def test_highlight_ngrams():
schema = fields.Schema(text=fields.NGRAMWORDS(stored=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(text=u("Multiplication and subtraction are good"))
with ix.searcher() as s:
qp = qparser.QueryParser("text", ix.schema)
q = qp.parse(u("multiplication"))
r = s.search(q)
assert r.scored_length() == 1
r.fragmenter = highlight.SentenceFragmenter()
r.formatter = highlight.UppercaseFormatter()
snippet = r[0].highlights("text")
assert snippet == "MULTIPLICATIon and subtracTION are good"
def test_workflow_manual():
schema = fields.Schema(id=fields.ID(stored=True),
title=fields.TEXT(stored=True))
ix = RamStorage().create_index(schema)
w = ix.writer()
w.add_document(id=u("1"), title=u("The man who wasn't there"))
w.add_document(id=u("2"), title=u("The dog who barked at midnight"))
w.add_document(id=u("3"), title=u("The invisible man"))
w.add_document(id=u("4"), title=u("The girl with the dragon tattoo"))
w.add_document(id=u("5"), title=u("The woman who disappeared"))
w.commit()
with ix.searcher() as s:
# Parse the user query
parser = qparser.QueryParser("title", schema=ix.schema)
q = parser.parse(u("man"))
# Extract the terms the user used in the field we're interested in
terms = [
text for fieldname, text in q.all_terms() if fieldname == "title"
]
# Perform the search
r = s.search(q)
assert_equal(len(r), 2)
# Use the same analyzer as the field uses. To be sure, you can
# do schema[fieldname].analyzer. Be careful not to do this
# on non-text field types such as DATETIME.
analyzer = schema["title"].analyzer
# Since we want to highlight the full title, not extract fragments,
# we'll use WholeFragmenter.
nf = highlight.WholeFragmenter()
# In this example we'll simply uppercase the matched terms
fmt = highlight.UppercaseFormatter()
outputs = []
for d in r:
text = d["title"]
outputs.append(highlight.highlight(text, terms, analyzer, nf, fmt))
assert_equal(outputs,
["The invisible MAN", "The MAN who wasn't there"])
def test_paged_highlights():
schema = fields.Schema(text=fields.TEXT(stored=True))
ix = RamStorage().create_index(schema)
with ix.writer() as w:
w.add_document(text=u("alfa bravo charlie delta echo foxtrot"))
w.add_document(text=u("bravo charlie delta echo foxtrot golf"))
w.add_document(text=u("charlie delta echo foxtrot golf hotel"))
w.add_document(text=u("delta echo foxtrot golf hotel india"))
w.add_document(text=u("echo foxtrot golf hotel india juliet"))
w.add_document(text=u("foxtrot golf hotel india juliet kilo"))
with ix.searcher() as s:
q = query.Term("text", u("alfa"))
page = s.search_page(q, 1, pagelen=3)
page.results.fragmenter = highlight.WholeFragmenter()
page.results.formatter = highlight.UppercaseFormatter()
hi = page[0].highlights("text")
assert hi == u("ALFA bravo charlie delta echo foxtrot")
def test_correct_correct():
from whoosh import qparser
schema = fields.Schema(a=fields.TEXT())
with TempIndex(schema) as ix:
with ix.writer() as w:
w.add_document(a=u'dworska')
w.add_document(a=u'swojska')
with ix.searcher() as s:
s = ix.searcher()
qtext = u'dworska'
qp = qparser.QueryParser('a', ix.schema)
q = qp.parse(qtext, ix.schema)
c = s.correct_query(q, qtext)
assert c.string == "dworska"
string = c.format_string(highlight.UppercaseFormatter())
assert string == "dworska"
def test_correct_correct():
from whoosh import qparser
schema = fields.Schema(a=fields.TEXT(spelling=True))
ix = RamStorage().create_index(schema)
ix_writer = ix.writer()
ix_writer.add_document(a=u('dworska'))
ix_writer.add_document(a=u('swojska'))
ix_writer.commit()
s = ix.searcher()
qtext = u('dworska')
qp = qparser.QueryParser('a', ix.schema)
q = qp.parse(qtext, ix.schema)
c = s.correct_query(q, qtext)
assert c.string == "dworska"
assert c.format_string(highlight.UppercaseFormatter()) == "dworska"
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 test_workflow_easy():
schema = fields.Schema(id=fields.ID(stored=True),
title=fields.TEXT(stored=True))
ix = RamStorage().create_index(schema)
w = ix.writer()
w.add_document(id=u("1"), title=u("The man who wasn't there"))
w.add_document(id=u("2"), title=u("The dog who barked at midnight"))
w.add_document(id=u("3"), title=u("The invisible man"))
w.add_document(id=u("4"), title=u("The girl with the dragon tattoo"))
w.add_document(id=u("5"), title=u("The woman who disappeared"))
w.commit()
with ix.searcher() as s:
# Parse the user query
parser = qparser.QueryParser("title", schema=ix.schema)
q = parser.parse(u("man"))
r = s.search(q, terms=True)
assert len(r) == 2
r.fragmenter = highlight.WholeFragmenter()
r.formatter = highlight.UppercaseFormatter()
outputs = [hit.highlights("title") for hit in r]
assert outputs == ["The invisible MAN", "The MAN who wasn't there"]
myquery = parser.parse( query.search_terms )
results = []
reponse = []
rr={}
with self.queryIndex.searcher() as searcher:
results = searcher.search( myquery )
for result in results:
temp = result['content']
temp = temp.split("\t")
sugg= temp[0]
print result
rr['content']= sugg
results.fragmenter = highlight.ContextFragmenter(surround=40)
results.formatter = highlight.UppercaseFormatter()
response = parse_whoosh_trec('WhooshQueryEngine', query.search_terms, results)
return response
# ----- The Following are Whoosh errors -----
# There's a problem with the Whoosh query created from the users query
except QueryError, e:
raise SearchEngineError("Whoosh Query Suggest Engine", e, errorType="Whoosh", query=query)
# Our Whoosh Index is empty
except EmptyIndexError, e:
raise SearchEngineError("Whoosh Query Suggest Engine", e, errorType="Whoosh")
# Our Whoosh Index does not match our version of Whoosh
