diff --git a/tutorials/6 - Chain Of Density Summarization.ipynb b/tutorials/6.chain-of-density.ipynb
similarity index 96%
rename from tutorials/6 - Chain Of Density Summarization.ipynb
rename to tutorials/6.chain-of-density.ipynb
index ef314a03c..cf9bd123d 100644
--- a/tutorials/6 - Chain Of Density Summarization.ipynb	
+++ b/tutorials/6.chain-of-density.ipynb
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "0dbdda0a-2648-4e0f-8633-ea19bef4a460",
    "metadata": {},
    "outputs": [
@@ -133,9 +133,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
       "You can now load the package via spacy.load('en_core_web_sm')\n"
      ]
@@ -192,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "bd6ebf95-60c6-4ec8-be17-d5ab436a67fd",
    "metadata": {},
    "outputs": [
@@ -202,7 +199,7 @@
        "['My', 'favourite', 'type', 'of', 'Sashimi', 'is', 'Toro']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "8a87b231-57b0-426c-98d5-cd7d8b512121",
    "metadata": {},
    "outputs": [
@@ -234,7 +231,7 @@
        "['I', \"'m\", 'fascinated', 'by', 'machine', 'learning', '!']"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "id": "c905dff4-5753-4274-90fe-44aa3393ff0f",
    "metadata": {},
    "outputs": [
@@ -287,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "id": "47a4a8f6-295d-4040-beb1-3c8e9ff3bf99",
    "metadata": {},
    "outputs": [],
@@ -301,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "id": "51197222-2124-46f8-9a57-555d43836401",
    "metadata": {},
    "outputs": [
@@ -311,7 +308,7 @@
        "(Apple, U.K., $1 billion)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "id": "9c2ad5a0-2f24-442e-a46a-3a265ef873f6",
    "metadata": {},
    "outputs": [
@@ -343,7 +340,7 @@
        "()"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -358,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "dc7964d3-61f6-436e-bfb0-080cd46c41bf",
    "metadata": {},
    "outputs": [
@@ -368,7 +365,7 @@
        "(J.K., one, Harry Potter')"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "id": "15accf59-a264-4e1c-9b77-8b486e423f95",
    "metadata": {},
    "outputs": [],
@@ -420,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 10,
    "id": "648206dc-a734-49eb-bd2e-8b46a914cacf",
    "metadata": {},
    "outputs": [
@@ -430,7 +427,7 @@
        "(17, 0, 0.0)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 11,
    "id": "9fd5717f-202a-4b39-976c-a32d0f1a4b29",
    "metadata": {},
    "outputs": [
@@ -454,7 +451,7 @@
        "(11, 3, 0.273)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -477,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 12,
    "id": "ae27bcc5-da32-4aaa-9ebb-dbc21700ee14",
    "metadata": {},
    "outputs": [
@@ -487,7 +484,7 @@
        "((82, 11, 0.134), (71, 17, 0.239))"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -538,18 +535,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 13,
    "id": "2ac40d98-2843-4c9c-bc18-50ab1d4ffa94",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel,Field\n",
+    "from pydantic import BaseModel,Field,field_validator\n",
     "from typing import List"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 14,
    "id": "486e85fc-3fc8-4143-bdf4-d7cef91a37cf",
    "metadata": {},
    "outputs": [],
@@ -581,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 15,
    "id": "609a9edd-7c4e-4586-a5be-037c4c3c7ff7",
    "metadata": {},
    "outputs": [
@@ -589,7 +586,7 @@
      "data": {
       "text/plain": [
        "{'description': 'This is an initial summary which should be long ( 4-5 sentences, ~80 words)\\nyet highly non-specific, containing little information beyond the entities marked as missing.\\nUse overly verbose languages and fillers (Eg. This article discusses) to reach ~80 words.',\n",
-       " 'properties': {'summary': {'description': 'This is a summary of the article provided which is overly verbose and uses fillers. It should be roughly 80 words in length',\n",
+       " 'properties': {'summary': {'description': 'This is a summary of the article provided which is overly verbose and uses fillers.         It should be roughly 80 words in length',\n",
        "   'title': 'Summary',\n",
        "   'type': 'string'}},\n",
        " 'required': ['summary'],\n",
@@ -597,7 +594,7 @@
        " 'type': 'object'}"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 16,
    "id": "d3d589ca-00cd-42cc-9a7a-a8f0620b4ea1",
    "metadata": {},
    "outputs": [],
@@ -685,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 17,
    "id": "8f81f281-0950-4973-81b6-e1acd8b35aa0",
    "metadata": {},
    "outputs": [],
@@ -778,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 18,
    "id": "fc66ffcc-db30-429a-8007-4d4a24bf2426",
    "metadata": {},
    "outputs": [],
@@ -792,7 +789,7 @@
     "    summary_chain = []\n",
     "    # We first generate an initial summary\n",
     "    summary: InitialSummary = client.chat.completions.create(  \n",
-    "        model=\"gpt-4-0613\",\n",
+    "        model=\"gpt-4-1106-preview\",\n",
     "        response_model=InitialSummary,\n",
     "        messages=[\n",
     "            {\n",
@@ -821,7 +818,7 @@
     "            ]\n",
     "        )\n",
     "        new_summary: RewrittenSummary = client.chat.completions.create( \n",
-    "            model=\"gpt-4-0613\",\n",
+    "            model=\"gpt-4-1106-preview\",\n",
     "            messages=[\n",
     "                {\n",
     "                    \"role\": \"system\",\n",
@@ -869,30 +866,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 19,
    "id": "6044c72b-fdc7-4cea-893b-a408c7b60230",
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"./article.txt\",\"r+\") as file:\n",
+    "with open(\"./assets/article.txt\",\"r+\") as file:\n",
     "    article = file.readline()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "id": "2302dedc-f22a-41e9-b9c2-1579a4e8f623",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 459 ms, sys: 17.5 ms, total: 477 ms\n",
-      "Wall time: 59.9 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -909,21 +897,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "id": "99f7361c-2737-44ef-8515-1919e009e718",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Article 1 -> Results (Tokens: 104, Entity Count: 0, Density: 0.0)\n",
-      "Article 2 -> Results (Tokens: 66, Entity Count: 6, Density: 0.091)\n",
-      "Article 3 -> Results (Tokens: 74, Entity Count: 7, Density: 0.095)\n",
-      "Article 4 -> Results (Tokens: 66, Entity Count: 11, Density: 0.167)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for index,summary in enumerate(summaries):\n",
     "    tokens,entity,density = calculate_entity_density(summary)\n",
@@ -940,29 +917,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "id": "e7149f4d-41ca-4cb1-8438-65cd97cb4246",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "This article discusses the instances of multiple renowned public figures and their respective candid encounters with body shaming. It delves into how these notable personalities, more specifically, famous females, have confidently demonstrated their authenticity on social media platforms, challenging societal constructs around body image. Furthermore, the article highlights the overwhelmingly positive public response and encouragement received from this display of genuine body acceptance, attempting to reshape conventional perspectives on beauty standards. Lastly, the article includes several individual narratives, illustrating each celebrity's unique approach in addressing body shaming and promoting body positivity.\n",
-      "\n",
-      "\n",
-      "Chrissy Teigen, Pink, Kelly Clarkson, Lena Dunham, Tyra Banks, and Gabourey Sidibe confront body shaming, displaying their bodies on social media and challenging body image norms. With diverse positivity approaches, they receive public backing. The struggle against body shaming by Banks and Sidibe, despite not being the focus of the piece, was also highlighted.\n",
-      "\n",
-      "\n",
-      "Chrissy Teigen, Pink, Kelly Clarkson, Lena Dunham, Tyra Banks, and Gabourey Sidibe are rallying against body shaming, proudly showcasing their bodies on social media. Each celebrity takes a unique approach to body positivity, generating public praise. Tyra Banks and Gabourey Sidibe particularly combat body shaming, with their stories noted in the report, underscoring the issue's widespread nature in Hollywood and beyond.\n",
-      "\n",
-      "\n",
-      "Stars Chrissy Teigen, Pink, Kelly Clarkson, Lena Dunham, Tyra Banks, and Gabourey Sidibe defy body shaming through social media platforms like Instagram and Twitter. Their unique approaches to body positivity earn public commendation. Tyra Banks and Gabourey Sidibe's stories, including Sidibe's response to Golden Globes critics, underscore the pervasive issue in Hollywood and beyond.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for summary in summaries:\n",
     "    print(f\"\\n{summary}\\n\")"
diff --git a/tutorials/article.txt b/tutorials/assets/article.txt
similarity index 100%
rename from tutorials/article.txt
rename to tutorials/assets/article.txt