improvements to search functionality

products/utils.py

@@ -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: