Egor fureunoir Gorbunov
778d65b963
Fixes: None; Extra: 1) Remove unused "synonym_filter" and "synonym_analyzer" definitions in Elasticsearch configuration; 2) Cleanup redundant code for better maintainability;
274 lines
8.6 KiB
Python
274 lines
8.6 KiB
Python
from django.conf import settings
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from django.shortcuts import get_object_or_404
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from django.utils.text import slugify
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from django_elasticsearch_dsl import fields
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from django_elasticsearch_dsl.registries import registry
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from elasticsearch.dsl import SF, Q, Search
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from rest_framework.request import Request
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from core.models import Brand, Category, Product
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SMART_FIELDS = [
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"name^4",
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"name.ngram^3",
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"name.phonetic",
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"description^2",
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"description.ngram",
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"description.phonetic",
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"name.auto^4",
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"description.auto^2",
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"brand__name^2",
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"brand__name.ngram",
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"brand__name.auto",
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"category__name^2",
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"category__name.ngram",
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"category__name.auto",
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"title^4",
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"title.ngram^3",
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"title.phonetic",
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"title.auto^4",
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]
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def process_query(
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query: str = "", request: Request | None = None, filters: dict | None = None
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):
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if not query:
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raise ValueError("no search term provided.")
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filters = filters or {}
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base_q = Q(
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"bool",
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should=[
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Q(
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"multi_match",
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query=query,
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fields=SMART_FIELDS,
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fuzziness="AUTO",
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operator="and",
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),
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Q(
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"multi_match",
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query=query,
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fields=[f for f in SMART_FIELDS if f.endswith(".auto")],
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type="bool_prefix",
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),
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],
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minimum_should_match=1,
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)
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functions = [
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SF("field_value_factor", field="quantity", modifier="log1p", missing=0),
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SF("field_value_factor", field="rating", modifier="sqrt", missing=0),
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SF("exp", field="created_at", origin="now", scale="30d"), # newness boost
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]
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fq = [
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Q("function_score", query=base_q, functions=functions, boost_mode="sum"),
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]
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if "category" in filters:
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fq.append(Q("term", category__slug=filters["category"]))
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if "brand" in filters:
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fq.append(Q("term", brand__slug=filters["brand"]))
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if "price_min" in filters or "price_max" in filters:
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range_q = {}
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if "price_min" in filters:
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range_q["gte"] = filters["price_min"]
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if "price_max" in filters:
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range_q["lte"] = filters["price_max"]
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fq.append(Q("range", price=range_q))
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search = (
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Search(index=["products", "categories", "brands", "posts"])
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.query(base_q)
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.filter(*fq)
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.extra(size=20)
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.highlight("description", fragment_size=150)
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.extra(
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aggs={
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"by_category": {"terms": {"field": "category.keyword"}},
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"by_brand": {"terms": {"field": "brand.keyword"}},
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"price_stats": {"stats": {"field": "price"}},
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}
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)
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)
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response = search.execute()
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results: dict = {"products": [], "categories": [], "brands": [], "posts": []}
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for hit in response.hits:
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obj_uuid = getattr(hit, "uuid", None) or hit.meta.id
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obj_name = getattr(hit, "name", None) or getattr(hit, "title", None) or "N/A"
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obj_slug = ""
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raw_slug = getattr(hit, "slug", None)
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if raw_slug:
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obj_slug = raw_slug
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elif hit.meta.index == "brands":
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obj_slug = slugify(obj_name)
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elif hit.meta.index == "categories":
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obj_slug = slugify(f"{obj_name}")
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image_url = None
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idx = hit.meta.index
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if idx == "products" and request:
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prod = get_object_or_404(Product, uuid=obj_uuid)
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first = prod.images.order_by("priority").first()
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if first and first.image:
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image_url = request.build_absolute_uri(first.image.url)
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elif idx == "brands" and request:
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brand = get_object_or_404(Brand, uuid=obj_uuid)
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if brand.small_logo:
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image_url = request.build_absolute_uri(brand.small_logo.url)
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elif idx == "categories" and request:
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cat = get_object_or_404(Category, uuid=obj_uuid)
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if cat.image:
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image_url = request.build_absolute_uri(cat.image.url)
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results[idx].append(
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{
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"uuid": str(obj_uuid),
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"name": obj_name,
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"slug": obj_slug,
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"image": image_url,
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}
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)
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facets = {
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"categories": [
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(b.key, b.doc_count) for b in response.aggregations.by_category.buckets
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],
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"brands": [
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(b.key, b.doc_count) for b in response.aggregations.by_brand.buckets
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],
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"price": {
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"min": response.aggregations.price_stats.min,
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"max": response.aggregations.price_stats.max,
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"avg": response.aggregations.price_stats.avg,
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},
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}
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return {"results": results, "facets": facets}
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LANGUAGE_ANALYZER_MAP = {
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"ar": "arabic",
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"cs": "czech",
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"da": "danish",
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"de": "german",
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"en": "english",
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"es": "spanish",
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"fr": "french",
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"hi": "hindi",
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"it": "italian",
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"ja": "standard",
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"kk": "standard",
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"nl": "dutch",
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"pl": "standard",
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"pt": "portuguese",
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"ro": "romanian",
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"ru": "russian",
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"zh": "standard",
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}
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def _lang_analyzer(lang_code: str) -> str:
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"""Return the best‑guess ES analyzer for an ISO language code."""
