), documents will only match the
resulting query if they match the both the first and second query: the
results of the first query are "filtered" to only include those which
also match the second query.
If exclude is True, documents will only match the resulting query if
they match the first query, but not the second query: the results of
the first query are "filtered" to only include those which do not match
the second query.
Documents will always be weighted according to only the first query.
- `query`: The query to filter.
- `filter`: The filter to apply to the query.
- `exclude`: If True, the sense of the filter is reversed - only
documents which do not match the second query will be returned.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if not isinstance(filter, _xapian.Query):
raise _errors.SearchError("Filter must be a Xapian Query object")
if exclude:
return _log(_xapian.Query, _xapian.Query.OP_AND_NOT, query, filter)
else:
return _log(_xapian.Query, _xapian.Query.OP_FILTER, query, filter)
def query_adjust(self, primary, secondary):
"""Adjust the weights of one query with a secondary query.
Documents will be returned from the resulting query if and only if they
match the primary query (specified by the "primary" parameter).
However, the weights (and hence, the relevance rankings) of the
documents will be adjusted by adding weights from the secondary query
(specified by the "secondary" parameter).
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
return _log(_xapian.Query, _xapian.Query.OP_AND_MAYBE, primary, secondary)
def query_range(self, field, begin, end):
"""Create a query for a range search.
This creates a query which matches only those documents which have a
field value in the specified range.
Begin and end must be appropriate values for the field, according to
the 'type' parameter supplied to the SORTABLE action for the field.
The begin and end values are both inclusive - any documents with a
value equal to begin or end will be returned (unless end is less than
begin, in which case no documents will be returned).
Begin or end may be set to None in order to create an open-ended
range. (They may also both be set to None, which will generate a query
which matches all documents containing any value for the field.)
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if begin is None and end is None:
# Return a "match everything" query
return _log(_xapian.Query, '')
try:
slot = self._field_mappings.get_slot(field, 'collsort')
except KeyError:
# Return a "match nothing" query
return _log(_xapian.Query)
sorttype = self._get_sort_type(field)
marshaller = SortableMarshaller(False)
fn = marshaller.get_marshall_function(field, sorttype)
if begin is not None:
begin = fn(field, begin)
if end is not None:
end = fn(field, end)
if begin is None:
return _log(_xapian.Query, _xapian.Query.OP_VALUE_LE, slot, end)
if end is None:
return _log(_xapian.Query, _xapian.Query.OP_VALUE_GE, slot, begin)
return _log(_xapian.Query, _xapian.Query.OP_VALUE_RANGE, slot, begin, end)
def query_facet(self, field, val):
"""Create a query for a facet value.
This creates a query which matches only those documents which have a
facet value in the specified range.
For a numeric range facet, val should be a tuple holding the start and
end of the range, or a comma separated string holding two floating
point values. For other facets, val should be the value to look
for.
The start and end values are both inclusive - any documents with a
value equal to start or end will be returned (unless end is less than
start, in which case no documents will be returned).
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if 'facets' in _checkxapian.missing_features:
raise errors.SearchError("Facets unsupported with this release of xapian")
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
facettype = None
for action, kwargslist in actions.iteritems():
if action == FieldActions.FACET:
for kwargs in kwargslist:
facettype = kwargs.get('type', None)
if facettype is not None:
break
if facettype is not None:
break
if facettype == 'float':
if isinstance(val, basestring):
val = [float(v) for v in val.split(',', 2)]
assert(len(val) == 2)
try:
slot = self._field_mappings.get_slot(field, 'facet')
except KeyError:
return _log(_xapian.Query)
# FIXME - check that sorttype == self._get_sort_type(field)
sorttype = 'float'
marshaller = SortableMarshaller(False)
fn = marshaller.get_marshall_function(field, sorttype)
begin = fn(field, val[0])
end = fn(field, val[1])
return _log(_xapian.Query, _xapian.Query.OP_VALUE_RANGE, slot, begin, end)
