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https://github.com/magnum-opus-tender-hack/backend.git
synced 2024-11-22 09:16:36 +03:00
made all search smart
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parent
f823bcff62
commit
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@ -8,37 +8,37 @@ def autocomplete_schema(val: str, exclude: List[Dict]):
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name_exclude = [x["value"] for x in exclude if x["type"] == "Name"]
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category_exclude = [x["value"] for x in exclude if x["type"] == "Category"]
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schema = []
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if not category_exclude:
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schema.extend(
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[
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{
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"coordinate": cat["name"].replace("ё", "е").lower().index(val.lower()),
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"value": {"type": "Category", "value": cat["name"]},
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}
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for cat in Category.objects.filter(name__unaccent__icontains=val)[
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:10
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].values("name")
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]
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)
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if not name_exclude:
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schema.extend(
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[
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{
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"coordinate": product["name"].lower().index(val.lower()),
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"coordinate": product["name"].replace("ё", "е").lower().index(val.lower()),
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"value": {
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"type": "Name",
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"value": product["name"],
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},
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}
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for product in Product.objects.filter(name__unaccent__icontains=val)[
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:20
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].values("name")
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]
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)
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if not category_exclude:
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schema.extend(
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[
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{
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"coordinate": cat["name"].lower().index(val.lower()),
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"value": {"type": "Category", "value": cat["name"]},
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}
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for cat in Category.objects.filter(name__unaccent__icontains=val)[
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:20
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:30
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].values("name")
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]
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)
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schema.extend(
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[
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{
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"coordinate": char["value"].lower().index(val.lower()),
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"coordinate": char["value"].replace("ё", "е").lower().index(val.lower()),
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"value": {"type": char["name"], "value": char["value"]},
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}
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for char in Characteristic.objects.filter(value__unaccent__icontains=val)[
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@ -49,12 +49,12 @@ def autocomplete_schema(val: str, exclude: List[Dict]):
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schema.extend(
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[
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{
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"coordinate": char["value"].lower().index(val.lower()),
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"coordinate": char["name"].lower().replace("ё", "е").index(val.lower()),
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"value": {
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"type": char["name"] + "_numeric",
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"value": char["value"]
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"value": char["name"]
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}
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} for char in UnitCharacteristic.objects.filter(value__unaccent__icontains=val)[:20].values("name", "value")
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} for char in UnitCharacteristic.objects.filter(name__unaccent__icontains=val)[:20].values("name", "value")
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]
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)
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return schema
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@ -18,7 +18,7 @@ def group(data: List[Dict], search_fields_d: List[Dict]) -> List[Dict]:
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else:
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re[field] = []
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re[field].append(
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[x["type"] for x in search_fields_dict if x["value"] == field][
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[x["type"] for x in search_fields_dict if x["value"].lower() == field][
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0
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]
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)
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@ -43,7 +43,7 @@ def _clean_text(text: str) -> List[str]:
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text = text.split()
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re = []
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for word in text:
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re.append(lemmatize(word))
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re.append(word)
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return re
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@ -62,35 +62,76 @@ def apply_qs_search(text: str):
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def apply_all_qs_search(orig_qs, text: str):
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# words
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qs = apply_qs_search(text)
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text = _clean_text(text)
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# categories
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cats = Category.objects.none()
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for word in text:
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cats = cats | cats.filter(name__icontains=word)
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qs = Product.objects.filter(category__in=cats).order_by("-score") | qs
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u_qs = None
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# characteristics
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chars = Characteristic.objects.none()
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for word in text:
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chars = (
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chars
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| Characteristic.objects.filter(
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value__icontains=word,
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)
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| Characteristic.objects.filter(
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value__unaccent__trigram_similar=word,
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)
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)
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qs = (
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Product.objects.filter(characteristics__characteristic__in=chars).order_by(
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"-score"
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)
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| qs
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)
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# try to find Unit characteristics
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if any(x.isnumeric() for x in text):
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u_qs = ProductUnitCharacteristic.objects.filter()
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for i in range(len(text)):
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el = text[i]
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if el.isnumeric():
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if i == len(text) - 1:
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if ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i - 1]
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).exists():
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unit = ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i - 1]
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)
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u_qs = u_qs & process_unit_operation(unit, f"={text[i]}")
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del text[i]
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del text[i - 1]
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break
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elif len(text) - 1 > i >= 1:
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if ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i - 1]
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).exists():
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unit = ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i - 1]
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)[0]
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u_qs = u_qs & process_unit_operation(unit, f"={text[i]}")
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del text[i]
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del text[i - 1]
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break
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elif ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i + 1]
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).exists():
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unit = UnitCharacteristic.objects.filter(
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ProductUnitCharacteristic=text[i + 1]
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)[0]
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u_qs = u_qs & process_unit_operation(unit, f"={text[i]}")
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del text[i]
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del text[i + 1]
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break
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else:
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if ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i + 1]
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).exists():
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unit = ProductUnitCharacteristic.objects.filter(
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characteristic__name__icontains=text[i + 1]
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)[0]
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u_qs = u_qs & process_unit_operation(unit, f"={text[i]}")
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del text[i]
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del text[i + 1]
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break
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return qs & orig_qs
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prod = Product.objects.filter()
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for word in text:
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car = ProductCharacteristic.objects.filter(
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characteristic__value__icontains=word,
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)
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qs = (
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Product.objects.filter(name__icontains=word)
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| Product.objects.filter(name__unaccent__trigram_similar=word)
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| Product.objects.filter(category__name__icontains=word)
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| Product.objects.filter(characteristics__in=car)
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)
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prod = prod & qs
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if u_qs:
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prod = prod & Product.objects.filter(unit_characteristics__in=u_qs)
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return prod
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def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
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@ -174,11 +215,9 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
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qs = qs & apply_qs_search(val)
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qs = qs.order_by("-score")
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elif typ == "All":
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qs = apply_all_qs_search(qs, val)
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qs = apply_all_qs_search(qs, val) & qs
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elif typ == "Category":
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qs = qs.filter(category__name__unaccent__trigram_similar=val) | qs.filter(
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category__name__icontains=val
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)
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qs = qs.filter(category__name__icontains=val)
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qs = qs.order_by("-score")
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elif typ == "Characteristic":
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char = ProductCharacteristic.objects.filter(product__in=qs)
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@ -19,6 +19,9 @@ def spell_check_en(word: str) -> str:
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return res
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morph = pymorphy2.MorphAnalyzer()
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def lemmatize(word):
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p = pymorphy2.MorphAnalyzer().parse(word)[0]
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p = morph.parse(word)[0]
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return p.normal_form
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