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base = lang_code.split("-")[0].lower()
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return LANGUAGE_ANALYZER_MAP.get(base, "standard")
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class ActiveOnlyMixin:
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"""QuerySet & indexing helpers, so only *active* objects are indexed."""
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def get_queryset(self):
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return super().get_queryset().filter(is_active=True)
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def should_index_object(self, obj):
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return getattr(obj, "is_active", False)
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COMMON_ANALYSIS = {
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"filter": {
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"edge_ngram_filter": {"type": "edge_ngram", "min_gram": 1, "max_gram": 20},
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"ngram_filter": {"type": "ngram", "min_gram": 2, "max_gram": 20},
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"double_metaphone": {
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"type": "phonetic",
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"encoder": "double_metaphone",
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"replace": False,
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},
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"english_stop": {"type": "stop", "stopwords": "_english_"},
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},
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"analyzer": {
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"autocomplete": {
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"tokenizer": "standard",
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"filter": ["lowercase", "asciifolding", "edge_ngram_filter"],
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},
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"autocomplete_search": {
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"tokenizer": "standard",
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"filter": ["lowercase", "asciifolding"],
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},
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"name_ngram": {
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"tokenizer": "standard",
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"filter": ["lowercase", "asciifolding", "ngram_filter"],
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},
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"name_phonetic": {
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"tokenizer": "standard",
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"filter": ["lowercase", "asciifolding", "double_metaphone"],
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},
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"query_lc": {"tokenizer": "standard", "filter": ["lowercase", "asciifolding"]},
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},
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}
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def _add_multilang_fields(cls):
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"""
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Dynamically add multilingual name/description fields and prepare methods to guard against None.
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"""
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for code, _lang in settings.LANGUAGES:
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lc = code.replace("-", "_").lower()
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# name_{lc}
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name_field = f"name_{lc}"
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setattr(
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cls,
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name_field,
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fields.TextField(
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attr=name_field,
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analyzer=_lang_analyzer(code),
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copy_to="name",
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fields={
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"raw": fields.KeywordField(ignore_above=256),
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"ngram": fields.TextField(
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analyzer="name_ngram", search_analyzer="query_lc"
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),
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"phonetic": fields.TextField(analyzer="name_phonetic"),
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},
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),
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)
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# prepare_name_{lc} to ensure no None values
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def make_prepare(attr):
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return lambda self, instance: getattr(instance, attr, "") or ""
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setattr(cls, f"prepare_{name_field}", make_prepare(name_field))
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# description_{lc}
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desc_field = f"description_{lc}"
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setattr(
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cls,
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desc_field,
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fields.TextField(
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attr=desc_field,
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analyzer=_lang_analyzer(code),
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copy_to="description",
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fields={
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"raw": fields.KeywordField(ignore_above=256),
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"ngram": fields.TextField(
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analyzer="name_ngram", search_analyzer="query_lc"
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),
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"phonetic": fields.TextField(analyzer="name_phonetic"),
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},
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),
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)
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setattr(cls, f"prepare_{desc_field}", make_prepare(desc_field))
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def populate_index():
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for doc in registry.get_documents(set(registry.get_models())):
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qs = doc().get_indexing_queryset()
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doc().update(qs, parallel=True, refresh=True)
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