diff --git a/docs/examples/Hybrid_Search.ipynb b/docs/examples/Hybrid_Search.ipynb
index 2defeb24..14a9a832 100644
--- a/docs/examples/Hybrid_Search.ipynb
+++ b/docs/examples/Hybrid_Search.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:46:59.293555Z",
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "'0.2.5'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:06.815264Z",
@@ -113,7 +113,7 @@
        "})"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:06.831216Z",
@@ -321,7 +321,7 @@
        "5  Panasonic FV-08VRE2 Ventilation Fan with Reces...  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:06.842492Z",
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:06.843214Z",
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:06.843675Z",
@@ -386,7 +386,7 @@
        "176"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -404,14 +404,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:08.983795Z",
      "start_time": "2024-03-30T00:47:06.839334Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "350b127222dc4ea1bb13183b0b424868",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 9 files:   0%|          | 0/9 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2a543cab92a42fb801b9842ad69b0c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 7 files:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sparse_model_name = \"prithvida/Splade_PP_en_v1\"\n",
     "dense_model_name = \"BAAI/bge-large-en-v1.5\"\n",
@@ -422,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:09.018037Z",
@@ -450,14 +479,14 @@
        "        13850, 16602, 23924]))]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def make_sparse_embedding(texts: List[str]):\n",
-    "    return list(sparse_model.embed(texts))\n",
+    "    return list(sparse_model.embed(texts, batch_size=32))\n",
     "\n",
     "\n",
     "sparse_embedding: List[SparseEmbedding] = make_sparse_embedding([\"Fastembed is a great library for text embeddings!\"])\n",
@@ -486,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:47:12.171596Z",
@@ -509,7 +538,7 @@
        "  'sources': {'hf': 'Qdrant/SPLADE_PP_en_v1'}}]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -616,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:11.018700Z",
@@ -634,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:12.418869Z",
@@ -648,7 +677,7 @@
        "(1024,)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -659,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:13.789029Z",
@@ -673,9 +702,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6min 15s, sys: 44.3 s, total: 6min 59s\n",
+      "Wall time: 3min 6s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "df[\"sparse_embedding\"] = make_sparse_embedding(product_texts)"
@@ -685,12 +723,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that FastEmbed uses data parallelism to speed up the embedding generation process. This improves throughput and reduces the time it takes to generate embeddings for large datasets. For our small dataset here, it reduces the time from 5 min 15 seconds to 1 min 44 seconds, or about 3x faster."
+    "Notice that FastEmbed uses data parallelism to speed up the embedding generation process. \n",
+    "\n",
+    "This improves throughput and reduces the time it takes to generate embeddings for large datasets. \n",
+    "\n",
+    "For our small dataset here, on my local machine -- it reduces the time from user's 6 min 15 seconds to a wall time of about 3 min 6 seconds, or about 2x faster. This is a function of the number of CPU cores available on the machine, CPU usage and other factors -- so your mileage may vary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -710,7 +752,7 @@
        "Name: sparse_embedding, Length: 176, dtype: object"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -721,9 +763,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15min 46s, sys: 4.48 s, total: 15min 50s\n",
+      "Wall time: 2min 46s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "df[\"dense_embedding\"] = make_dense_embedding(product_texts)"
@@ -738,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:19.041998Z",
@@ -754,12 +805,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Qdrant is a vector similarity search engine that allows you to index and search high-dimensional vectors. It supports both sparse and dense embeddings, and it's a great tool for building search engines. Here, we use the memory mode which is Numpy under the hood for demonstration purposes. In production, you can use the Docker or Cloud for full DB support."
+    "### About Qdrant\n",
+    "\n",
+    "Qdrant is a vector similarity search engine that allows you to index and search high-dimensional vectors. It supports both sparse and dense embeddings, and it's a great tool for building search engines. \n",
+    "\n",
+    "Here, we use the memory mode which is Numpy under the hood for demonstration purposes. In production, you can use the Docker or Cloud for full DB support."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:23.670884Z",
@@ -773,7 +828,7 @@
        "True"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -800,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:26.252861Z",
@@ -834,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -843,7 +898,7 @@
        "UpdateResult(operation_id=0, status=<UpdateStatus.COMPLETED: 'completed'>)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:48.298878Z",
@@ -923,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:48:53.075137Z",
@@ -934,14 +989,14 @@
     {
      "data": {
       "text/plain": [
-       "[('B', 0.033465871107430434),\n",
-       " ('A', 0.033465871107430434),\n",
+       "[('A', 0.033465871107430434),\n",
+       " ('B', 0.033465871107430434),\n",
        " ('D', 0.03320985472238179),\n",
        " ('C', 0.03294544435749548),\n",
        " ('E', 0.01775980832584606)]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1014,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1023,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +1097,7 @@
        " (4, 0.01564927857935627)]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,7 +1108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1072,7 +1127,7 @@
        " Record(id=4, payload={'text': 'Panasonic FV-08VRE2 Ventilation Fan with Recessed LED (Renewed)\\nPanasonic FV-08VRE2 Ventilation Fan with Recessed LED (Renewed)\\nPanasonic\\nWhite\\nNone\\nThe design solution for Fan/light combinations\\nEnergy Star rated architectural grade recessed Fan/LED light\\nQuiet, energy efficient and powerful 80 CFM ventilation hidden above the Ceiling\\nLED lamp is dimmable\\nBeautiful Lighting with 6-1/2”aperture and advanced luminaire design\\nThe design solution for Fan/light combinations\\nEnergy Star rated architectural grade recessed Fan/LED light\\nQuiet, energy efficient and powerful 80 CFM ventilation hidden above the Ceiling\\nLED lamp is dimmable\\nBeautiful Lighting with 6-1/2”aperture and advanced luminaire design', 'product_id': 'B07QJ7WYFQ'}, vector=None, shard_key=None)]"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1094,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1132,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1141,7 +1196,7 @@
        "11"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1176,7 +1231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,