else:
assert(facettype == 'string' or facettype is None)
prefix = self._field_mappings.get_prefix(field)
return _log(_xapian.Query, prefix + val.lower())
def _prepare_queryparser(self, allow, deny, default_op, default_allow,
default_deny):
"""Prepare (and return) a query parser using the specified fields and
operator.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if isinstance(allow, basestring):
allow = (allow, )
if isinstance(deny, basestring):
deny = (deny, )
if allow is not None and len(allow) == 0:
allow = None
if deny is not None and len(deny) == 0:
deny = None
if allow is not None and deny is not None:
raise _errors.SearchError("Cannot specify both `allow` and `deny` "
"(got %r and %r)" % (allow, deny))
if isinstance(default_allow, basestring):
default_allow = (default_allow, )
if isinstance(default_deny, basestring):
default_deny = (default_deny, )
if default_allow is not None and len(default_allow) == 0:
default_allow = None
if default_deny is not None and len(default_deny) == 0:
default_deny = None
if default_allow is not None and default_deny is not None:
raise _errors.SearchError("Cannot specify both `default_allow` and `default_deny` "
"(got %r and %r)" % (default_allow, default_deny))
qp = _log(_xapian.QueryParser)
qp.set_database(self._index)
qp.set_default_op(default_op)
if allow is None:
allow = [key for key in self._field_actions]
if deny is not None:
allow = [key for key in allow if key not in deny]
for field in allow:
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
for action, kwargslist in actions.iteritems():
if action == FieldActions.INDEX_EXACT:
# FIXME - need patched version of xapian to add exact prefixes
#qp.add_exact_prefix(field, self._field_mappings.get_prefix(field))
qp.add_prefix(field, self._field_mappings.get_prefix(field))
if action == FieldActions.INDEX_FREETEXT:
allow_field_specific = True
for kwargs in kwargslist:
allow_field_specific = allow_field_specific or kwargs.get('allow_field_specific', True)
if not allow_field_specific:
continue
qp.add_prefix(field, self._field_mappings.get_prefix(field))
for kwargs in kwargslist:
try:
lang = kwargs['language']
my_stemmer = _log(_xapian.Stem, lang)
qp.my_stemmer = my_stemmer
qp.set_stemmer(my_stemmer)
qp.set_stemming_strategy(qp.STEM_SOME)
except KeyError:
pass
if default_allow is not None or default_deny is not None:
if default_allow is None:
default_allow = [key for key in self._field_actions]
if default_deny is not None:
default_allow = [key for key in default_allow if key not in default_deny]
for field in default_allow:
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
for action, kwargslist in actions.iteritems():
if action == FieldActions.INDEX_FREETEXT:
qp.add_prefix('', self._field_mappings.get_prefix(field))
# FIXME - set stemming options for the default prefix
return qp
def _query_parse_with_prefix(self, qp, string, flags, prefix):
"""Parse a query, with an optional prefix.
"""
if prefix is None:
return qp.parse_query(string, flags)
else:
return qp.parse_query(string, flags, prefix)
def _query_parse_with_fallback(self, qp, string, prefix=None):
"""Parse a query with various flags.
If the initial boolean pass fails, fall back to not using boolean
operators.
"""
try:
q1 = self._query_parse_with_prefix(qp, string,
self._qp_flags_base |
self._qp_flags_phrase |
self._qp_flags_synonym |
self._qp_flags_bool,
prefix)
except _xapian.QueryParserError, e:
# If we got a parse error, retry without boolean operators (since
# these are the usual cause of the parse error).
q1 = self._query_parse_with_prefix(qp, string,
self._qp_flags_base |
self._qp_flags_phrase |
self._qp_flags_synonym,
prefix)
qp.set_stemming_strategy(qp.STEM_NONE)
try:
q2 = self._query_parse_with_prefix(qp, string,
self._qp_flags_base |
self._qp_flags_bool,
prefix)
except _xapian.QueryParserError, e:
# If we got a parse error, retry without boolean operators (since
# these are the usual cause of the parse error).
q2 = self._query_parse_with_prefix(qp, string,
self._qp_flags_base,
prefix)
return _log(_xapian.Query, _xapian.Query.OP_AND_MAYBE, q1, q2)
def query_parse(self, string, allow=None, deny=None, default_op=OP_AND,
default_allow=None, default_deny=None):
"""Parse a query string.
This is intended for parsing queries entered by a user. If you wish to
combine structured queries, it is generally better to use the other
query building methods, such as `query_composite` (though you may wish
to create parts of the query to combine with such methods with this
method).
