new and improved Q-based search implementation

products/utils.py

@@ -81,11 +81,23 @@ def find_related_products_v1(keyword):
     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 products_qs
+
+
 def find_related_products_v2(keyword):
     """
-    More advanced search
+    More advanced: using search vectors
-
-    Using search vectors
     """
     fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
     vector = SearchVector(*fields)

products/views.py

@@ -23,7 +23,7 @@ from companies.models import Company
 from history.models import HistorySync
 
 from back_latienda.permissions import IsCreator
-from .utils import extract_search_filters, find_related_products_v1, find_related_products_v2, find_related_products_v3
+from .utils import extract_search_filters, find_related_products_v5, find_related_products_v2, find_related_products_v3
 from utils.tag_serializers import TaggitSerializer
 from utils.tag_filters import ProductTagFilter
 
@@ -155,7 +155,8 @@ def product_search(request):
             chunks = query_string.split(' ')
 
             for chunk in chunks:
-                  products_qs = find_related_products_v1(chunk)
+                  # import ipdb; ipdb.set_trace()
+                  products_qs = find_related_products_v5(chunk)
                   # products_qs = find_related_products_v2(chunk)
                   # products_qs = find_related_products_v3(chunk)
                   for instance in products_qs: