diff --git a/tutorials/44_Creating_Custom_SuperComponents.ipynb b/tutorials/44_Creating_Custom_SuperComponents.ipynb
index 092a0ed..c3f27b0 100644
--- a/tutorials/44_Creating_Custom_SuperComponents.ipynb
+++ b/tutorials/44_Creating_Custom_SuperComponents.ipynb
@@ -47,17 +47,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "id": "UQbU8GUfO-qZ"
    },
-   "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install haystack-ai\n",
     "pip install \"sentence-transformers>=4.1.0\" datasets \"transformers[torch,sentencepiece]<5\""
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -96,11 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "metadata": {
     "id": "XvLVaFHTO-qb"
    },
-   "outputs": [],
    "source": [
     "from haystack import Document, Pipeline, super_component\n",
     "from haystack.components.joiners import DocumentJoiner\n",
@@ -131,7 +129,9 @@
     "        self.pipeline.connect(\"text_embedder\", \"embedding_retriever\")\n",
     "        self.pipeline.connect(\"bm25_retriever\", \"document_joiner\")\n",
     "        self.pipeline.connect(\"embedding_retriever\", \"document_joiner\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -144,11 +144,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
-    "id": "aNzUi4iz0FAU"
+    "id": "aNzUi4iz0FAU",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T08:59:55.715295Z",
+     "start_time": "2026-05-26T08:59:25.735412Z"
+    }
    },
-   "outputs": [],
    "source": [
     "# Load a dataset\n",
     "dataset = load_dataset(\"HaystackBot/medrag-pubmed-chunk-with-embeddings\", split=\"train\")\n",
@@ -162,18 +164,40 @@
     "result = retriever.run(\n",
     "    text=query, query=query\n",
     ")  # `query` variable will match with `text` and `query` inputs of components in the pipeline."
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating train split: 100%|██████████| 15377/15377 [00:00<00:00, 159479.28 examples/s]\n",
+      "The `tokenizer_kwargs` argument was renamed and is now deprecated. Please use `processor_kwargs` instead.\n",
+      "Batches: 100%|██████████| 1/1 [00:01<00:00,  1.04s/it]\n"
+     ]
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "ZoTmeqV_j-OI",
-    "outputId": "7fa55a12-563c-4341-bbaa-3c578cc5e976"
+    "outputId": "7fa55a12-563c-4341-bbaa-3c578cc5e976",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T08:59:55.778626Z",
+     "start_time": "2026-05-26T08:59:55.738878Z"
+    }
    },
+   "source": [
+    "# Print the results\n",
+    "print(f\"Found {len(result['documents'])} documents\")\n",
+    "for i, doc in enumerate(result[\"documents\"][:3]):  # Show first 3 documents\n",
+    "    print(f\"\\nDocument {i+1} (Score: {doc.score:.4f}):\")\n",
+    "    print(doc.content[:200] + \"...\")"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -192,13 +216,7 @@
      ]
     }
    ],
-   "source": [
-    "# Print the results\n",
-    "print(f\"Found {len(result['documents'])} documents\")\n",
-    "for i, doc in enumerate(result[\"documents\"][:3]):  # Show first 3 documents\n",
-    "    print(f\"\\nDocument {i+1} (Score: {doc.score:.4f}):\")\n",
-    "    print(doc.content[:200] + \"...\")"
-   ]
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -236,11 +254,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
-    "id": "INdC3WvLO-qb"
+    "id": "INdC3WvLO-qb",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T08:59:55.806030Z",
+     "start_time": "2026-05-26T08:59:55.782346Z"
+    }
    },
-   "outputs": [],
    "source": [
     "from haystack import Document, Pipeline, super_component\n",
     "from haystack.components.joiners import DocumentJoiner\n",
@@ -283,11 +303,12 @@
     "\n",
     "        # Define input mapping\n",
     "        self.input_mapping = {\"query\": [\"text_embedder.text\", \"bm25_retriever.query\", \"ranker.query\"]}"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 5
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -373,106 +394,31 @@
      ]
     },
     "id": "yxaN3KBo65pv",
-    "outputId": "21486dba-7914-4349-a579-2993ec212d86"
+    "outputId": "21486dba-7914-4349-a579-2993ec212d86",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T09:01:35.627813Z",
+     "start_time": "2026-05-26T08:59:55.807152Z"
+    }
    },
+   "source": [
+    "# Create and run the HybridRetrieverWithRanker\n",
+    "retriever = HybridRetrieverWithRanker(document_store)\n",
+    "result = retriever.run(query=query)  # instead of retriever.run(text=query, query=query) thanks to input_mapping\n",
+    "\n",
+    "# Print the results\n",
+    "print(f\"Found {len(result['documents'])} documents\")\n",
+    "for i, doc in enumerate(result[\"documents\"][:3]):  # Show first 3 documents\n",
+    "    print(f\"\\nDocument {i+1} (Score: {doc.score:.4f}):\")\n",
+    "    print(doc.content[:200] + \"...\")"
+   ],
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4246cbc7b0b4f7784293fbd8337befe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/799 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f41d46b53c454c6d886acb02ad054cda",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "165ba79a9c1a465b829b16643b485e54",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c511266135c740e483df076e2812c139",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "858d7e87d43f4f6c9c5ce83488b160b8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "573b84602f75411f801c7a69509754d2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "special_tokens_map.json:   0%|          | 0.00/279 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59c201c7fd8b4e04b23246db1bc7061a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TransformersSimilarityRanker is considered legacy and will no longer receive updates. It may be deprecated in a future release, with removal following after a deprecation period. Consider using SentenceTransformersSimilarityRanker instead, which provides the same functionality along with additional features.\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 13.00it/s]\n"
+     ]
     },
     {
      "name": "stdout",
@@ -491,17 +437,7 @@
      ]
     }
    ],
-   "source": [
-    "# Create and run the HybridRetrieverWithRanker\n",
-    "retriever = HybridRetrieverWithRanker(document_store)\n",
-    "result = retriever.run(query=query)  # instead of retriever.run(text=query, query=query) thanks to input_mapping\n",
-    "\n",
-    "# Print the results\n",
-    "print(f\"Found {len(result['documents'])} documents\")\n",
-    "for i, doc in enumerate(result[\"documents\"][:3]):  # Show first 3 documents\n",
-    "    print(f\"\\nDocument {i+1} (Score: {doc.score:.4f}):\")\n",
-    "    print(doc.content[:200] + \"...\")"
-   ]
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -534,40 +470,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 420
     },
     "id": "chne3yLKXgEe",
-    "outputId": "4fa8cd47-049b-4baa-bcb3-1b1f6f3e6ef8"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Serialized component:\n",
-      "{'type': 'haystack.components.preprocessors.document_preprocessor.DocumentPreprocessor', 'init_parameters': {'remove_empty_lines': True, 'remove_extra_whitespaces': True, 'remove_repeated_substrings': False, 'keep_id': False, 'remove_substrings': None, 'remove_regex': None, 'unicode_normalization': None, 'ascii_only': False, 'split_by': 'word', 'split_length': 250, 'split_overlap': 0, 'split_threshold': 0, 'splitting_function': None, 'respect_sentence_boundary': False, 'language': 'en', 'use_split_rules': True, 'extend_abbreviations': True}}\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'NoneType' object has no attribute 'split'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-f8ba0e3526c3>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Deserialize the component from the dictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mdeserialized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomponent_from_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDocumentPreprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserialized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"document_preprocessor\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nDeserialized component:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeserialized\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/haystack/core/serialization.py\u001b[0m in \u001b[0;36mcomponent_from_dict\u001b[0;34m(cls, data, name, callbacks)\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_pre_init\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdo_from_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_hook_component_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomponent_pre_init_callback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/haystack/core/serialization.py\u001b[0m in \u001b[0;36mdo_from_dict\u001b[0;34m()\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_from_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_dict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdefault_from_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/haystack/components/preprocessors/document_preprocessor.py\u001b[0m in \u001b[0;36mfrom_dict\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    188\u001b[0m         \"\"\"\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"splitting_function\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"init_parameters\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m             data[\"init_parameters\"][\"splitting_function\"] = deserialize_callable(\n\u001b[0m\u001b[1;32m    191\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"init_parameters\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"splitting_function\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             )\n",
-      "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/haystack/utils/callable_serialization.py\u001b[0m in \u001b[0;36mdeserialize_callable\u001b[0;34m(callable_handle)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;34m:\u001b[0m\u001b[0mraises\u001b[0m \u001b[0mDeserializationError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcallable\u001b[0m \u001b[0mcannot\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \"\"\"\n\u001b[0;32m---> 53\u001b[0;31m     \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallable_handle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
-     ]
+    "outputId": "4fa8cd47-049b-4baa-bcb3-1b1f6f3e6ef8",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T09:01:35.682709Z",
+     "start_time": "2026-05-26T09:01:35.651336Z"
     }
-   ],
+   },
    "source": [
     "from haystack.core.serialization import component_to_dict, component_from_dict\n",
     "from haystack.components.preprocessors import DocumentPreprocessor\n",
@@ -589,7 +503,27 @@
     "doc = Document(content=\"I love pizza!\")\n",
     "result = deserialized.run(documents=[doc])\n",
     "print(f\"\\nDeserialized component produced {len(result['documents'])} documents\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Serialized component:\n",
+      "{'type': 'haystack.components.preprocessors.document_preprocessor.DocumentPreprocessor', 'init_parameters': {'remove_empty_lines': True, 'remove_extra_whitespaces': True, 'remove_repeated_substrings': False, 'keep_id': False, 'remove_substrings': None, 'remove_regex': None, 'unicode_normalization': None, 'ascii_only': False, 'split_by': 'word', 'split_length': 250, 'split_overlap': 0, 'split_threshold': 0, 'splitting_function': None, 'respect_sentence_boundary': False, 'language': 'en', 'use_split_rules': True, 'extend_abbreviations': True}}\n",
+      "\n",
+      "Deserialized component:\n",
+      "<haystack.components.preprocessors.document_preprocessor.DocumentPreprocessor object at 0x152c61a30>\n",
+      "Inputs:\n",
+      "  - documents: list[Document]\n",
+      "Outputs:\n",
+      "  - documents: list[Document]\n",
+      "\n",
+      "Deserialized component produced 1 documents\n"
+     ]
+    }
+   ],
+   "execution_count": 7
   },
   {
    "cell_type": "markdown",
@@ -619,11 +553,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
-    "id": "HryYZP9ZO-qc"
+    "id": "HryYZP9ZO-qc",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T09:01:35.692681Z",
+     "start_time": "2026-05-26T09:01:35.685324Z"
+    }
    },
-   "outputs": [],
    "source": [
     "from haystack import Document, Pipeline, super_component\n",
     "from haystack.components.joiners import DocumentJoiner\n",
@@ -673,11 +609,12 @@
     "            \"ranker.documents\": \"ranked_documents\",\n",
     "            \"text_embedder.embedding\": \"query_embedding\",\n",
