def __init__(self, index_dir: str):
ts = TurkishStemmer()
self.__schema = fields.Schema(
message=fields.TEXT(stored=True,
field_boost=1.5,
analyzer=analysis.StemmingAnalyzer()
| analysis.NgramFilter(minsize=2, maxsize=5)),
meta_content=fields.TEXT(
stored=True,
analyzer=analysis.StemmingAnalyzer()
| analysis.NgramFilter(minsize=2, maxsize=5)),
message_id=fields.NUMERIC(stored=True, bits=64),
chat_id=fields.NUMERIC(stored=True, bits=64),
message_tr=fields.TEXT(
stored=False,
field_boost=1.5,
analyzer=analysis.StemmingAnalyzer(stemfn=ts.stem,
stoplist=STOP_WORDS_TR)
| analysis.NgramFilter(minsize=2, maxsize=5)),
meta_content_tr=fields.TEXT(
stored=False,
analyzer=analysis.StemmingAnalyzer(stemfn=ts.stem,
stoplist=STOP_WORDS_TR)
| analysis.NgramFilter(minsize=2, maxsize=5)),
)
if not os.path.isdir(index_dir):
os.mkdir(index_dir)
self.__index = index.create_in(index_dir, self.__schema)
else:
self.__index = index.open_dir(index_dir)
def test_multi_language():
# Analyzer for English
ana_eng = analysis.StemmingAnalyzer()
# analyzer for Pig Latin
def stem_piglatin(w):
if w.endswith("ay"):
w = w[:-2]
return w
ana_pig = analysis.StemmingAnalyzer(stoplist=["nday", "roay"],
stemfn=stem_piglatin)
# Dictionary mapping languages to analyzers
analyzers = {"eng": ana_eng, "pig": ana_pig}
# Fake documents
corpus = [(u("eng"), u("Such stuff as dreams are made on")),
(u("pig"), u("Otay ebay, roay otnay otay ebay"))]
schema = fields.Schema(content=fields.TEXT(stored=True),
lang=fields.ID(stored=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
for doclang, content in corpus:
ana = analyzers[doclang]
# "Pre-analyze" the field into token strings
words = [token.text for token in ana(content)]
# Note we store the original value but index the pre-analyzed words
w.add_document(lang=doclang,
content=words,
_stored_content=content)
with ix.searcher() as s:
schema = s.schema
# Modify the schema to fake the correct analyzer for the language
# we're searching in
schema["content"].analyzer = analyzers["eng"]
qp = qparser.QueryParser("content", schema)
q = qp.parse("dreaming")
r = s.search(q)
assert len(r) == 1
assert r[0]["content"] == "Such stuff as dreams are made on"
schema["content"].analyzer = analyzers["pig"]
qp = qparser.QueryParser("content", schema)
q = qp.parse("otnay")
r = s.search(q)
assert len(r) == 1
assert r[0]["content"] == "Otay ebay, roay otnay otay ebay"
def test_add_reader_spelling():
# Test whether add_spell_word() items get copied over in a merge
# Because b is stemming and spelled, it will use add_spell_word()
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(a=fields.TEXT(spelling=True),
b=fields.TEXT(analyzer=ana, spelling=True))
with TempIndex(schema, "addreadersp") as ix:
with ix.writer() as w:
w.add_document(a=u("rendering modeling compositing enabling"),
b=u("rendering modeling compositing enabling"))
w.add_document(a=u("flying rolling tying quitting polling"),
b=u("flying rolling tying quitting polling"))
with ix.writer() as w:
w.add_document(a=u("writing eyeing ripping timing yelling"),
b=u("writing eyeing ripping timing yelling"))
w.add_document(a=u("undoing indicating opening pressing"),
b=u("undoing indicating opening pressing"))
with ix.searcher() as s:
gr = s.reader().word_graph("a")
assert " ".join(gr.flatten_strings()) == (
"compositing enabling eyeing flying indicating "
"modeling opening polling pressing quitting "
"rendering ripping rolling timing tying undoing "
"writing yelling")
gr = s.reader().word_graph("b")
assert " ".join(gr.flatten_strings()) == (
"compositing enabling eyeing flying indicating "
"modeling opening polling pressing quitting "
"rendering ripping rolling timing tying undoing "
"writing yelling")
def test_analyzing_terms(self):
schema = fields.Schema(text=fields.TEXT(
analyzer=analysis.StemmingAnalyzer()))
qp = qparser.QueryParser("text", schema=schema)
q = qp.parse(u"Indexed!")