The string passed to this method can have various operators in it. In
particular, it may contain field specifiers (ie, field names, followed
by a colon, followed by some text to search for in that field). For
example, if "author" is a field in the database, the search string
could contain "author:richard", and this would be interpreted as
"search for richard in the author field". By default, any fields in
the database which are indexed with INDEX_EXACT or INDEX_FREETEXT will
be available for field specific searching in this way - however, this
can be modified using the "allow" or "deny" parameters, and also by the
allow_field_specific tag on INDEX_FREETEXT fields.
Any text which isn't prefixed by a field specifier is used to search
the "default set" of fields. By default, this is the full set of
fields in the database which are indexed with INDEX_FREETEXT and for
which the search_by_default flag set (ie, if the text is found in any
of those fields, the query will match). However, this may be modified
with the "default_allow" and "default_deny" parameters. (Note that
fields which are indexed with INDEX_EXACT aren't allowed to be used in
the default list of fields.)
- `string`: The string to parse.
- `allow`: A list of fields to allow in the query.
- `deny`: A list of fields not to allow in the query.
- `default_op`: The default operator to combine query terms with.
- `default_allow`: A list of fields to search for by default.
- `default_deny`: A list of fields not to search for by default.
Only one of `allow` and `deny` may be specified.
Only one of `default_allow` and `default_deny` may be specified.
If any of the entries in `allow` are not present in the configuration
for the database, or are not specified for indexing (either as
INDEX_EXACT or INDEX_FREETEXT), they will be ignored. If any of the
entries in `deny` are not present in the configuration for the
database, they will be ignored.
Returns a Query object, which may be passed to the search() method, or
combined with other queries.
"""
qp = self._prepare_queryparser(allow, deny, default_op, default_allow,
default_deny)
return self._query_parse_with_fallback(qp, string)
def query_field(self, field, value, default_op=OP_AND):
"""A query for a single field.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
# need to check on field type, and stem / split as appropriate
for action, kwargslist in actions.iteritems():
if action in (FieldActions.INDEX_EXACT,
FieldActions.TAG,
FieldActions.FACET,):
prefix = self._field_mappings.get_prefix(field)
if len(value) > 0:
chval = ord(value[0])
if chval >= ord('A') and chval <= ord('Z'):
prefix = prefix + ':'
return _log(_xapian.Query, prefix + value)
if action == FieldActions.INDEX_FREETEXT:
qp = _log(_xapian.QueryParser)
qp.set_default_op(default_op)
prefix = self._field_mappings.get_prefix(field)
for kwargs in kwargslist:
try:
lang = kwargs['language']
qp.set_stemmer(_log(_xapian.Stem, lang))
qp.set_stemming_strategy(qp.STEM_SOME)
except KeyError:
pass
return self._query_parse_with_fallback(qp, value, prefix)
return _log(_xapian.Query)
def query_similar(self, ids, allow=None, deny=None, simterms=10):
"""Get a query which returns documents which are similar to others.
The list of document IDs to base the similarity search on is given in
`ids`. This should be an iterable, holding a list of strings. If
any of the supplied IDs cannot be found in the database, they will be
ignored. (If no IDs can be found in the database, the resulting query
will not match any documents.)
By default, all fields which have been indexed for freetext searching
will be used for the similarity calculation. The list of fields used
for this can be customised using the `allow` and `deny` parameters
(only one of which may be specified):
- `allow`: A list of fields to base the similarity calculation on.
- `deny`: A list of fields not to base the similarity calculation on.
- `simterms`: Number of terms to use for the similarity calculation.
For convenience, any of `ids`, `allow`, or `deny` may be strings, which
will be treated the same as a list of length 1.
Regardless of the setting of `allow` and `deny`, only fields which have
been indexed for freetext searching will be used for the similarity
measure - all other fields will always be ignored for this purpose.
"""
eterms, prefixes = self._get_eterms(ids, allow, deny, simterms)
# Use the "elite set" operator, which chooses the terms with the
# highest query weight to use.
q = _log(_xapian.Query, _xapian.Query.OP_ELITE_SET, eterms, simterms)
return q
def significant_terms(self, ids, maxterms=10, allow=None, deny=None):
"""Get a set of "significant" terms for a document, or documents.
This has a similar interface to query_similar(): it takes a list of
ids, and an optional specification of a set of fields to consider.
