def search_all(username, field_name="content", key_word="分布式", **kwargs):
WHOOSH_PATH = '/home/python/Learn/django/Haystack-Whoosh/whoosh_index/%s' % username
if not os.path.exists(WHOOSH_PATH):
return []
# if not id and not keyword and not title and not content:
# return []
index = whoosh_open_idx(WHOOSH_PATH, WHOOSH_SCHEMA)
searcher = index.searcher()
field_name = "content"
key_word = "分布式"
args = {
"limit": None,
}
if "sortedby" in kwargs:
sortedby = kwargs.pop("sortedby")
if "orderby" in kwargs:
orderby = kwargs.pop("orderby")
else:
orderby = "desc"
if orderby == "desc":
facet = sorting.FieldFacet(sortedby, reverse=True)
else:
facet = sorting.FieldFacet(sortedby)
args["sortedby"] = facet
args.update(kwargs)
return searcher.find(field_name, key_word, **args)
def query_search(indexdir, queries, n=10, function='BM25F'):
ix = index.open_dir(indexdir)
search_fields = ['resname', 'categories', 'address', 'city',
'state'] # search fields
og = qparser.OrGroup.factory(0.9)
qp = MultifieldParser(search_fields,
ix.schema,
termclass=query.Variations,
group=og)
qp.add_plugin(DateParserPlugin(free=True))
q = qp.parse(queries)
result_index = []
if function == 'BM25F':
with ix.searcher(weighting=scoring.BM25F(B=0.75, K1=1.2)) as s:
rates = sorting.FieldFacet('rating', reverse=True)
scores = sorting.ScoreFacet()
results = s.search(q, limit=n, sortedby=[scores, rates])
k = min(len(results), n)
for i in range(k):
result_index.append(int(results[i]['ID']))
if function == 'TF_IDF':
with ix.searcher(weighting=scoring.TF_IDF()) as s:
rates = sorting.FieldFacet('rating', reverse=True)
scores = sorting.ScoreFacet()
results = s.search(q, limit=n, sortedby=[scores, rates])
k = min(len(results), n)
for i in range(k):
result_index.append(int(results[i]['ID']))
return result_index
def _paginated_search_mails(self, query, window, page):
page = int(page) if page is not None and int(page) > 1 else 1
window = int(window) if window is not None else 25
with self._index.searcher() as searcher:
tags_facet = sorting.FieldFacet('tag', allow_overlap=True, maptype=sorting.Count)
sorting_facet = sorting.FieldFacet('date', reverse=True)
results = searcher.search_page(query, page, pagelen=window, groupedby=tags_facet, sortedby=sorting_facet)
return unique([mail['ident'] for mail in results]), sum(results.results.groups().values())
class test_translate():
domain = [
("alfa", 100, 50),
("bravo", 20, 80),
("charlie", 10, 10),
("delta", 82, 39),
("echo", 20, 73),
("foxtrot", 81, 59),
("golf", 39, 93),
("hotel", 57, 48),
("india", 84, 75),
]
schema = fields.Schema(name=fields.TEXT(sortable=True),
a=fields.NUMERIC(sortable=True),
b=fields.NUMERIC(sortable=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
for name, a, b in domain:
w.add_document(name=u(name), a=a, b=b)
with ix.searcher() as s:
q = query.Every()
# Baseline: just sort by a field
r = s.search(q, sortedby="a")
assert " ".join([
hit["name"] for hit in r
]) == "charlie bravo echo golf hotel foxtrot delta india alfa"
# Sort by reversed name
target = [x[0] for x in sorted(domain, key=lambda x: x[0][::-1])]
tf = sorting.TranslateFacet(lambda name: name[::-1],
sorting.FieldFacet("name"))
r = s.search(q, sortedby=tf)
assert [hit["name"] for hit in r] == target
# Sort by average of a and b
def avg(a, b):
return (a + b) / 2
target = [
x[0] for x in sorted(domain, key=lambda x: (x[1] + x[2]) / 2)
]
af = sorting.FieldFacet("a")
bf = sorting.FieldFacet("b")
tf = sorting.TranslateFacet(avg, af, bf)
r = s.search(q, sortedby=tf)
assert [hit["name"] for hit in r] == target
def search2(username,
id=None,
keyword=None,
title=None,
content=None,
sortedby="id",
orderby="desc",
page=1,
page_size=10):
