diff --git a/backend/open_webui/apps/retrieval/vector/dbs/qdrant.py b/backend/open_webui/apps/retrieval/vector/dbs/qdrant.py
index 6ef00fff0..a0e93a843 100644
--- a/backend/open_webui/apps/retrieval/vector/dbs/qdrant.py
+++ b/backend/open_webui/apps/retrieval/vector/dbs/qdrant.py
@@ -1,4 +1,3 @@
-import logging
 from typing import Optional
 
 from qdrant_client import QdrantClient as Qclient
@@ -8,10 +7,6 @@ from qdrant_client.models import models
 from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
 from open_webui.config import QDRANT_URI
 
-log = logging.getLogger(__name__)
-log.setLevel("INFO")
-
-
 class QdrantClient:
     def __init__(self):
         self.collection_prefix = "open-webui"
@@ -44,7 +39,7 @@ class QdrantClient:
             vectors_config=models.VectorParams(size=dimension, distance=models.Distance.COSINE),
         )
 
-        log.info(f"collection {collection_name_with_prefix} successfully created!")
+        print(f"collection {collection_name_with_prefix} successfully created!")
 
     def _create_collection_if_not_exists(self, collection_name, dimension):
         if not self.has_collection(
@@ -65,7 +60,6 @@ class QdrantClient:
     ) -> Optional[SearchResult]:
         # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
 
-        log.info("start search...")
         query_response = self.client.query_points(
             collection_name=f"{self.collection_prefix}_{collection_name}",
             query=vectors[0],
@@ -90,7 +84,6 @@ class QdrantClient:
                 field_conditions.append(
                     models.FieldCondition(key=f"metadata.{key}", match=models.MatchValue(value=value)))
 
-            log.info("start search...")
             points = self.client.query_points(
                 collection_name=f"{self.collection_prefix}_{collection_name}",
                 query_filter=models.Filter(should=field_conditions),
@@ -164,15 +157,16 @@ class QdrantClient:
                 self.client.delete_collection(collection_name=collection_name.name)
 
     def create_points(self, items: list[VectorItem]):
-        vectors = [item["vector"] for item in items]
-        log.info("insert points...")
         points = []
         for idx, item in enumerate(items):
             points.append(
                 PointStruct(
                     id=item["id"],
-                    vector=vectors[idx],
-                    payload={"text": item["text"], "metadata": item["metadata"]},
+                    vector=item["vector"],
+                    payload={
+                        "text": item["text"],
+                        "metadata": item["metadata"]
+                    },
                 )
             )
         return points