self.assertEqual(q.__class__.__name__, "Term")
self.assertEqual(q.text, "index")
def test_analyzing_terms():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(analyzer=ana))
qp = default.QueryParser("text", schema)
q = qp.parse(u("Indexed!"))
assert q.__class__ == query.Term
assert q.text == "index"
def test_add_reader_spelling():
# Test whether add_spell_word() items get copied over in a merge
# Because b is stemming and spelled, it will use add_spell_word()
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(a=fields.TEXT(analyzer=ana),
b=fields.TEXT(analyzer=ana, spelling=True))
with TempIndex(schema, "addreadersp") as ix:
with ix.writer() as w:
w.add_document(a=u"rendering modeling",
b=u"rendering modeling")
w.add_document(a=u"flying rolling",
b=u"flying rolling")
with ix.writer() as w:
w.add_document(a=u"writing eyeing",
b=u"writing eyeing")
w.add_document(a=u"undoing indicating",
b=u"undoing indicating")
w.optimize = True
with ix.reader() as r:
sws = list(r.lexicon("spell_b"))
assert sws == [b"eyeing", b"flying", b"indicating", b"modeling",
b"rendering", b"rolling", b"undoing", b"writing"]
assert list(r.terms_within("a", "undoink", 1)) == []
assert list(r.terms_within("b", "undoink", 1)) == ["undoing"]
def test_spelling_field():
text = u"rendering shading modeling reactions"
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(analyzer=ana, spelling=True))
assert schema["text"].spelling
assert schema["text"].separate_spelling()
with TempIndex(schema) as ix:
with ix.writer() as w:
w.add_document(text=text)
with ix.searcher() as s:
r = s.reader()
fieldobj = schema["text"]
words = [fieldobj.from_bytes(t) for t in r.lexicon("text")]
assert words == ["model", "reaction", "render", "shade"]
words = [fieldobj.from_bytes(t) for t in r.lexicon("spell_text")]
assert words == ["modeling", "reactions", "rendering", "shading"]
# suggest() automatically looks in the spell_text field because
# it calls fieldobj.spelling_fieldname() first
assert s.suggest("text", "renderink") == ["rendering"]
with ix.writer() as w:
w.delete_document(0)
def add_key_terms(ix):
s = ix.searcher()
w = ix.writer()
stemmer = analysis.StemmingAnalyzer()
print("Adding key terms...")
last_book = None
for doc_num in s.document_numbers():
fields = s.stored_fields(doc_num)
if fields['book_name'] != last_book:
last_book = fields['book_name']
print(last_book)
m = re.search(r'session (\d+)', fields['session'], flags=re.IGNORECASE)
is_session_num = lambda k: re.match(r'{0}(st|nd|rd|th)?'.format(m.group(1)), k) if m else False
key_terms = [k for k, v in s.key_terms([doc_num], 'key_terms_content', numterms=10) if not is_session_num(k)]
stemmed = [t.text for t in stemmer(' '.join(key_terms))]
final_terms = []
final_stemmed = set()
for (term, stemmed_term) in zip(key_terms, stemmed):
if stemmed_term not in final_stemmed:
final_terms.append(term)
final_stemmed.add(stemmed_term)
fields['key_terms'] = final_terms
fields['stemmed'] = fields['key_terms_content']
fields['exact'] = fields['key_terms_content']
fields['common'] = fields['key_terms_content']
del fields['key_terms_content']
w.delete_document(doc_num)
w.add_document(**fields)
w.commit()
def test_issue324():
sa = analysis.StemmingAnalyzer()
result = highlight.highlight(u("Indexed!\n1"), [u("index")],
sa,
fragmenter=highlight.ContextFragmenter(),
formatter=highlight.UppercaseFormatter())
assert result == "INDEXED!\n1"
def test_pickle_schema():
from whoosh import analysis
from whoosh.support.charset import accent_map
from whoosh.compat import dumps
freetext_analyzer = (analysis.StemmingAnalyzer()
| analysis.CharsetFilter(accent_map))
schema = fields.Schema(path=fields.ID(stored=True, unique=True),
file_mtime=fields.DATETIME(stored=True),
name=fields.TEXT(stored=False, field_boost=2.0),
description=fields.TEXT(stored=False,
field_boost=1.5,
analyzer=freetext_analyzer),
content=fields.TEXT(analyzer=freetext_analyzer))
# Try to make some sentences that will require stemming
docs = [
u"The rain in spain falls mainly in the plain",
u"Plainly sitting on the plain",
u"Imagine a greatly improved sentence here"
]
with TempIndex(schema) as ix:
with ix.writer() as w:
for doc in docs:
w.add_document(description=doc, content=doc)
assert dumps(schema, 2)
with ix.reader() as r:
assert dumps(r.schema, 2)
def test_correct_spell_field():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(analyzer=ana, spelling=True))
with TempIndex(schema) as ix:
with ix.writer() as w:
w.add_document(text=u"rendering shading modeling reactions")
with ix.searcher() as s:
text = s.schema["text"]
spell_text = s.schema["spell_text"]
r = s.reader()
words = [text.from_bytes(t) for t in r.lexicon("text")]
assert words == ["model", "reaction", "render", "shade"]
words = [spell_text.from_bytes(t) for t in r.lexicon("spell_text")]
assert words == ["modeling", "reactions", "rendering", "shading"]
qp = QueryParser("text", s.schema)
qtext = u"renderink"
q = qp.parse(qtext, s.schema)
r = s.search(q)
assert len(r) == 0
c = s.correct_query(q, qtext)
assert c.string == "rendering"
assert c.query == query.Term("text", "rendering")
hf = highlight.HtmlFormatter(classname="c")
assert c.format_string(
hf) == '<strong class="c term0">rendering</strong>'
def test_memory_codec():
from whoosh.codec import memory
from whoosh.searching import Searcher
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(a=fields.TEXT(vector=True),
b=fields.STORED,
c=fields.NUMERIC(stored=True, sortable=True),
d=fields.TEXT(analyzer=ana, spelling=True))
codec = memory.MemoryCodec()
with codec.writer(schema) as w:
w.add_document(a=u("alfa bravo charlie"),
b="hello",
c=100,
d=u("quelling whining echoing"))
w.add_document(a=u("bravo charlie delta"),
b=1000,
c=200,
d=u("rolling timing yelling"))
w.add_document(a=u("charlie delta echo"),
b=5.5,
c=300,
d=u("using opening pulling"))
w.add_document(a=u("delta echo foxtrot"),
b=True,
c=-100,
d=u("aching selling dipping"))
w.add_document(a=u("echo foxtrot india"),
b=None,
c=-200,
d=u("filling going hopping"))
reader = codec.reader(schema)
s = Searcher(reader)
assert ("a", "delta") in reader
q = query.Term("a", "delta")
r = s.search(q)
assert len(r) == 3
assert [hit["b"] for hit in r] == [1000, 5.5, True]
assert (" ".join(
s.field_terms("a")) == "alfa bravo charlie delta echo foxtrot india")
cfield = schema["c"]
c_sortables = cfield.sortable_terms(reader, "c")
c_values = [cfield.from_bytes(t) for t in c_sortables]
assert c_values, [-200, -100, 100, 200, 300]
assert reader.has_column("c")
c_values = list(reader.column_reader("c"))
assert c_values == [100, 200, 300, -100, -200]
assert s.has_vector(2, "a")
v = s.vector(2, "a")
assert " ".join(v.all_ids()) == "charlie delta echo"
def __init__(self, analyzer=NOT_PROVIDED, **kwargs):
if kwargs.get("facet_class") is None:
kwargs["facet_class"] = FacetCharField
# use StemmingAnalyzer by default
kwargs["analyzer"] = (
analysis.StemmingAnalyzer() if analyzer is NOT_PROVIDED else analyzer
)
super().__init__(**kwargs)
def test_bypass_stemming2():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(content=fields.TEXT(analyzer=ana, spelling=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(
content=u("IPFSTD1 IPFSTD_kdwq134 Kaminski-all Study00:00:00"))
w.add_document(content=u("IPFSTD1 IPFSTD_kdwq134 Kaminski-all Study"))
w.add_document(content=u("This is the first document we've added!"))
def create_whoosh_index(cls):
indexdir = os.path.sep.join(
[config['datadir'], cls.module_dir, 'index'])
if not os.path.exists(indexdir):
os.mkdir(indexdir)
print "Creating a new index"
ana = analysis.StemmingAnalyzer()
schema = Schema(title=TEXT(stored=True),
basefile=ID(stored=True, unique=True),
content=TEXT)
# FIXME: Get a keyword list, correct title, and list of treaty
# references (celex nums as keywords or uris or...)
whoosh_ix = create_in(indexdir, schema)
base_dir = config['datadir']
from time import time
for basefile in cls.get_iterable_for("relate_all", base_dir):
if not ("J" in basefile or "A" in basefile or "K" in basefile):
continue
readstart = time()
# just save the text from the document, strip out the tags
from BeautifulSoup import BeautifulSoup
m = cls.re_celexno.match(basefile)
year = m.group(2)
parsed_file = os.path.sep.join([
base_dir, cls.module_dir, u'parsed', year, basefile + '.xhtml'
])
soup = BeautifulSoup(open(parsed_file).read())
text = ''.join(soup.findAll(text=True))
# Skip the first 150 chars (XML junk) and normalize space
text = ' '.join(text[150:].split())
if text:
indexstart = time()
writer = whoosh_ix.writer()
writer.update_document(title="Case " + basefile,
basefile=basefile,
content=text)
writer.commit()
print "Added %s '%s...' %.1f kb in %.3f + %.3f s" % (
basefile, text[:39], len(text) / 1024,
indexstart - readstart, time() - indexstart)
else:
print "Noadd %s (no text)" % (basefile)
searcher = whoosh_ix.searcher()
results = searcher.find("content",
"quantitative imports equivalent prohibited",
limit=10)
for i in range(len(results)):
print "%s: %s" % (results[i]['title'], results.score(i))
def make_index(basedir,
ixdir,
procs=4,
limitmb=128,
multisegment=True,
glob="*.mrc"):
if not os.path.exists(ixdir):
os.mkdir(ixdir)
# Multi-lingual stop words
stoplist = (analysis.STOP_WORDS
| set("de la der und le die et en al no von di du da "
"del zur ein".split()))
# Schema
ana = analysis.StemmingAnalyzer(stoplist=stoplist)
schema = fields.Schema(
title=fields.TEXT(analyzer=ana),
author=fields.TEXT(phrase=False),
subject=fields.TEXT(analyzer=ana, phrase=False),
file=fields.STORED,
pos=fields.STORED,
)
# MARC fields to extract
mfields = set(subjectfields) # Subjects
mfields.update("100 110 111".split()) # Author
mfields.add("245") # Title
print("Indexing with %d processor(s) and %d MB per processor" %
(procs, limitmb))
c = 0
t = now()
ix = index.create_in(ixdir, schema)
with ix.writer(procs=procs, limitmb=limitmb,
multisegment=multisegment) as w:
filenames = [
filename for filename in os.listdir(basedir)
if fnmatch.fnmatch(filename, glob)
]
for filename in filenames:
path = os.path.join(basedir, filename)
print("Indexing", path)
f = open(path, 'rb')
for x, pos in read_file(f, mfields):
w.add_document(title=uni(title(x)),
author=uni(author(x)),
subject=uni(subjects(x)),
file=filename,
pos=pos)
c += 1
f.close()
print("Committing...")