# 可以每个用户一个目录
WHOOSH_PATH = '/home/python/Learn/django/Haystack-Whoosh/whoosh_index/%s' % username
if not os.path.exists(WHOOSH_PATH):
return []
# if not id and not keyword and not title and not content:
# return []
index = whoosh_open_idx(WHOOSH_PATH, WHOOSH_SCHEMA)
searcher = index.searcher()
print("-----------2")
parser = QueryParser("content", index.schema)
myquery = parser.parse("分布式")
facet = sorting.FieldFacet("id", reverse=True) # 按序排列搜索结果
results = searcher.search(
myquery, limit=None, sortedby=facet) # limit为搜索结果的限制，默认为10，详见博客开头的官方文档
for result1 in results:
print(dict(result1))
print("-----------2")
def test_nocachefield_segments():
schema = fields.Schema(a=fields.ID(stored=True))
ix = RamStorage().create_index(schema)
w = ix.writer()
w.add_document(a=u("bravo"))
w.add_document(a=u("echo"))
w.add_document(a=u("juliet"))
w.commit()
w = ix.writer()
w.add_document(a=u("kilo"))
w.add_document(a=u("foxtrot"))
w.add_document(a=u("charlie"))
w.commit(merge=False)
w = ix.writer()
w.delete_by_term("a", u("echo"))
w.add_document(a=u("alfa"))
w.add_document(a=u("india"))
w.add_document(a=u("delta"))
w.commit(merge=False)
with ix.searcher() as s:
q = query.TermRange("a", u("bravo"), u("k"))
facet = sorting.FieldFacet("a", reverse=True)
r = s.search(q, sortedby=facet)
assert [hit["a"] for hit in r] == [
"juliet", "india", "foxtrot", "delta", "charlie", "bravo"
]
mq = query.Or(
[query.Term("a", u("bravo")),
query.Term("a", u("delta"))])
anq = query.AndNot(q, mq)
r = s.search(anq, sortedby=facet)
assert [hit["a"]
for hit in r] == ["juliet", "india", "foxtrot", "charlie"]
mq = query.Or(
[query.Term("a", u("bravo")),
query.Term("a", u("delta"))])
r = s.search(q, mask=mq, sortedby=facet)
assert [hit["a"]
for hit in r] == ["juliet", "india", "foxtrot", "charlie"]
fq = query.Or([
query.Term("a", u("alfa")),
query.Term("a", u("charlie")),
query.Term("a", u("echo")),
query.Term("a", u("india")),
])
r = s.search(query.Every(), filter=fq, sortedby=facet)
assert [hit["a"] for hit in r] == ["india", "charlie", "alfa"]
nq = query.Not(
query.Or([query.Term("a", u("alfa")),
query.Term("a", u("india"))]))
r = s.search(query.Every(), filter=nq, sortedby=facet)
assert [hit["a"] for hit in r] == [
"kilo", "juliet", "foxtrot", "delta", "charlie", "bravo"
]
def test(ix):
with ix.searcher() as s:
# Sort by title
r = s.search(query.Every(), sortedby="title")
assert [hit["title"] for hit in r] == sorted_titles
# Sort by reverse title
facet = sorting.FieldFacet("title", reverse=True)
r = s.search(query.Every(), sortedby=facet)
assert [hit["title"] for hit in r] == list(reversed(sorted_titles))
# Sort by num (-10 to 10) first, and within that, by reverse title
facet = sorting.MultiFacet()
facet.add_field("num")
facet.add_field("title", reverse=True)
r = s.search(query.Every(), sortedby=facet)
target = ["Visual and Statistical Thinking",
"Cognitive Style of Powerpoint",
"Beautiful Evidence",
"Visual Explanations",
"Visual Display of Quantitative Information, The",
"Envisioning Information",
]
assert [hit["title"] for hit in r] == target
def searchuser():
q = request.args.get("q", "") # repr
offset = int(request.args.get("offset", 0))
count = int(request.args.get("count", 20))
with uix.searcher() as searcher:
query = QueryParser("nickname", uix.schema).parse(
"nickname:*%s*" %
q) # QueryParser("name", ix.schema).parse("tash*")
#print query
user_id = sorting.FieldFacet("user_id", reverse=True)
results = searcher.search_page(query,
max(offset / count, 0) + 1,
pagelen=count,
sortedby=user_id)
print results.offset, count, offset, max(offset / count, 0) + 1
if results.offset < offset:
return "[]"
tmp = hashlib.md5(str(mktime(datetime.datetime.now().timetuple()))
).hexdigest() + "user_search_tmp"
