kit-eco.social/consumocuidado-server
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improvements to search functionality

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This commit is contained in:
Sam
2021-03-02 11:01:22 +00:00
parent 626461bd99
commit 9baa13f30a
3 changed files with 12 additions and 74 deletions
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8
products/tests.py
View File

@@ -675,9 +675,9 @@ class ProductSearchTest(TestCase):
def test_anon_user_can_filter_by_category(self):
expected_instances = [
self.factory(tags="lunares/rojos", category='ropa', description="zapatos verdes", discount=None),
self.factory(tags="lunares/rojos", category="ropa", discount=0.00),
self.factory(attributes='"zapatos de campo", tono/oscuro', category="ropa", discount=9.00),
self.factory(tags="lunares/rojos", category='ropa/nueva', description="zapatos verdes", discount=None),
self.factory(tags="lunares/rojos", category="ropa/nueva", discount=0.00),
self.factory(attributes='"zapatos de campo", tono/oscuro', category="ropa/nueva", discount=9.00),
]
unexpected_instances = [
self.factory(description="chanclas", tags='rojos'),
@@ -686,7 +686,7 @@ class ProductSearchTest(TestCase):
q = quote("zapatos rojos")
# discount=true
url = f"{self.endpoint}?q={q}&category=ropa"
url = f"{self.endpoint}?q={q}&category=ropa/nueva"
# send in request
response = self.client.get(url)
# check response

64
products/utils.py
View File

@@ -85,56 +85,6 @@ def extract_search_filters(result_set):
return filter_dict
def find_related_products_v1(keyword):
"""
Classical approach to the search
Using Q objects
"""
# search in tags
tags = Product.tags.tag_model.objects.filter(name__icontains=keyword)
# search in category
categories = Product.category.tag_model.objects.filter(name__icontains=keyword)
# search in attributes
attributes = Product.attributes.tag_model.objects.filter(name__icontains=keyword)
# unified tag search
products_qs = Product.objects.filter(
Q(name__icontains=keyword)|
Q(description__icontains=keyword)|
Q(tags__in=tags)|
Q(category__in=categories)|
Q(attributes__in=attributes)
)
return products_qs
def find_related_products_v5(keyword):
"""
Single query solution, using Q objects
"""
products_qs = Product.objects.filter(
Q(name__icontains=keyword)|
Q(description__icontains=keyword)|
Q(tags__label__icontains=keyword)|
Q(category__name__icontains=keyword)|
Q(attributes__label__icontains=keyword)
)
return set(products_qs)
def find_related_products_v2(keyword):
"""
More advanced: using search vectors
"""
fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
vector = SearchVector(*fields)
products_qs = Product.objects.annotate(
search=vector
).filter(search=keyword)
return set(products_qs)
def find_related_products_v3(keyword):
"""
Ranked product search
@@ -215,20 +165,6 @@ def find_related_products_v6(keyword, shipping_cost=None, discount=None, categor
return set(products_qs), min_price, max_price
def find_related_products_v4(keyword):
"""
Similarity-ranked search using trigrams
Not working
"""
# fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
products_qs = Product.objects.annotate(
similarity=TrigramSimilarity('name', keyword),
).order_by('-similarity')
return set(products_qs)
def product_loader(csv_reader, user, company=None):
"""
Parse csv data and extract:

2
products/views.py
View File

@@ -161,6 +161,8 @@ def product_search(request):
for chunk in chunks:
product_set, min_price, max_price = find_related_products_v6(chunk, shipping_cost, discount, category, tags, price_min, price_max)
# update price values
if product_set:
# import ipdb; ipdb.set_trace()
if prices['min'] is None or min_price['price__min'] < prices['min']:
prices['min'] = min_price['price__min']
if prices['max'] is None or max_price['price__max'] > prices['max']: