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@@ -58,6 +58,12 @@ def extract_search_filters(result_set):
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def find_related_products_v1(keyword):
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"""
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Classical approach to the search
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Using Q objects
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"""
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# search in tags
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tags = Product.tags.tag_model.objects.filter(name__icontains=keyword)
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# search in category
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@@ -76,6 +82,11 @@ def find_related_products_v1(keyword):
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def find_related_products_v2(keyword):
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"""
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More advanced search
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Using search vectors
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"""
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fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
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vector = SearchVector(*fields)
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products_qs = Product.objects.annotate(
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@@ -84,7 +95,16 @@ def find_related_products_v2(keyword):
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return products_qs
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def alt_rank_find_related_products(keyword):
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def find_related_products_v3(keyword):
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"""
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Fully loaded product search
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SearchVectors for the fields
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SearchQuery for the value
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SearchRank for relevancy scoring and ranking
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PROBLEM: returns unrelated instances
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"""
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# TODO: figure out why it includes unrelated instances
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fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
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vector = SearchVector(*fields)
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@@ -23,7 +23,7 @@ from companies.models import Company
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from history.models import HistorySync
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from back_latienda.permissions import IsCreator
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from .utils import extract_search_filters, find_related_products_v1, find_related_products_v2, alt_rank_find_related_products
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from .utils import extract_search_filters, find_related_products_v1, find_related_products_v2, find_related_products_v3
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from utils.tag_serializers import TaggitSerializer
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from utils.tag_filters import ProductTagFilter
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@@ -157,7 +157,7 @@ def product_search(request):
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for chunk in chunks:
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products_qs = find_related_products_v1(chunk)
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# products_qs = find_related_products_v2(chunk)
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# products_qs = alt_rank_find_related_products(chunk)
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# products_qs = find_related_products_v3(chunk)
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for instance in products_qs:
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result_set.add(instance)
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