lua = """local searched = loadstring('return ' .. KEYS[1])()
for i = 1, table.getn(searched) do
redis.call('sadd', KEYS[2], tostring(searched[i]))
end
local arr = redis.call('sort', KEYS[2], 'GET', 'users:*')
return arr"""
res = rs.eval(
lua, 2,
'{' + ','.join(str(hit['user_id']) for hit in results) + '}', tmp)
rs.delete(tmp)
r = ",".join(res)
return "[" + r + "]"
def test_overlapping_lists():
schema = fields.Schema(id=fields.STORED, tags=fields.KEYWORD)
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(id=0, tags=u("alfa bravo charlie"))
w.add_document(id=1, tags=u("bravo charlie delta"))
w.add_document(id=2, tags=u("charlie delta echo"))
w.add_document(id=3, tags=u("delta echo alfa"))
w.add_document(id=4, tags=u("echo alfa bravo"))
with ix.searcher() as s:
of = sorting.FieldFacet("tags", allow_overlap=True)
cat = of.categorizer(s)
assert not cat._use_vectors
r = s.search(query.Every(), groupedby={"tags": of})
assert r.groups("tags") == {'alfa': [0, 3, 4], 'bravo': [0, 1, 4],
'charlie': [0, 1, 2], 'delta': [1, 2, 3],
'echo': [2, 3, 4]}
fcts = sorting.Facets()
fcts.add_field("tags", allow_overlap=True)
r = s.search(query.Every(), groupedby=fcts)
assert r.groups("tags") == {'alfa': [0, 3, 4], 'bravo': [0, 1, 4],
'charlie': [0, 1, 2], 'delta': [1, 2, 3],
'echo': [2, 3, 4]}
def find_unique_orgid(self, q, limit):
facet = sorting.FieldFacet("id", reverse=True)
jobs = self.ix.searcher().search(q,
collapse="orgid",
sortedby=facet,
limit=limit)
return jobs
def test_sorting():
from whoosh import sorting
schema = fields.Schema(id=fields.STORED,
name=fields.ID(stored=True),
size=fields.NUMERIC)
ix = RamIndex(schema)
with ix.writer() as w:
w.add_document(id=0, name=u("bravo"), size=10)
w.add_document(id=1, name=u("alfa"), size=9)
w.add_document(id=2, name=u("delta"), size=8)
w.add_document(id=3, name=u("charlie"), size=7)
with ix.searcher() as s:
q = query.Every()
r = s.search(q, sortedby="name")
assert_equal([hit["id"] for hit in r], [1, 0, 3, 2])
r = s.search(q, sortedby="size")
assert_equal([hit["id"] for hit in r], [3, 2, 1, 0])
facet = sorting.FieldFacet("size", reverse=True)
r = s.search(q, sortedby=facet)
assert_equal([hit["id"] for hit in r], [0, 1, 2, 3])
def search(self, parameter):
# 提取查询字段，创建检索器
keys = parameter['keys']
parser = None
if len(keys) == 1:
parser = QueryParser(keys[0], schema=self.index.schema)
elif len(keys) > 1:
parser = MultifieldParser(keys, schema=self.index.schema)
# 搜索参数（排序、分页）
# score = sorting.ScoreFacet() # 相关度
id = sorting.FieldFacet('id', reverse=False) # 标题字段
_limit = None # 分页限制
if 'page' in parameter and 'pagesize' in parameter:
page = parameter['page']
pagesize = parameter['pagesize']
if page > 0 and pagesize != 0:
_limit = page * pagesize
# 执行搜索
query = parser.parse(parameter['keywords'])
result = self.searcher.search(query, limit=_limit, sortedby=[id])
# 返回结果
res = list()
for hit in result:
res.append({
'title':
hit['title'],
'url':
hit['url'],
'content':
re.sub(r'<[^>]+>', ' | ', hit.highlights('content'), re.S)
})
return res
def test_add_sortable():
st = RamStorage()
schema = fields.Schema(chapter=fields.ID(stored=True), price=fields.NUMERIC)
ix = st.create_index(schema)
with ix.writer() as w:
w.add_document(chapter=u("alfa"), price=100)
w.add_document(chapter=u("bravo"), price=200)
w.add_document(chapter=u("charlie"), price=300)
w.add_document(chapter=u("delta"), price=400)
with ix.writer() as w:
w.add_document(chapter=u("bravo"), price=500)
w.add_document(chapter=u("alfa"), price=600)
w.add_document(chapter=u("delta"), price=100)
w.add_document(chapter=u("charlie"), price=200)
w.merge = False
with ix.reader() as r:
assert not r.has_column("chapter")
assert not r.has_column("price")
with ix.writer() as w:
sorting.add_sortable(w, "chapter", sorting.StoredFieldFacet("chapter"))
sorting.add_sortable(w, "price", sorting.FieldFacet("price"))
w.schema.test = 100
with ix.reader() as r:
assert r.has_column("chapter")
assert r.has_column("price")
chapr = r.column_reader("chapter")
pricer = r.column_reader("price")
assert chapr[0] == "alfa"
assert pricer[0] == 100
def bounced_addresses_filter(searcher, contacts):
query = QueryParser('bounced', searcher.schema).parse('*')
bounced_addresses = searcher.search(
query,
limit=None,
groupedby=sorting.FieldFacet('bounced', allow_overlap=True)).groups()
return set(contacts) - set(flatten([bounced_addresses]))
def search(self, query_string, page="1", limit=20):
results = []
query_string = unicode(query_string, 'utf-8')
with self.index.searcher() as searcher:
query = QueryParser("content",
self.index.schema).parse(query_string)
scores = sorting.ScoreFacet()
sortperson = sorting.FieldFacet("person")
sortcollection = sorting.FieldFacet("collection", reverse=True)
resultset = searcher.search_page(
query,
int(page),
pagelen=int(limit),
sortedby=[sortcollection, scores, sortperson])
# NOTE: Need to copy plain dicts out, since once the searcher
# dies (end of with block), the Hit results lose their reference to
# the data.
for hit in resultset[0:]:
# Grab a copy of the results as a plain dict.
result = hit.fields()
# Also grab the surrounding fragment as a highlight.
# NOTE: This is pretty much the only point we know
# "where" in the matched document the hit occurs.
# The raw content we indexed is stored in 'content',
# so we tell the Hit instance to pull the surrounding
# text fragments from there.
# Also:
# These highlights are pretty much the only reason
# we need to bother stashing the entire document.
# Otherwise, the index can be even smaller.
# Whoosh allows to hunt for the content in the
# original files, if they're available. But as our
# text content isn't large -- keeping it in the
# index seems faster.
result['highlights'] = hit.highlights('content')
results.append(result)
results = {
'matches': results,
'matches_returned': resultset.scored_length(),
'total_matches': len(resultset),
'query': query_string
}
return results
def _search_all_mails(self, query):
with self._index.searcher() as searcher:
sorting_facet = sorting.FieldFacet('date', reverse=True)
results = searcher.search(query,
sortedby=sorting_facet,
reverse=True,
limit=None)
return unique([mail['ident'] for mail in results])
def test_reverse_collapse():
from whoosh import sorting
schema = fields.Schema(title=fields.TEXT(stored=True),
content=fields.TEXT,
path=fields.ID(stored=True),
tags=fields.KEYWORD,
order=fields.NUMERIC(stored=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(title=u"First document",
content=u"This is my document!",
path=u"/a",
tags=u"first",
order=20.0)
w.add_document(title=u"Second document",
content=u"This is the second example.",
path=u"/b",
tags=u"second",
order=12.0)
w.add_document(title=u"Third document",
content=u"Examples are many.",
path=u"/c",
tags=u"third",
order=15.0)
w.add_document(title=u"Thirdish document",
content=u"Examples are too many.",
path=u"/d",
tags=u"third",
order=25.0)
with ix.searcher() as s:
q = query.Every('content')
r = s.search(q)
assert [hit["path"] for hit in r] == ["/a", "/b", "/c", "/d"]
q = query.Or([
query.Term("title", "document"),
query.Term("content", "document"),
query.Term("tags", "document")
])
cf = sorting.FieldFacet("tags")
of = sorting.FieldFacet("order", reverse=True)
r = s.search(q, collapse=cf, collapse_order=of, terms=True)
assert [hit["path"] for hit in r] == ["/a", "/b", "/d"]
def autocomplete(query_str, results=10):
query_str = u' '.join([