     "        }"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 8
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -697,22 +634,45 @@
      ]
     },
     "id": "INdC3WvLO-qc",
-    "outputId": "a9d0a257-6969-49bd-f141-7d3d4fe24813"
+    "outputId": "a9d0a257-6969-49bd-f141-7d3d4fe24813",
+    "ExecuteTime": {
+     "end_time": "2026-05-26T09:01:39.167534Z",
+     "start_time": "2026-05-26T09:01:35.694539Z"
+    }
    },
+   "source": [
+    "# Create and run the AdvancedHybridRetriever\n",
+    "retriever = AdvancedHybridRetriever(document_store)\n",
+    "result = retriever.run(query=query)\n",
+    "\n",
+    "# Print the results\n",
+    "print(f\"BM25 documents: {len(result['bm25_documents'])}\")\n",
+    "print(f\"Embedding documents: {len(result['embedding_documents'])}\")\n",
+    "print(f\"Joined documents: {len(result['joined_documents'])}\")\n",
+    "print(f\"Ranked documents: {len(result['ranked_documents'])}\")\n",
+    "print(f\"Query embedding shape: {len(result['query_embedding'])}\")\n",
+    "\n",
+    "# Compare the top document from each stage\n",
+    "print(\"\\nTop BM25 document:\")\n",
+    "print(result[\"bm25_documents\"][0].content[:200] + \"...\")\n",
+    "print(f\"Score: {result['bm25_documents'][0].score:.4f}\")\n",
+    "\n",
+    "print(\"\\nTop embedding document:\")\n",
+    "print(result[\"embedding_documents\"][0].content[:200] + \"...\")\n",
+    "print(f\"Score: {result['embedding_documents'][0].score:.4f}\")\n",
+    "\n",
+    "print(\"\\nTop ranked document:\")\n",
+    "print(result[\"ranked_documents\"][0].content[:200] + \"...\")\n",
+    "print(f\"Score: {result['ranked_documents'][0].score:.4f}\")"
+   ],
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c63ba6f43574419b66fe173a763b04a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TransformersSimilarityRanker is considered legacy and will no longer receive updates. It may be deprecated in a future release, with removal following after a deprecation period. Consider using SentenceTransformersSimilarityRanker instead, which provides the same functionality along with additional features.\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  7.29it/s]\n"
+     ]
     },
     {
      "name": "stdout",
@@ -726,7 +686,7 @@
       "\n",
       "Top BM25 document:\n",
       "Type-specific pneumococcal respiratory disease in the elderly and chronically III. Type-specific pneumococcal respiratory disease was studied in a chronic-disease hospital during a 27-month period. Is...\n",
-      "Score: 32.3482\n",
+      "Score: 32.4112\n",
       "\n",
       "Top embedding document:\n",
       "[Proceedings: Long-term therapy with antibiotics in chronic bronchitis]. Longterm therapy of chronic bacterial bronchitis assumes two forms: (a) therapy of acute exacerbations, and (b) continuous long...\n",
@@ -738,31 +698,7 @@
      ]
     }
    ],
-   "source": [
-    "# Create and run the AdvancedHybridRetriever\n",
-    "retriever = AdvancedHybridRetriever(document_store)\n",
-    "result = retriever.run(query=query)\n",
-    "\n",
-    "# Print the results\n",
-    "print(f\"BM25 documents: {len(result['bm25_documents'])}\")\n",
-    "print(f\"Embedding documents: {len(result['embedding_documents'])}\")\n",
-    "print(f\"Joined documents: {len(result['joined_documents'])}\")\n",
-    "print(f\"Ranked documents: {len(result['ranked_documents'])}\")\n",
-    "print(f\"Query embedding shape: {len(result['query_embedding'])}\")\n",
-    "\n",
-    "# Compare the top document from each stage\n",
-    "print(\"\\nTop BM25 document:\")\n",
-    "print(result[\"bm25_documents\"][0].content[:200] + \"...\")\n",
-    "print(f\"Score: {result['bm25_documents'][0].score:.4f}\")\n",
-    "\n",
-    "print(\"\\nTop embedding document:\")\n",
-    "print(result[\"embedding_documents\"][0].content[:200] + \"...\")\n",
-    "print(f\"Score: {result['embedding_documents'][0].score:.4f}\")\n",
-    "\n",
-    "print(\"\\nTop ranked document:\")\n",
-    "print(result[\"ranked_documents\"][0].content[:200] + \"...\")\n",
-    "print(f\"Score: {result['ranked_documents'][0].score:.4f}\")"
-   ]
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",