diff --git a/vacancies/main/vector_store.py b/vacancies/main/vector_store.py
index 25ee98c..2a736dd 100644
--- a/vacancies/main/vector_store.py
+++ b/vacancies/main/vector_store.py
@@ -5,14 +5,29 @@ from qdrant_client import QdrantClient
 from qdrant_client.models import Filter
 from vacancies.main.models import VacancyFeatures
 
-client = QdrantClient(path="./embeddings")
-#client = QdrantClient(url="http://localhost:6333")
+# client = QdrantClient(path="./embeddings")
+client = QdrantClient(url="http://localhost:6333")
 
 FEATURE_NAMES = [
     "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
     "location", "salary_range", "languages", "education", "schedule", "additional_requirements"
 ]
 
+weights = {
+    "employment_type": 2,
+    "work_format": 2,
+    "experience": 4,
+    "position_level": 4,
+    "industry": 4,
+    "tech_stack": 5,
+    "location": 2,
+    "salary_range": 2,
+    "languages": 2,
+    "education": 1,
+    "schedule": 1,
+    "additional_requirements": 1,
+}
+
 vectors_config = {
     name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
 }
@@ -90,7 +105,7 @@ def search_similarities(query_filter: Filter, cv_id: int) -> list[dict]:
 
     scored = []
     for vid, feature_sims in max_similarities.items():
-        total = sum(feature_sims.values())
+        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
         scored.append({"id": vid, "score": total})
 
     scored.sort(key=lambda x: x["score"], reverse=True)