t.text for t in _analyzer(query_str)
if not 'university'.startswith(t.text)
])
q = _query_parser.parse(query_str)
return [
_ror_rows[row['ror']] for row in _searcher.search_page(
q,
1,
results,
sortedby=[
sorting.FieldFacet('citation_score', reverse=True),
sorting.FieldFacet('num_students', reverse=True),
sorting.ScoreFacet(),
])
]
def search(term):
with ix.searcher() as searcher:
query = QueryParser("content", ix.schema).parse(term)
date_sort_facet = sorting.FieldFacet("date", reverse=True)
results = searcher.search(query, sortedby=date_sort_facet)
links = []
for x in results:
links.append('<li><a href="' + x['path'] + '.html' + '">' +
x['title'] + '</a></li>\n')
return u"".join(links).encode('utf8')
def search_index(query):
# sizes = sorting.FieldFacet("size")
# prices = sorting.FieldFacet("price", reverse=True)
# results = searcher.search(myquery, sortedby=[sizes, prices])
lec_ids = sorting.FieldFacet("lec_id")
agreeCounts = sorting.FieldFacet("agreeCount", reverse=True)
marks = sorting.FieldFacet("mark", reverse=True)
ix = open_dir("indexdir")
with ix.searcher() as searcher:
# query = MultifieldParser(["url", "title", "tags", "note", "article"], ix.schema).parse("使用")
parser = QueryParser("content", ix.schema)
myquery = parser.parse(query)
# 一开始这里失败了，是由于txt文件的编码形式不是UTF-8，导致了乱码。
# results = searcher.search(myquery,limit=None)
# results = searcher.search_page(myquery, 5)
results = searcher.search(myquery,
limit=None,
sortedby=[lec_ids, agreeCounts, marks])
print(len(results))
print(type(results))
# print(results[:])
# for i in range(len(results)):
# print(results[i]);print('\n') #IndexError: results[10]: Results only has 10 hits
print(results)
count = 0
client = pymongo.MongoClient("mongodb://address")
db_opt = client["course_info"]
b = list()
for i in results:
count += 1
print(i)
j = dict(i)
print(j)
db_opt.result.insert_one({
'lec_id': j['lec_id'],
'agreeCount': j['agreeCount'],
'mark': j['mark'],
'content': j['content']
})
b.append(j['lec_id'])
seta = set(b)
print(count)
return seta
def _search_tag_groups(self, is_filtering_tags):
seen = None
query_parser = QueryParser('tag', self._index.schema)
options = {'limit': None, 'groupedby': sorting.FieldFacet('tag', allow_overlap=True), 'maptype': sorting.Count}
with self._index.searcher() as searcher:
total = searcher.search(query_parser.parse('*'), **options).groups()
if not is_filtering_tags:
seen = searcher.search(query_parser.parse("* AND flags:%s" % Status.SEEN), **options).groups()
return seen, total
def contacts(self, query):
restrict_q = Term("tag", "drafts") | Term("tag", "trash")
if query:
to = QueryParser('to', self._index.schema)
cc = QueryParser('cc', self._index.schema)
bcc = QueryParser('bcc', self._index.schema)
sender = QueryParser('sender', self._index.schema)
with self._index.searcher() as searcher:
to = searcher.search(to.parse("*%s*" % query), limit=None, mask=restrict_q,
groupedby=sorting.FieldFacet('to', allow_overlap=True)).groups()
cc = searcher.search(cc.parse("*%s*" % query), limit=None, mask=restrict_q,
groupedby=sorting.FieldFacet('cc', allow_overlap=True)).groups()
bcc = searcher.search(bcc.parse("*%s*" % query), limit=None, mask=restrict_q,
groupedby=sorting.FieldFacet('bcc', allow_overlap=True)).groups()
sender = searcher.search(sender.parse("*%s*" % query), limit=None, mask=restrict_q,
groupedby=sorting.FieldFacet('sender', allow_overlap=True)).groups()
return flatten([to, cc, bcc, sender])
return []
def test_compound_sort():
fspec = fields.KEYWORD(stored=True, sortable=True)
schema = fields.Schema(a=fspec, b=fspec, c=fspec)
ix = RamStorage().create_index(schema)
alist = u("alfa bravo alfa bravo alfa bravo alfa bravo alfa bravo").split()
