def search(request):
query = request.GET.get("q")
page_number = request.GET.get("page")
search_vector = (psql_search.SearchVector("title", weight="A") +
psql_search.SearchVector("body", weight="A") +
psql_search.SearchVector("summary", weight="B"))
search_query = psql_search.SearchQuery(query)
posts = (Post.published.annotate(
rank=psql_search.SearchRank(search_vector, search_query)).filter(
rank__gte=0.3).order_by("-rank"))
paginator = Paginator(posts, os.environ.get("SEARCH_RESULTS_PER_PAGE", 6))
try:
posts = paginator.page(number=page_number)
except PageNotAnInteger:
posts = paginator.page(number=1) # first page
except EmptyPage:
posts = paginator.page(number=paginator.num_pages) # last page
return render(
request,
"portfolio/search.html",
{
"posts": posts,
"active": "home",
"query": query
},
)
def get_search_results(query: str, user: models.User) -> QuerySet:
"""Gets search results for the user..
Args:
query: The raw text query submitted by the user.
user: The user who submitting the query.
Returns:
A list of entities ranked in order to its match to the query.
"""
search_vectors: List[search.SearchVector] = [
search.SearchVector('patient_profile__first_name', weight='B'),
search.SearchVector('patient_profile__last_name', weight='A')
]
combined_search_vector = search_vectors[0]
for index, vector in enumerate(search_vectors):
if index == 0:
continue
combined_search_vector += vector
search_query: search.SearchQuery = search.SearchQuery(query)
users: QuerySet[models.User] = models.User.objects.all()
return users.annotate(
rank=search.SearchRank(combined_search_vector, search_query)).filter(
rank__gte=MATCHING_THRESHOLD).order_by('-rank')
def packages_search_filter(query, queryset, word_rule='&', quantity=10):
# Split the query into whitespace-separated subqueries, each of which will
# either be queried together or separately, depending on the options.
nice_query = split(r'\s', query.strip())
models = queryset
if not models:
models = Package.objects.all()
vector = search.SearchVector('name', weight='A')
vector += search.SearchVector(Func(F('keywords'), Value(' '),
function='array_to_string'),
weight='B')
vector += search.SearchVector('description', weight='B')
search_query = search.SearchQuery(nice_query[0])
for ii in range(1, len(nice_query)):
if word_rule == '&':
search_query &= search.SearchQuery(nice_query[ii])
else:
search_query |= search.SearchQuery(nice_query[ii])
rank = search.SearchRank(vector, search_query)
annotated = models.annotate(rank=rank).order_by('-rank')
# Grab any exact (case-insensitive) name match to the query. This is
# necessary for very short package names.
name_pass = annotated.filter(name__iexact=query)
# Filter out all rank 0 results.
rank_high_pass = annotated.filter(rank__gt=0)
# Add the exact match at the front, if it exists.
results = name_pass.union(rank_high_pass)
# Restrict the number of results to the quantity argument.
if quantity != -1:
results = results[0:quantity]
return results
def __init__(self, qd):
self.category, self.subcategory = parse_cats(qd.get('category', ''))
st = qd.get('status', '')
if st.isdigit():
self.status = int(st)
else:
self.status = None
self.sort = qd.get('sort', None)
self.order = qd.get('order', None)
mx = qd.get('max', '')
if mx.isdigit():
self.max = int(mx)
else:
self.max = 20
self.query = qd.get('query', '').lower()
self.sv = search.SearchVector('name','description')
def update(self):
lines = self.entry.lines.all()
text_vector = pg_search.SearchVector(models.Value(
strip_tags(self.entry.note), output_field=models.TextField()),
weight='C',
config='english')
for tag in self.entry.tags.all():
text_vector += pg_search.SearchVector(models.Value(
tag.name, output_field=models.TextField()),
weight='A',
config='english')
for tag in self.entry.event.tags.all():
text_vector += pg_search.SearchVector(models.Value(
tag.name, output_field=models.TextField()),
weight='C',
config='english')
if lines.exists():
speaker_vectors = []
for line in lines:
text_vector += pg_search.SearchVector(models.Value(
strip_tags(line.text), output_field=models.TextField()),
weight='B',
config='english')
speaker_vectors.append(
pg_search.SearchVector(models.Value(
strip_tags(line.speaker),
output_field=models.TextField()),
weight='A',
config='english'))
text_vector += pg_search.SearchVector(models.Value(
strip_tags(line.speaker), output_field=models.TextField()),
weight='D',
config='english')
speaker_vector = speaker_vectors[0]
for sv in speaker_vectors[1:]:
speaker_vector += sv
self.speaker_vector = speaker_vector
self.text_vector = text_vector
self.save()
def auto_update_search_vector(sender: Any, instance: Any, *args: Any,
**kwargs: Any) -> None:
"""Keep the index up-to-date automatically"""
sender.objects.filter(pk=instance.pk).update(
search_vector=s.SearchVector("text_feature"))