print("Indexed %d records in %0.02f minutes" % (c, (now() - t) / 60.0))
def whoosh_schema(self):
ana = analysis.StemmingAnalyzer(maxsize=40, cachesize=None)
storebody = self.options.storebody
schema = fields.Schema(body=fields.TEXT(analyzer=ana, stored=storebody),
filepos=fields.STORED,
date=fields.ID(stored=True),
frm=fields.ID(stored=True),
to=fields.IDLIST(stored=True),
subject=fields.TEXT(stored=True),
cc=fields.IDLIST,
bcc=fields.IDLIST)
return schema
def test_spellable_list():
# Make sure a spellable field works with a list of pre-analyzed tokens
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(Location=fields.STORED,Lang=fields.STORED,
Title=fields.TEXT(spelling=True, analyzer=ana))
ix = RamStorage().create_index(schema)
doc = {'Location': '1000/123', 'Lang': 'E',
'Title': ['Introduction', 'Numerical', 'Analysis']}
with ix.writer() as w:
w.add_document(**doc)
def test_bypass_stemming():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(analyzer=ana, spelling=True))
ix = RamStorage().create_index(schema)
w = ix.writer()
w.add_document(text=u("rendering shading modeling reactions"))
w.commit()
with ix.reader() as r:
assert_equal(list(r.lexicon("text")),
["model", "reaction", "render", "shade"])
assert_equal(list(r.word_graph("text").flatten_strings()),
["modeling", "reactions", "rendering", "shading"])
def populate_whoosh(text_dir, whoosh_dir):
loaded = 0
# Create analyzer used for tokenizing and normalizing tokens
# 000, 001, 010, 011,
my_analyzers = [(analysis.RegexTokenizer()),
(analysis.RegexTokenizer() | analysis.LowercaseFilter()),
(analysis.RegexTokenizer() | analysis.LowercaseFilter()
| analysis.StopFilter()), (analysis.StemmingAnalyzer())]
# Create schemas
schemas = []
for my_analyzer in my_analyzers:
schema = Schema(url=ID(stored=True),
body=TEXT(stored=True, analyzer=my_analyzer))
schemas.append(schema)
# Setup index
ixs = []
for i, my_analyzer in enumerate(my_analyzers):
whoosh_dir_current = whoosh_dir + str(i) + '/'
os.makedirs(whoosh_dir_current, exist_ok=True)
ix = index.create_in(whoosh_dir_current, schemas[i])
ixs.append(ix)
# Clear index
writers = []
for i, my_analyzer in enumerate(my_analyzer):
writer = ixs[i].writer()
writer.commit(mergetype=writing.CLEAR)
writer = ixs[i].writer()
writers.append(writer)
# Index documents
for root, dirs, files in os.walk(text_dir, topdown=False):
for name in files:
text_file = os.path.join(root, name)
print('.', end='')
with open(text_file) as tf:
body = tf.read()
url = text_file.replace(text_dir, "")
for writer in writers:
writer.add_document(url=url, body=body)
# print("Added", url)
loaded += 1
for writer in writers:
writer.commit()
print("\n\nLoaded", loaded, "documents")
def get_index(self):
stem_ana = analysis.StemmingAnalyzer()
schema = fields.Schema(
id=fields.ID(unique=True),
datetime=fields.DATETIME(sortable=True),
reply=fields.BOOLEAN,
retweet=fields.BOOLEAN,
text=fields.TEXT(analyzer=stem_ana, stored=True)
)
index_dir = os.path.join(self.dir, "index")
if os.path.exists(index_dir):
self.index = index.open_dir(index_dir)
else:
os.mkdir(index_dir)
self.index = index.create_in(index_dir, schema)
def open_index():
from whoosh import index, fields as f
if os.path.isdir(app.config['WHOOSH_INDEX']):
return index.open_dir(app.config['WHOOSH_INDEX'])
os.mkdir(app.config['WHOOSH_INDEX'])
analyzer = analysis.StemmingAnalyzer() | analysis.CharsetFilter(accent_map)
schema = f.Schema(url=f.ID(stored=True, unique=True),
id=f.ID(stored=True),
title=f.TEXT(stored=True,
field_boost=2.0,
analyzer=analyzer),
type=f.ID(stored=True),
keywords=f.KEYWORD(commas=True),
content=f.TEXT(analyzer=analyzer))
return index.create_in(app.config['WHOOSH_INDEX'], schema)
def test_spelling_field_order():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(a=fields.TEXT,
b=fields.TEXT(analyzer=ana),
c=fields.TEXT,
d=fields.TEXT(analyzer=ana),
e=fields.TEXT(analyzer=ana),
f=fields.TEXT)
ix = RamStorage().create_index(schema)