blist = u("alfa bravo charlie alfa bravo charlie alfa bravo charlie alfa"
).split()
clist = u("alfa bravo charlie delta echo foxtrot golf hotel india juliet"
).split()
assert all(len(ls) == 10 for ls in (alist, blist, clist))
with ix.writer() as w:
for i in xrange(10):
w.add_document(a=alist[i], b=blist[i], c=clist[i])
with ix.searcher() as s:
q = query.Every()
sortedby = [
sorting.FieldFacet("a"),
sorting.FieldFacet("b", reverse=True),
sorting.FieldFacet("c")
]
r = s.search(q, sortedby=sortedby)
output = []
for hit in r:
output.append(" ".join((hit["a"], hit["b"], hit["c"])))
assert output == [
"alfa charlie charlie",
"alfa charlie india",
"alfa bravo echo",
"alfa alfa alfa",
"alfa alfa golf",
"bravo charlie foxtrot",
"bravo bravo bravo",
"bravo bravo hotel",
"bravo alfa delta",
"bravo alfa juliet",
]
def search_addresses(searcher, query):
restrict_q = Term("tag", "drafts") | Term("tag", "trash")
results = []
for field in ['to', 'cc', 'bcc', 'sender']:
query_parser = QueryParser(field, searcher.schema)
results.append(
searcher.search(query_parser.parse("*%s*" % query),
limit=None,
mask=restrict_q,
groupedby=sorting.FieldFacet(
field, allow_overlap=True)).groups())
return flatten(results)
def search(q, filters, query_string, max_facets=5):
""" Search for a query term and a set o filters
Returns a list of hits and the representation of the facets
"""
ix = get_or_create_index()
hits = []
facets = [
sorting.FieldFacet("tags", allow_overlap=True, maptype=sorting.Count)
]
tags = Tag.objects.values(
'slug',
'title',
)
parser = qparser.QueryParser("text", schema=ix.schema) # , group=og)
try:
q = parser.parse("*" + q + "*")
except:
q = None
if q or filters:
searcher = ix.searcher()
for filter_value in filters:
filter_name = "tags"
q = q & query.Term(filter_name, filter_value)
hits = searcher.search(q.normalize(), groupedby=facets)
active_facets = []
sorted_facets = sorted(hits.groups("tags").items(),
key=operator.itemgetter(1, 0),
reverse=True)
facets = []
for facet_slug, facet_value in sorted_facets:
if not facet_slug:
continue
qs = query_string.copy()
qs["page"] = "1"
if facet_slug in filters:
qs.setlist('f', [f for f in filters if f != facet_slug])
state = "active"
else:
qs.appendlist('f', facet_slug)
state = "available"
obj = tags.get(slug=facet_slug)
facet_dict = {
'slug': facet_slug,
'title': obj.get("title", ""),
'count': facet_value,
'qs': qs.urlencode(),
}
if state == 'active':
active_facets.append(facet_dict)
else:
facets.append(facet_dict)
return {"hits": hits, "facets": facets, "active_facets": active_facets}
def contacts(self, query):
if query:
to = QueryParser('to', self._index.schema)
cc = QueryParser('cc', self._index.schema)
bcc = QueryParser('bcc', self._index.schema)
with self._index.searcher() as searcher:
to = searcher.search(to.parse("*%s*" % query),
limit=None,
groupedby=sorting.FieldFacet(
'to', allow_overlap=True)).groups()
cc = searcher.search(cc.parse("*%s*" % query),
limit=None,
groupedby=sorting.FieldFacet(
'cc', allow_overlap=True)).groups()
bcc = searcher.search(bcc.parse("*%s*" % query),
limit=None,
groupedby=sorting.FieldFacet(
'bcc', allow_overlap=True)).groups()
return flatten([to, cc, bcc])
return []
def search(self, text: str, sorted_by_count=False, fieldname='body'):
"""returns (dset, item_gen)
results.docs(): a set of docnum of the results in index
results.items(): a generator for (docnum, score) in from highest score/count to lowest
"""
isearcher = self._searcher
skw = {'limit': None}
skw['q'] = QueryParser("body", self.ix.schema).parse(text)
if sorted_by_count:
skw['sortedby'] = sorting.FieldFacet('count', reverse=True)
results = isearcher.search(**skw)
return results.docs(), results.items()
def search(humanReadableId):
