Features: 1) Add top_level field to documents with preparation logic; 2) Add price field to documents with preparation logic; 3) Introduce score boosting with function_score using sales_rank field;

Fixes: 1) None;

Extra: 1) Replace direct query with boosted query in Elasticsearch `search` logic; 2) Minor refactoring for improved readability;

core/elasticsearch/__init__.py

@@ -65,9 +65,31 @@ def process_query(query: str = "", request: Request | None = None):
             minimum_should_match=1,
         )
 
+        functions = [
+            {
+                "gauss": {
+                    "sales_rank": {
+                        "origin": 100,
+                        "scale": 500,
+                        "offset": 0,
+                        "decay": 0.3,
+                    }
+                },
+                "weight": 3,
+            },
+        ]
+
+        boosted = Q(
+            "function_score",
+            query=q,
+            boost_mode="sum",
+            score_mode="sum",
+            functions=functions,
+        )
+
         search = (
             Search(index=["products", "categories", "brands", "posts"])
-            .query(q)
+            .query(boosted)
             .extra(size=100)
         )
         response = search.execute()

core/elasticsearch/documents.py

@@ -56,6 +56,15 @@ class ProductDocument(_BaseDoc):
     rating = fields.FloatField(attr="rating")
     product_type = fields.KeywordField()
     sales_rank = fields.IntegerField()
+    top_level = fields.KeywordField()
+    price = fields.FloatField()
+
+    def prepare_price(self, obj):
+        return float(obj.price) if obj.price else 0.0
+
+    def prepare_top_level(self, obj):
+        root = obj.category.get_root() if obj.category else None
+        return root.slug if root else "other"
 
     def prepare_product_type(self, obj):
         return obj.category.slug.split("-")[0] if obj.category else "other"