domain = u("alfa bravo charlie delta").split()
w = ix.writer()
for ls in permutations(domain):
value = " ".join(ls)
w.add_document(a=value, b=value, c=value, d=value, e=value, f=value)
w.commit()
def test_very_long_words():
import sys
length = int(sys.getrecursionlimit() * 1.5)
strings1 = [u(chr(i) * length) for i in range(65, 70)]
strings2 = [u(chr(i) * length) for i in range(71, 75)]
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(analyzer=ana, spelling=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
for string in strings1:
w.add_document(text=string)
with ix.writer() as w:
for string in strings2:
w.add_document(text=string)
w.optimize = True
def test_multivalue():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(id=fields.STORED,
date=fields.DATETIME,
num=fields.NUMERIC,
txt=fields.TEXT(analyzer=ana))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(id=1, date=datetime(2001, 1, 1), num=5)
w.add_document(id=2,
date=[datetime(2002, 2, 2),
datetime(2003, 3, 3)],
num=[1, 2, 3, 12])
w.add_document(txt=u("a b c").split())
with ix.reader() as r:
assert ("num", 3) in r
assert ("date", datetime(2003, 3, 3)) in r
assert " ".join(r.field_terms("txt")) == "a b c"
def test_missing_suggestion():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(content=fields.TEXT(analyzer=ana, spelling=True),
organism=fields.ID)
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(organism=u("hs"), content=u("cells"))
w.add_document(organism=u("hs"), content=u("cell"))
with ix.searcher() as s:
r = s.reader()
assert r.has_word_graph("content")
gr = r.word_graph("content")
assert list(gr.flatten()) == [b("cell"), b("cells")]
c = s.corrector("content")
# Note that corrector won't suggest the word you submit even though it's
# in the index
assert c.suggest("cell") == ["cells"]
def test_snippets():
ana = analysis.StemmingAnalyzer()
schema = fields.Schema(text=fields.TEXT(stored=True, analyzer=ana))
ix = RamStorage().create_index(schema)
w = ix.writer()
w.add_document(text=u(
"Lay out the rough animation by creating the important poses where they occur on the timeline."
))
w.add_document(text=u(
"Set key frames on everything that's key-able. This is for control and predictability: you don't want to accidentally leave something un-keyed. This is also much faster than selecting the parameters to key."
))
w.add_document(text=u(
"Use constant (straight) or sometimes linear transitions between keyframes in the channel editor. This makes the character jump between poses."
))
w.add_document(text=u(
"Keying everything gives quick, immediate results. But it can become difficult to tweak the animation later, especially for complex characters."
))
w.add_document(text=u(
"Copy the current pose to create the next one: pose the character, key everything, then copy the keyframe in the playbar to another frame, and key everything at that frame."
))
w.commit()
target = [
"Set KEY frames on everything that's KEY-able",
"Copy the current pose to create the next one: pose the character, KEY everything, then copy the keyframe in the playbar to another frame, and KEY everything at that frame",
"KEYING everything gives quick, immediate results"
]
with ix.searcher() as s:
qp = qparser.QueryParser("text", ix.schema)
q = qp.parse(u("key"))
r = s.search(q, terms=True)
r.fragmenter = highlight.SentenceFragmenter()
r.formatter = highlight.UppercaseFormatter()
assert sorted([hit.highlights("text", top=1)
for hit in r]) == sorted(target)
def _process_results(self, raw_page, highlight=False, query_string='', spelling_query=None):
from haystack import site
results = []
# It's important to grab the hits first before slicing. Otherwise, this
# can cause pagination failures.
hits = len(raw_page)
facets = {}
spelling_suggestion = None
indexed_models = site.get_indexed_models()
for doc_offset, raw_result in enumerate(raw_page):
score = raw_page.score(doc_offset) or 0
app_label, model_name = raw_result['django_ct'].split('.')