query = request.args.get('q', '').strip()
pagination = None
if query:
index_base_dir = config().get_path("ZIM", "wikipedia_index_dir")
index_dir = os.path.join(index_base_dir, humanReadableId)
page = int(request.args.get('page', 1))
# Load index so we can query it for which fields exist
ix = whoosh_open_dir_32_or_64(index_dir)
# Set a higher value for the title field so it is weighted more
weighting = scoring.BM25F(title_B=1.0)
# Sort pages with "Image:" in their title after
# regular articles
def image_pages_last(searcher, docnum):
fields = searcher.stored_fields(docnum)
if fields['title'].find("Image:") == 0:
return 1
else:
return 0
# Support older whoosh indexes that do not have a reverse_links field
if 'reverse_links' in ix.schema.names():
sortedby = sorting.MultiFacet([
sorting.FunctionFacet(image_pages_last),
sorting.ScoreFacet(),
sorting.FieldFacet("reverse_links", reverse=True),
])
else:
sortedby = sorting.MultiFacet([
sorting.FunctionFacet(image_pages_last),
sorting.ScoreFacet(),
])
(pagination, suggestion) = paginated_search(ix, ["title", "content"],
query,
page,
weighting=weighting,
sort_column=sortedby)
else:
flash(_('Please input keyword(s)'), 'error')
return render_template('zim/search.html',
humanReadableId=humanReadableId,
pagination=pagination,
suggestion=suggestion,
keywords=query,
endpoint_desc=EndPointDescription(
'zim_views.search',
{'humanReadableId': humanReadableId}))
def test_group_types():
schema = fields.Schema(a=fields.STORED, b=fields.TEXT, c=fields.ID)
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(a=0, b=u("blah"), c=u("apple"))
w.add_document(a=1, b=u("blah blah"), c=u("bear"))
w.add_document(a=2, b=u("blah blah blah"), c=u("apple"))
w.add_document(a=3, b=u("blah blah blah blah"), c=u("bear"))
w.add_document(a=4, b=u("blah blah blah blah blah"), c=u("apple"))
w.add_document(a=5, b=u("blah blah blah blah blah blah"), c=u("bear"))
w.add_document(a=6,
b=u("blah blah blah blah blah blah blah"),
c=u("apple"))
with ix.searcher() as s:
q = query.Term("b", "blah")
f = sorting.FieldFacet("c", maptype=sorting.UnorderedList)
r = s.search(q, groupedby=f)
gs = r.groups("c")
assert_equal(gs["apple"], [0, 2, 4, 6])
assert_equal(gs["bear"], [1, 3, 5])
f = sorting.FieldFacet("c", maptype=sorting.Count)
r = s.search(q, groupedby=f)
gs = r.groups("c")
assert_equal(gs["apple"], 4)
assert_equal(gs["bear"], 3)
f = sorting.FieldFacet("c", maptype=sorting.Best)
r = s.search(q, groupedby=f)
gs = r.groups()
assert_equal(gs["apple"], 6)
assert_equal(gs["bear"], 5)
r = s.search(q, groupedby="c", maptype=sorting.Count)
gs = r.groups()
assert_equal(gs["apple"], 4)
assert_equal(gs["bear"], 3)
def get_context(self):
if self.context:
return self.context
self.get_index()
from whoosh.qparser.dateparse import DateParserPlugin
datetimes = sorting.FieldFacet("datetime", reverse=True)
parser = qparser.QueryParser("text", self.index.schema)
query = parser.parse("*")
searcher = self.index.searcher()
restrict_replies = whoosh.query.Term("reply", True)
results = searcher.search(
query, mask=restrict_replies, sortedby=datetimes, limit=MAX_TWEETS_NUMBER)
click.secho("Adding to context...", fg="green")
context = set()
for result in results:
text = result["text"]
click.secho(text)
import re
# don't want these influence context
# drop usernames
text = re.sub(r"@[^ ]+", " ", text)
# drop links
text = re.sub(r"http(s?)://[^ ]+", " ", text)
# drop cut-off text from the end of manual rts
text = re.sub(r"[^ ]+…$", " ", text)
context.update(text.split(" "))
# split and discard empty strings
context = " ".join(filter(None, context))
click.secho("Processed context:", fg="green")
click.secho(context)
self.context = context
return self.context