Instead of returning a query, it returns a list of terms from the
document (or documents), which appear "significant". Roughly,
in this situation significant means that the terms occur more
frequently in the specified document than in the rest of the corpus.
The list is in decreasing order of "significance".
By default, all terms related to fields which have been indexed for
freetext searching will be considered for the list of significant
terms. The list of fields used for this can be customised using the
`allow` and `deny` parameters (only one of which may be specified):
- `allow`: A list of fields to consider.
- `deny`: A list of fields not to consider.
For convenience, any of `ids`, `allow`, or `deny` may be strings, which
will be treated the same as a list of length 1.
Regardless of the setting of `allow` and `deny`, only fields which have
been indexed for freetext searching will be considered - all other
fields will always be ignored for this purpose.
The maximum number of terms to return may be specified by the maxterms
parameter.
"""
eterms, prefixes = self._get_eterms(ids, allow, deny, maxterms)
terms = []
for term in eterms:
pos = 0
for char in term:
if not char.isupper():
break
pos += 1
field = prefixes[term[:pos]]
value = term[pos:]
terms.append((field, value))
return terms
def _get_eterms(self, ids, allow, deny, simterms):
"""Get a set of terms for an expand
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if allow is not None and deny is not None:
raise _errors.SearchError("Cannot specify both `allow` and `deny`")
if isinstance(ids, basestring):
ids = (ids, )
if isinstance(allow, basestring):
allow = (allow, )
if isinstance(deny, basestring):
deny = (deny, )
# Set "allow" to contain a list of all the fields to use.
if allow is None:
allow = [key for key in self._field_actions]
if deny is not None:
allow = [key for key in allow if key not in deny]
# Set "prefixes" to contain a list of all the prefixes to use.
prefixes = {}
for field in allow:
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
for action, kwargslist in actions.iteritems():
if action == FieldActions.INDEX_FREETEXT:
prefixes[self._field_mappings.get_prefix(field)] = field
# Repeat the expand until we don't get a DatabaseModifiedError
while True:
try:
eterms = self._perform_expand(ids, prefixes, simterms)
break;
except _xapian.DatabaseModifiedError, e:
self.reopen()
return eterms, prefixes
class ExpandDecider(_xapian.ExpandDecider):
def __init__(self, prefixes):
_xapian.ExpandDecider.__init__(self)
self._prefixes = prefixes
def __call__(self, term):
pos = 0
for char in term:
if not char.isupper():
break
pos += 1
if term[:pos] in self._prefixes:
return True
return False
def _perform_expand(self, ids, prefixes, simterms):
"""Perform an expand operation to get the terms for a similarity
search, given a set of ids (and a set of prefixes to restrict the
similarity operation to).
"""
# Set idquery to be a query which returns the documents listed in
# "ids".
idquery = _log(_xapian.Query, _xapian.Query.OP_OR, ['Q' + id for id in ids])
enq = _log(_xapian.Enquire, self._index)
enq.set_query(idquery)
rset = _log(_xapian.RSet)
for id in ids:
pl = self._index.postlist('Q' + id)
try:
xapid = pl.next()
rset.add_document(xapid.docid)
except StopIteration:
pass
expanddecider = _log(self.ExpandDecider, prefixes)
eset = enq.get_eset(simterms, rset, 0, 1.0, expanddecider)
return [term.term for term in eset]
def query_all(self):
"""A query which matches all the documents in the database.
"""
return _log(_xapian.Query, '')
def query_none(self):
"""A query which matches no documents in the database.
This may be useful as a placeholder in various situations.
"""
return _log(_xapian.Query)
def spell_correct(self, querystr, allow=None, deny=None, default_op=OP_AND,
default_allow=None, default_deny=None):
"""Correct a query spelling.
This returns a version of the query string with any misspelt words
corrected.
- `allow`: A list of fields to allow in the query.
- `deny`: A list of fields not to allow in the query.
- `default_op`: The default operator to combine query terms with.
- `default_allow`: A list of fields to search for by default.
- `default_deny`: A list of fields not to search for by default.
Only one of `allow` and `deny` may be specified.
Only one of `default_allow` and `default_deny` may be specified.
If any of the entries in `allow` are not present in the configuration
for the database, or are not specified for indexing (either as
INDEX_EXACT or INDEX_FREETEXT), they will be ignored. If any of the
entries in `deny` are not present in the configuration for the
database, they will be ignored.
Note that it is possible that the resulting spell-corrected query will
still match no documents - the user should usually check that some
documents are matched by the corrected query before suggesting it to
users.
"""
qp = self._prepare_queryparser(allow, deny, default_op, default_allow,
default_deny)
try:
qp.parse_query(querystr,
self._qp_flags_base |
self._qp_flags_phrase |
self._qp_flags_synonym |
self._qp_flags_bool |
qp.FLAG_SPELLING_CORRECTION)
except _xapian.QueryParserError:
qp.parse_query(querystr,
self._qp_flags_base |
self._qp_flags_phrase |
self._qp_flags_synonym |
qp.FLAG_SPELLING_CORRECTION)
corrected = qp.get_corrected_query_string()
if len(corrected) == 0:
if isinstance(querystr, unicode):
# Encode as UTF-8 for consistency - this happens automatically
# to values passed to Xapian.
return querystr.encode('utf-8')
return querystr
return corrected
def can_collapse_on(self, field):
"""Check if this database supports collapsing on a specified field.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
try:
self._field_mappings.get_slot(field, 'collsort')
except KeyError:
return False
return True
def can_sort_on(self, field):
"""Check if this database supports sorting on a specified field.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
try:
self._field_mappings.get_slot(field, 'collsort')
except KeyError:
return False
return True
def _get_prefix_from_term(self, term):
"""Get the prefix of a term.
Prefixes are any initial capital letters, with the exception that R always
ends a prefix, even if followed by capital letters.
"""
for p in xrange(len(term)):
if term[p].islower():
return term[:p]
elif term[p] == 'R':
return term[:p+1]
return term
def _facet_query_never(self, facet, query_type):
"""Check if a facet must never be returned by a particular query type.
Returns True if the facet must never be returned.
Returns False if the facet may be returned - either becuase there is no
entry for the query type, or because the entry is not
FacetQueryType_Never.
"""
if query_type is None:
return False
if query_type not in self._facet_query_table:
return False
if facet not in self._facet_query_table[query_type]:
return False
return self._facet_query_table[query_type][facet] == _indexerconnection.IndexerConnection.FacetQueryType_Never
def search(self, query, startrank, endrank,
checkatleast=0, sortby=None, collapse=None,
gettags=None,
getfacets=None, allowfacets=None, denyfacets=None, usesubfacets=None,
percentcutoff=None, weightcutoff=None,
query_type=None):
"""Perform a search, for documents matching a query.
- `query` is the query to perform.
- `startrank` is the rank of the start of the range of matching
documents to return (ie, the result with this rank will be returned).
ranks start at 0, which represents the "best" matching document.
- `endrank` is the rank at the end of the range of matching documents
to return. This is exclusive, so the result with this rank will not
be returned.
- `checkatleast` is the minimum number of results to check for: the
estimate of the total number of matches will always be exact if
the number of matches is less than `checkatleast`. A value of ``-1``
can be specified for the checkatleast parameter - this has the
special meaning of "check all matches", and is equivalent to passing
the result of get_doccount().
- `sortby` is the name of a field to sort by. It may be preceded by a
'+' or a '-' to indicate ascending or descending order
(respectively). If the first character is neither '+' or '-', the
sort will be in ascending order.
- `collapse` is the name of a field to collapse the result documents
on. If this is specified, there will be at most one result in the
result set for each value of the field.
- `gettags` is the name of a field to count tag occurrences in, or a
list of fields to do so.
- `getfacets` is a boolean - if True, the matching documents will be
examined to build up a list of the facet values contained in them.
- `allowfacets` is a list of the fieldnames of facets to consider.
- `denyfacets` is a list of fieldnames of facets which will not be
considered.
- `usesubfacets` is a boolean - if True, only top-level facets and
subfacets of facets appearing in the query are considered (taking
precedence over `allowfacets` and `denyfacets`).
- `percentcutoff` is the minimum percentage a result must have to be
returned.
- `weightcutoff` is the minimum weight a result must have to be
returned.
- `query_type` is a value indicating the type of query being
performed. If not None, the value is used to influence which facets
are be returned by the get_suggested_facets() function. If the
value of `getfacets` is False, it has no effect.
If neither 'allowfacets' or 'denyfacets' is specified, all fields
holding facets will be considered (but see 'usesubfacets').
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
if 'facets' in _checkxapian.missing_features:
if getfacets is not None or \
allowfacets is not None or \
denyfacets is not None or \
usesubfacets is not None or \
query_type is not None:
raise errors.SearchError("Facets unsupported with this release of xapian")
if 'tags' in _checkxapian.missing_features:
if gettags is not None:
raise errors.SearchError("Tags unsupported with this release of xapian")
if checkatleast == -1:
checkatleast = self._index.get_doccount()
enq = _log(_xapian.Enquire, self._index)
enq.set_query(query)
if sortby is not None:
asc = True
if sortby[0] == '-':
asc = False
sortby = sortby[1:]
elif sortby[0] == '+':
sortby = sortby[1:]
try:
slotnum = self._field_mappings.get_slot(sortby, 'collsort')
except KeyError:
raise _errors.SearchError("Field %r was not indexed for sorting" % sortby)
# Note: we invert the "asc" parameter, because xapian treats
# "ascending" as meaning "higher values are better"; in other
# words, it considers "ascending" to mean return results in
# descending order.
enq.set_sort_by_value_then_relevance(slotnum, not asc)
if collapse is not None:
try:
slotnum = self._field_mappings.get_slot(collapse, 'collsort')
except KeyError:
raise _errors.SearchError("Field %r was not indexed for collapsing" % collapse)
enq.set_collapse_key(slotnum)
maxitems = max(endrank - startrank, 0)
# Always check for at least one more result, so we can report whether
# there are more matches.
checkatleast = max(checkatleast, endrank + 1)
# Build the matchspy.
matchspies = []
# First, add a matchspy for any gettags fields
if isinstance(gettags, basestring):
if len(gettags) != 0:
gettags = [gettags]
tagspy = None
if gettags is not None and len(gettags) != 0:
tagspy = _log(_xapian.TermCountMatchSpy)
for field in gettags:
try:
prefix = self._field_mappings.get_prefix(field)
tagspy.add_prefix(prefix)
except KeyError:
raise _errors.SearchError("Field %r was not indexed for tagging" % field)
matchspies.append(tagspy)
# add a matchspy for facet selection here.
facetspy = None
facetfields = []
if getfacets:
if allowfacets is not None and denyfacets is not None:
raise _errors.SearchError("Cannot specify both `allowfacets` and `denyfacets`")
if allowfacets is None:
allowfacets = [key for key in self._field_actions]
if denyfacets is not None:
allowfacets = [key for key in allowfacets if key not in denyfacets]
# include None in queryfacets so a top-level facet will
# satisfy self._facet_hierarchy.get(field) in queryfacets
# (i.e. always include top-level facets)
queryfacets = set([None])
if usesubfacets:
# add facets used in the query to queryfacets
termsiter = query.get_terms_begin()
termsend = query.get_terms_end()
while termsiter != termsend:
prefix = self._get_prefix_from_term(termsiter.get_term())
field = self._field_mappings.get_fieldname_from_prefix(prefix)
if field and FieldActions.FACET in self._field_actions[field]._actions:
queryfacets.add(field)
termsiter.next()
for field in allowfacets:
try:
actions = self._field_actions[field]._actions
except KeyError:
actions = {}
for action, kwargslist in actions.iteritems():
if action == FieldActions.FACET:
# filter out non-top-level facets that aren't subfacets
# of a facet in the query
if usesubfacets and self._facet_hierarchy.get(field) not in queryfacets:
continue
# filter out facets that should never be returned for the query type
if self._facet_query_never(field, query_type):
continue
slot = self._field_mappings.get_slot(field, 'facet')
if facetspy is None:
facetspy = _log(_xapian.CategorySelectMatchSpy)
facettype = None
for kwargs in kwargslist:
facettype = kwargs.get('type', None)
if facettype is not None:
break
if facettype is None or facettype == 'string':
facetspy.add_slot(slot, True)
else:
facetspy.add_slot(slot)
facetfields.append((field, slot, kwargslist))
if facetspy is None:
# Set facetspy to False, to distinguish from no facet
# calculation being performed. (This will prevent an
# error being thrown when the list of suggested facets is
# requested - instead, an empty list will be returned.)
facetspy = False
else:
matchspies.append(facetspy)
# Finally, build a single matchspy to pass to get_mset().
if len(matchspies) == 0:
matchspy = None
elif len(matchspies) == 1:
matchspy = matchspies[0]
else:
matchspy = _log(_xapian.MultipleMatchDecider)
for spy in matchspies:
matchspy.append(spy)
enq.set_docid_order(enq.DONT_CARE)
# Set percentage and weight cutoffs
if percentcutoff is not None or weightcutoff is not None:
if percentcutoff is None:
percentcutoff = 0
if weightcutoff is None:
weightcutoff = 0
enq.set_cutoff(percentcutoff, weightcutoff)
# Repeat the search until we don't get a DatabaseModifiedError
while True:
try:
if matchspy is None:
mset = enq.get_mset(startrank, maxitems, checkatleast)
else:
mset = enq.get_mset(startrank, maxitems, checkatleast,
None, None, matchspy)
break
except _xapian.DatabaseModifiedError, e:
self.reopen()
facet_hierarchy = None
if usesubfacets:
facet_hierarchy = self._facet_hierarchy
return SearchResults(self, enq, query, mset, self._field_mappings,
tagspy, gettags, facetspy, facetfields,
facet_hierarchy,
self._facet_query_table.get(query_type))
def iterids(self):
"""Get an iterator which returns all the ids in the database.
The unqiue_ids are currently returned in binary lexicographical sort
order, but this should not be relied on.
Note that the iterator returned by this method may raise a
xapian.DatabaseModifiedError exception if modifications are committed
to the database while the iteration is in progress. If this happens,
the search connection must be reopened (by calling reopen) and the
iteration restarted.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
return _indexerconnection.PrefixedTermIter('Q', self._index.allterms())
def get_document(self, id):
"""Get the document with the specified unique ID.
Raises a KeyError if there is no such document. Otherwise, it returns
a ProcessedDocument.
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
while True:
try:
postlist = self._index.postlist('Q' + id)
try:
plitem = postlist.next()
except StopIteration:
# Unique ID not found
raise KeyError('Unique ID %r not found' % id)
try:
postlist.next()
raise _errors.IndexerError("Multiple documents " #pragma: no cover
"found with same unique ID")
except StopIteration:
# Only one instance of the unique ID found, as it should be.
pass
result = ProcessedDocument(self._field_mappings)
result.id = id
result._doc = self._index.get_document(plitem.docid)
return result
except _xapian.DatabaseModifiedError, e:
self.reopen()
def iter_synonyms(self, prefix=""):
"""Get an iterator over the synonyms.
- `prefix`: if specified, only synonym keys with this prefix will be
returned.
The iterator returns 2-tuples, in which the first item is the key (ie,
a 2-tuple holding the term or terms which will be synonym expanded,
followed by the fieldname specified (or None if no fieldname)), and the
second item is a tuple of strings holding the synonyms for the first
item.
These return values are suitable for the dict() builtin, so you can
write things like:
>>> conn = _indexerconnection.IndexerConnection('foo')
>>> conn.add_synonym('foo', 'bar')
>>> conn.add_synonym('foo bar', 'baz')
>>> conn.add_synonym('foo bar', 'foo baz')
>>> conn.flush()
>>> conn = SearchConnection('foo')
>>> dict(conn.iter_synonyms())
{('foo', None): ('bar',), ('foo bar', None): ('baz', 'foo baz')}
"""
if self._index is None:
raise _errors.SearchError("SearchConnection has been closed")
return _indexerconnection.SynonymIter(self._index, self._field_mappings, prefix)
def get_metadata(self, key):
"""Get an item of metadata stored in the connection.
This returns a value stored by a previous call to
IndexerConnection.set_metadata.
If the value is not found, this will return the empty string.
"""
if self._index is None:
raise _errors.IndexerError("SearchConnection has been closed")
if not hasattr(self._index, 'get_metadata'):
raise _errors.IndexerError("Version of xapian in use does not support metadata")
return _log(self._index.get_metadata, key)
if __name__ == '__main__':
import doctest, sys
doctest.testmod (sys.modules[__name__])
|