diff --git a/examples/vector_databases/qdrant/Using_Qdrant_for_embeddings_search.ipynb b/examples/vector_databases/qdrant/Using_Qdrant_for_embeddings_search.ipynb
index ffb598d24a..caab9c4830 100644
--- a/examples/vector_databases/qdrant/Using_Qdrant_for_embeddings_search.ipynb
+++ b/examples/vector_databases/qdrant/Using_Qdrant_for_embeddings_search.ipynb
@@ -109,7 +109,7 @@
     {
      "data": {
       "text/plain": [
-       "'vector_database_wikipedia_articles_embedded (10).zip'"
+       "'vector_database_wikipedia_articles_embedded.zip'"
       ]
      },
      "execution_count": 3,
@@ -508,46 +508,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "9f39a8c395554ca3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-05-21T23:56:21.577594Z",
-     "start_time": "2024-05-21T23:56:21.460740Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_size = len(article_df['content_vector'][0])\n",
-    "\n",
-    "qdrant.recreate_collection(\n",
-    "    collection_name='Articles',\n",
-    "    vectors_config={\n",
-    "        'title': rest.VectorParams(\n",
-    "            distance=rest.Distance.COSINE,\n",
-    "            size=vector_size,\n",
-    "        ),\n",
-    "        'content': rest.VectorParams(\n",
-    "            distance=rest.Distance.COSINE,\n",
-    "            size=vector_size,\n",
-    "        ),\n",
-    "    }\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e95be6e0c9af4c21",
@@ -571,13 +531,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Upserting articles: 100%|█████████████████████████████████████████████████████████████████████████████████████| 25000/25000 [02:52<00:00, 144.82it/s]\n"
+      "Upserting articles: 100%|██████████| 25000/25000 [06:50<00:00, 60.93it/s]\n"
      ]
     }
    ],
    "source": [
     "from qdrant_client.models import PointStruct # Import the PointStruct to store the vector and payload\n",
-    "from tqdm import tqdm # Library to show the progress bar \n",
+    "from tqdm import tqdm # Library to show the progress bar\n",
     "\n",
     "# Populate collection with vectors using tqdm to show progress\n",
     "for k, v in tqdm(article_df.iterrows(), desc=\"Upserting articles\", total=len(article_df)):\n",
@@ -587,7 +547,7 @@
     "            points=[\n",
     "                PointStruct(\n",
     "                    id=k,\n",
-    "                    vector={'title': v['title_vector'], \n",
+    "                    vector={'title': v['title_vector'],\n",
     "                            'content': v['content_vector']},\n",
     "                    payload={\n",
     "                        'id': v['id'],\n",
@@ -652,22 +612,21 @@
    "outputs": [],
    "source": [
     "def query_qdrant(query, collection_name, vector_name='title', top_k=20):\n",
-    "\n",
     "    # Creates embedding vector from user query\n",
     "    embedded_query = openai.embeddings.create(\n",
     "        input=query,\n",
     "        model=EMBEDDING_MODEL,\n",
     "    ).data[0].embedding # We take the first embedding from the list\n",
-    "    \n",
+    "\n",
     "    query_results = qdrant.search(\n",
     "        collection_name=collection_name,\n",
     "        query_vector=(\n",
     "            vector_name, embedded_query\n",
     "        ),\n",
-    "        limit=top_k, \n",
+    "        limit=top_k,\n",
     "        query_filter=None\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    return query_results"
    ]
   },
@@ -777,12 +736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "fd16a328ca3d68029457069b79cb0b38eb39a0f5ccc4fe4473d3047707df8207"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,