Features: 1) Add suggest, price, and quantity fields to ProductDocument; 2) Introduce prepare_suggest method for enhancing autocomplete functionality; 3) Enhance search with filters, function scoring, and result aggregation; 4) Add new analyzers and token filters for synonyms and stopwords.

Fixes: 1) Remove unused imports in `core/elasticsearch/__init__.py`.

Extra: Refactor `process_query` for improved readability and functionality; update aggregation and result processing logic; reformat and clean up code.

core/elasticsearch/__init__.py

@@ -1,12 +1,9 @@
 from django.conf import settings
-from django.http import Http404
 from django.shortcuts import get_object_or_404
 from django.utils.text import slugify
-from django.utils.translation import gettext_lazy as _
 from django_elasticsearch_dsl import fields
 from django_elasticsearch_dsl.registries import registry
-from elasticsearch import NotFoundError
+from elasticsearch.dsl import SF, Q, Search
-from elasticsearch.dsl import Q, Search
 from rest_framework.request import Request
 
 from core.models import Brand, Category, Product
@@ -33,19 +30,13 @@ SMART_FIELDS = [
 ]
 
 
-def process_query(query: str = "", request: Request | None = None):
+def process_query(
-    """
+    query: str = "", request: Request | None = None, filters: dict | None = None
-    Perform a lenient, typo‑tolerant, multi‑index search.
+):
-
-    * Full‑text with fuzziness for spelling mistakes
-    * `bool_prefix` for edge‑ngram autocomplete / “icontains”
-    """
     if not query:
-        raise ValueError(_("no search term provided."))
+        raise ValueError("no search term provided.")
-
+    filters = filters or {}
-    query = query.strip()
+    base_q = Q(
-    try:
-        q = Q(
         "bool",
         should=[
             Q(
@@ -64,20 +55,46 @@ def process_query(query: str = "", request: Request | None = None):
         ],
         minimum_should_match=1,
     )
+    functions = [
+        SF("field_value_factor", field="quantity", modifier="log1p", missing=0),
+        SF("field_value_factor", field="rating", modifier="sqrt", missing=0),
+        SF("exp", field="created_at", origin="now", scale="30d"),  # newness boost
+    ]
+    fq = [
+        Q("function_score", query=base_q, functions=functions, boost_mode="sum"),
+    ]
+    if "category" in filters:
+        fq.append(Q("term", category__slug=filters["category"]))
+    if "brand" in filters:
+        fq.append(Q("term", brand__slug=filters["brand"]))
+    if "price_min" in filters or "price_max" in filters:
+        range_q = {}
+        if "price_min" in filters:
+            range_q["gte"] = filters["price_min"]
+        if "price_max" in filters:
+            range_q["lte"] = filters["price_max"]
+        fq.append(Q("range", price=range_q))
 
     search = (
         Search(index=["products", "categories", "brands", "posts"])
-            .query(q)
+        .query(base_q)
-            .extra(size=100)
+        .filter(*fq)
+        .extra(size=20)
+        .highlight("description", fragment_size=150)
+        .extra(
+            aggs={
+                "by_category": {"terms": {"field": "category.keyword"}},
+                "by_brand": {"terms": {"field": "brand.keyword"}},
+                "price_stats": {"stats": {"field": "price"}},
+            }
+        )
     )
     response = search.execute()
 
     results: dict = {"products": [], "categories": [], "brands": [], "posts": []}
     for hit in response.hits:
         obj_uuid = getattr(hit, "uuid", None) or hit.meta.id
-            obj_name = (
+        obj_name = getattr(hit, "name", None) or getattr(hit, "title", None) or "N/A"
-                getattr(hit, "name", None) or getattr(hit, "title", None) or "N/A"
-            )
         obj_slug = ""
         raw_slug = getattr(hit, "slug", None)
         if raw_slug:
@@ -112,9 +129,21 @@ def process_query(query: str = "", request: Request | None = None):
             }
         )
 
-        return results
+    facets = {
-    except NotFoundError:
+        "categories": [
-        raise Http404
+            (b.key, b.doc_count) for b in response.aggregations.by_category.buckets
+        ],
+        "brands": [
+            (b.key, b.doc_count) for b in response.aggregations.by_brand.buckets
+        ],
+        "price": {
+            "min": response.aggregations.price_stats.min,
+            "max": response.aggregations.price_stats.max,
+            "avg": response.aggregations.price_stats.avg,
+        },
+    }
+
+    return {"results": results, "facets": facets}
 
 
 LANGUAGE_ANALYZER_MAP = {
@@ -163,6 +192,8 @@ COMMON_ANALYSIS = {
             "encoder": "double_metaphone",
             "replace": False,
         },
+        "synonym_filter": {"type": "synonym", "synonyms_path": "analysis/synonyms.txt"},
+        "english_stop": {"type": "stop", "stopwords": "_english_"},
     },
     "analyzer": {
         "autocomplete": {
@@ -177,6 +208,10 @@ COMMON_ANALYSIS = {
             "tokenizer": "standard",
             "filter": ["lowercase", "asciifolding", "ngram_filter"],
         },
+        "synonym_analyzer": {
+            "tokenizer": "standard",
+            "filter": ["lowercase", "asciifolding", "synonym_filter"],
+        },
         "name_phonetic": {
             "tokenizer": "standard",
             "filter": ["lowercase", "asciifolding", "double_metaphone"],

core/elasticsearch/documents.py

@@ -52,6 +52,12 @@ class _BaseDoc(ActiveOnlyMixin, Document):
 
 
 class ProductDocument(_BaseDoc):
+    suggest = fields.CompletionField(
+        analyzer="autocomplete",
+        search_analyzer="autocomplete_search",
+    )
+    price = fields.FloatField(attr="price")
+    quantity = fields.IntegerField(attr="quantity")
     rating = fields.FloatField(attr="rating")
 
     class Index(_BaseDoc.Index):
@@ -61,6 +67,13 @@ class ProductDocument(_BaseDoc):
         model = Product
         fields = ["uuid"]
 
+    def prepare_suggest(self, instance):
+        terms = [instance.name]
+        if instance.brand:
+            terms.append(instance.brand.name)
+        terms.append(instance.category.name)
+        return {"input": terms, "weight": int(instance.quantity)}
+
 
 _add_multilang_fields(ProductDocument)
 registry.register_document(ProductDocument)