141 lines
4.1 KiB
Python
141 lines
4.1 KiB
Python
import logging
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from django.db.models import Q
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from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector, TrigramSimilarity
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from products.models import Product
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def extract_search_filters(result_set):
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"""
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Returned object should look something like:
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{
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"singles": [], # non tree tags
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"entry_1": [ 'tag1', 'tag2' ],
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"entry_2": [ 'tag1', 'tag2' ],
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}
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"""
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filter_dict = {
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'singles': set(),
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}
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for item in result_set:
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try:
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# extract tags
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tags = item.tags.all()
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for tag in tags:
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if len(tag.name.split('/')) == 1:
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filter_dict['singles'].add(tag.name)
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else:
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# set penultimate tag as header
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chunks = tag.name.split('/')
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header = chunks[-2]
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name = chunks[-1]
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# check if
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entry = filter_dict.get(header)
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if entry is None:
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filter_dict[header] = set()
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filter_dict[header].add(name)
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# extract attributes
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attributes = item.attributes.all()
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for tag in attributes:
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if len(tag.name.split('/')) == 1:
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filter_dict['singles'].add(tag.name)
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else:
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# set penultimate tag as header
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chunks = tag.name.split('/')
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header = chunks[-2]
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name = chunks[-1]
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# check if
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entry = filter_dict.get(header)
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if entry is None:
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filter_dict[header] = set()
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filter_dict[header].add(name)
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except Exception as e:
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logging.error(f'Extacting filters for {item}')
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return filter_dict
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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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categories = Product.category.tag_model.objects.filter(name__icontains=keyword)
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# search in attributes
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attributes = Product.attributes.tag_model.objects.filter(name__icontains=keyword)
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# unified tag search
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products_qs = Product.objects.filter(
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Q(name__icontains=keyword)|
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Q(description__icontains=keyword)|
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Q(tags__in=tags)|
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Q(category__in=categories)|
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Q(attributes__in=attributes)
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)
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return products_qs
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def find_related_products_v5(keyword):
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"""
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Single query solution, using Q objects
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"""
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products_qs = Product.objects.filter(
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Q(name__icontains=keyword)|
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Q(description__icontains=keyword)|
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Q(tags__label__icontains=keyword)|
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Q(category__name__icontains=keyword)|
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Q(attributes__label__icontains=keyword)
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)
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return products_qs
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def find_related_products_v2(keyword):
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"""
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More advanced: 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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search=vector
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).filter(search=keyword)
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return products_qs
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def find_related_products_v3(keyword):
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"""
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Ranked 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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query = SearchQuery(keyword)
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products_qs = Product.objects.annotate(
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rank=SearchRank(vector, query)
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).order_by('-rank')
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return products_qs
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def find_related_products_v4(keyword):
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"""
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Using trigrams
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"""
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# fields=('name', 'description', 'tags__label', 'attributes__label', 'category__name')
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products_qs = Product.objects.annotate(
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similarity=TrigramSimilarity('name', keyword),
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).order_by('-similarity')
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return products_qs
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