additional_fields = {}
model = get_model(app_label, model_name)
if model and model in indexed_models:
for key, value in raw_result.items():
index = site.get_index(model)
string_key = str(key)
if string_key in index.fields and hasattr(index.fields[string_key], 'convert'):
# Special-cased due to the nature of KEYWORD fields.
if isinstance(index.fields[string_key], MultiValueField):
if value is None or len(value) is 0:
additional_fields[string_key] = []
else:
additional_fields[string_key] = value.split(',')
else:
additional_fields[string_key] = index.fields[string_key].convert(value)
else:
additional_fields[string_key] = self._to_python(value)
del(additional_fields['django_ct'])
del(additional_fields['django_id'])
if highlight:
from whoosh import analysis
from whoosh.highlight import highlight, ContextFragmenter, UppercaseFormatter
sa = analysis.StemmingAnalyzer()
terms = [term.replace('*', '') for term in query_string.split()]
additional_fields['highlighted'] = {
self.content_field_name: [highlight(additional_fields.get(self.content_field_name), terms, sa, ContextFragmenter(terms), UppercaseFormatter())],
}
result = SearchResult(app_label, model_name, raw_result['django_id'], score, **additional_fields)
results.append(result)
else:
hits -= 1
if getattr(settings, 'HAYSTACK_INCLUDE_SPELLING', False):
if spelling_query:
spelling_suggestion = self.create_spelling_suggestion(spelling_query)
else:
spelling_suggestion = self.create_spelling_suggestion(query_string)
return {
'results': results,
'hits': hits,
'facets': facets,
'spelling_suggestion': spelling_suggestion,
}
def test_analyzing_terms():
schema = fields.Schema(text=fields.TEXT(analyzer=analysis.StemmingAnalyzer()))
qp = default.QueryParser("text", schema)
q = qp.parse(u("Indexed!"))
assert_equal(q.__class__, query.Term)
assert_equal(q.text, "index")
def _process_results(self,
raw_page,
highlight=False,
query_string='',
spelling_query=None,
result_class=None):
from haystack import connections
results = []
# It's important to grab the hits first before slicing. Otherwise, this
# can cause pagination failures.
hits = len(raw_page)
if result_class is None:
result_class = SearchResult
facets = {}
spelling_suggestion = None
unified_index = connections[self.connection_alias].get_unified_index()
indexed_models = unified_index.get_indexed_models()
for doc_offset, raw_result in enumerate(raw_page):
score = raw_page.score(doc_offset) or 0
app_label, model_name = raw_result[DJANGO_CT].split('.')
additional_fields = {}
model = get_model(app_label, model_name)
if model and model in indexed_models:
for key, value in raw_result.items():
index = unified_index.get_index(model)
string_key = str(key)
if string_key in index.fields and hasattr(
index.fields[string_key], 'convert'):
# Special-cased due to the nature of KEYWORD fields.
if index.fields[string_key].is_multivalued:
if value is None or len(value) is 0:
additional_fields[string_key] = []
else:
additional_fields[string_key] = value.split(
',')
else:
additional_fields[string_key] = index.fields[
string_key].convert(value)
else:
additional_fields[string_key] = self._to_python(value)
del (additional_fields[DJANGO_CT])
del (additional_fields[DJANGO_ID])
if highlight:
from whoosh import analysis
from whoosh.highlight import highlight, ContextFragmenter, UppercaseFormatter
sa = analysis.StemmingAnalyzer()
terms = [
term.replace('*', '') for term in query_string.split()
]
additional_fields['highlighted'] = {
self.content_field_name: [
highlight(
additional_fields.get(self.content_field_name),
terms, sa, ContextFragmenter(terms),
UppercaseFormatter())
],
}
result = result_class(app_label, model_name,
raw_result[DJANGO_ID], score,
**additional_fields)
results.append(result)
else:
hits -= 1
if self.include_spelling:
if spelling_query:
spelling_suggestion = self.create_spelling_suggestion(
spelling_query)
else:
spelling_suggestion = self.create_spelling_suggestion(
query_string)
return {
'results': results,
'hits': hits,
'facets': facets,
'spelling_suggestion': spelling_suggestion,
}