magnum-opus-tender-hack/backend
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fixed global lookup

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This commit is contained in:
Alexander Karpov 2022-10-22 18:20:58 +03:00
parent faaccff7dc
commit b683d2e444
2 changed files with 71 additions and 14 deletions
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1
app/search/models.py
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@ -27,7 +27,6 @@ class UnitCharacteristic(models.Model):
def serialize_self(self):
return {
"id": self.id,
"name": self.name,
"value": self.numeric_value_min
if self.numeric_value_min == self.numeric_value_max

84
app/search/services/search.py
View File

@ -1,11 +1,14 @@
import string
from django.db.models import QuerySet
from search.models import (
Product,
Characteristic,
ProductCharacteristic,
ProductUnitCharacteristic,
UnitCharacteristic,
Category,
)
from typing import List
@ -39,17 +42,51 @@ def process_unit_operation(unit: ProductUnitCharacteristic.objects, operation: s
return unit
def apply_qs_search(qs: Product.objects, text: str):
def _clean_text(text: str) -> List[str]:
for st in [".", ",", "!", "?"]:
text = text.replace(st, " ")
text = text.split()
return text
def apply_qs_search(qs: Product.objects, text: str):
text = _clean_text(text)
words = Product.objects.none()
for word in text:
qs = qs.filter(name__unaccent__trigram_similar=word) | qs.filter(
name__unaccent__icontains=word
words = (
words
| Product.objects.filter(name__unaccent__trigram_similar=word)
| Product.objects.filter(name__unaccent__icontains=word)
)
print(words)
qs = qs | words
print(qs)
return qs
def apply_all_qs_search(orig_qs, text: str):
# words
qs = apply_qs_search(Product.objects.none(), text)
text = _clean_text(text)
# categories
cats = Category.objects.none()
for word in text:
cats = cats | cats.filter(name__icontains=word)
qs = qs | Product.objects.filter(category__in=cats)
# characteristics
chars = Characteristic.objects.none()
for word in text:
chars = chars | chars.filter(
value__icontains=word,
)
qs = qs | Product.objects.filter(characteristics__characteristic__in=chars)
# print(qs)
return qs & orig_qs
def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
prep_data = []
prep_dict = {}
@ -57,7 +94,7 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
# --------------------------------------- prepare filters -------------------------------------------------------- #
for x in data:
dat = dict(x)
if x["type"] in ["Name", "Category"]:
if x["type"] in ["Name", "Category", "Characteristic", "All"]:
prep_data.append(
{
"type": dat["type"],
@ -89,17 +126,22 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
x["type"]
] | process_unit_operation(unit, x["value"])
else:
prep_dict[x["type"]] = prep_dict[
x["type"]
] | ProductCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
characteristic__value__unaccent__trigram_similar=val,
prep_dict[x["type"]] = (
prep_dict[x["type"]]
| ProductCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
characteristic__value__unaccent__trigram_similar=val,
)
| ProductCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
characteristic__value__icontains=val,
)
)
else:
if x["type"].startswith("*"):
prep_dict_char_type[x["type"]] = UnitCharacteristic.objects.filter(
name__unaccent__trigram_similar=x["type"]
)
) | UnitCharacteristic.objects.filter(name__icontains=x["type"])
unit = ProductUnitCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
)
@ -107,10 +149,13 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
else:
prep_dict_char_type[x["type"]] = Characteristic.objects.filter(
name__unaccent__trigram_similar=x["type"]
)
) | Characteristic.objects.filter(name__icontains=x["type"])
prep_dict[x["type"]] = ProductCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
characteristic__value__unaccent__trigram_similar=val,
) | ProductCharacteristic.objects.filter(
characteristic__in=prep_dict_char_type[x["type"]],
characteristic__value__icontains=val,
)
for el, val in prep_dict.items():
prep_data.append({"type": el, "value": val})
@ -121,8 +166,18 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
val = x["value"]
if typ == "Name":
qs = apply_qs_search(qs, val)
elif typ == "All":
qs = apply_all_qs_search(qs, val)
elif typ == "Category":
qs = qs.filter(category__name__unaccent__trigram_similar=val)
qs = qs.filter(category__name__unaccent__trigram_similar=val) | qs.filter(
category__name__icontains=val
)
elif typ == "Characteristic":
char = ProductCharacteristic.objects.filter(product__in=qs)
char = char.filter(characteristic__value__icontains=val) | char.filter(
characteristic__value__unaccent__trigram_similar=val
)
qs = qs.filter(characteristics__in=char)
elif typ == "Unknown":
if val[0] in string.printable:
val = "".join(translate_en_ru(val))
@ -145,4 +200,7 @@ def process_search(data: List[dict], limit=5, offset=0) -> List[dict]:
qs = qs.filter(unit_characteristics__in=val)
else:
qs = qs.filter(characteristics__in=val)
return [x.serialize_self() for x in qs[offset : offset + limit]]
return [
x.serialize_self()
for x in qs.distinct().order_by("-score")[offset : offset + limit]
]