diff --git a/week1/day2 EXERCISE.ipynb b/week1/day2 EXERCISE.ipynb
index 2c079f1..89a383f 100644
--- a/week1/day2 EXERCISE.ipynb	
+++ b/week1/day2 EXERCISE.ipynb	
@@ -308,7 +308,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week1/day5.ipynb b/week1/day5.ipynb
index 2d02cdf..300145f 100644
--- a/week1/day5.ipynb
+++ b/week1/day5.ipynb
@@ -501,7 +501,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week2/day1.ipynb b/week2/day1.ipynb
index e4803ee..c6d98f4 100644
--- a/week2/day1.ipynb
+++ b/week2/day1.ipynb
@@ -226,9 +226,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# GPT-3.5-Turbo\n",
+    "# GPT-4o-mini\n",
     "\n",
-    "completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n",
+    "completion = openai.chat.completions.create(model='gpt-4o-mini', messages=prompts)\n",
     "print(completion.choices[0].message.content)"
    ]
   },
@@ -239,17 +239,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# GPT-4o-mini\n",
+    "# GPT-4.1-mini\n",
     "# Temperature setting controls creativity\n",
     "\n",
     "completion = openai.chat.completions.create(\n",
-    "    model='gpt-4o-mini',\n",
+    "    model='gpt-4.1-mini',\n",
     "    messages=prompts,\n",
     "    temperature=0.7\n",
     ")\n",
     "print(completion.choices[0].message.content)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12d2a549-9d6e-4ea0-9c3e-b96a39e9959e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GPT-4.1-nano - extremely fast and cheap\n",
+    "\n",
+    "completion = openai.chat.completions.create(\n",
+    "    model='gpt-4.1-nano',\n",
+    "    messages=prompts\n",
+    ")\n",
+    "print(completion.choices[0].message.content)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -257,16 +273,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# GPT-4o\n",
+    "# GPT-4.1\n",
     "\n",
     "completion = openai.chat.completions.create(\n",
-    "    model='gpt-4o',\n",
+    "    model='gpt-4.1',\n",
     "    messages=prompts,\n",
     "    temperature=0.4\n",
     ")\n",
     "print(completion.choices[0].message.content)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96232ef4-dc9e-430b-a9df-f516685e7c9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you have access to this, here is the reasoning model o3-mini\n",
+    "# This is trained to think through its response before replying\n",
+    "# So it will take longer but the answer should be more reasoned - not that this helps..\n",
+    "\n",
+    "completion = openai.chat.completions.create(\n",
+    "    model='o3-mini',\n",
+    "    messages=prompts\n",
+    ")\n",
+    "print(completion.choices[0].message.content)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -365,7 +399,8 @@
    "outputs": [],
    "source": [
     "# As an alternative way to use Gemini that bypasses Google's python API library,\n",
-    "# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
+    "# Google released endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
+    "# We're also trying Gemini's latest reasoning/thinking model\n",
     "\n",
     "gemini_via_openai_client = OpenAI(\n",
     "    api_key=google_api_key, \n",
@@ -373,7 +408,7 @@
     ")\n",
     "\n",
     "response = gemini_via_openai_client.chat.completions.create(\n",
-    "    model=\"gemini-2.0-flash\",\n",
+    "    model=\"gemini-2.5-flash-preview-04-17\",\n",
     "    messages=prompts\n",
     ")\n",
     "print(response.choices[0].message.content)"
@@ -488,6 +523,33 @@
     "print(\"Number of words:\", len(content.split(\" \")))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cbf0d5dd-7f20-4090-a46d-da56ceec218f",
+   "metadata": {},
+   "source": [
+    "## Additional exercise to build your experience with the models\n",
+    "\n",
+    "This is optional, but if you have time, it's so great to get first hand experience with the capabilities of these different models.\n",
+    "\n",
+    "You could go back and ask the same question via the APIs above to get your own personal experience with the pros & cons of the models.\n",
+    "\n",
+    "Later in the course we'll look at benchmarks and compare LLMs on many dimensions. But nothing beats personal experience!\n",
+    "\n",
+    "Here are some questions to try:\n",
+    "1. The question above: \"How many words are there in your answer to this prompt\"\n",
+    "2. A creative question: \"In 3 sentences, describe the color Blue to someone who's never been able to see\"\n",
+    "3. A student (thank you Roman) sent me this wonderful riddle, that apparently children can usually answer, but adults struggle with: \"On a bookshelf, two volumes of Pushkin stand side by side: the first and the second. The pages of each volume together have a thickness of 2 cm, and each cover is 2 mm thick. A worm gnawed (perpendicular to the pages) from the first page of the first volume to the last page of the second volume. What distance did it gnaw through?\".\n",
+    "\n",
+    "The answer may not be what you expect, and even though I'm quite good at puzzles, I'm embarrassed to admit that I got this one wrong.\n",
+    "\n",
+    "### What to look out for as you experiment with models\n",
+    "\n",
+    "1. How the Chat models differ from the Reasoning models (also known as Thinking models)\n",
+    "2. The ability to solve problems and the ability to be creative\n",
+    "3. Speed of generation\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c09e6b5c-6816-4cd3-a5cd-a20e4171b1a0",
@@ -762,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week5/day4.5.ipynb b/week5/day4.5.ipynb
index cf30df3..2cad1ed 100644
--- a/week5/day4.5.ipynb
+++ b/week5/day4.5.ipynb
@@ -372,7 +372,7 @@
    "source": [
     "# And in Gradio:\n",
     "\n",
-    "view = gr.ChatInterface(chat).launch()"
+    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
    ]
   },
   {
@@ -400,7 +400,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week5/day4.ipynb b/week5/day4.ipynb
index 78b0ed7..d12a111 100644
--- a/week5/day4.ipynb
+++ b/week5/day4.ipynb
@@ -434,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week6/day5.ipynb b/week6/day5.ipynb
index e437dbc..5d5619e 100644
--- a/week6/day5.ipynb
+++ b/week6/day5.ipynb
@@ -547,7 +547,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/week8/day2.0.ipynb b/week8/day2.0.ipynb
index d1e054e..553880e 100644
--- a/week8/day2.0.ipynb
+++ b/week8/day2.0.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "993a2a24-1a58-42be-8034-6d116fb8d786",
    "metadata": {},
    "outputs": [],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "2359ccc0-dbf2-4b1e-9473-e472b32f548b",
    "metadata": {},
    "outputs": [],
@@ -66,18 +66,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "645167e6-cf0d-42d2-949f-1089a25a2841",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Log in to HuggingFace\n",
     "\n",
@@ -101,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "688bd995-ec3e-43cd-8179-7fe14b275877",
    "metadata": {},
    "outputs": [],
@@ -114,21 +106,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2817eaf5-4302-4a18-9148-d1062e3b3dbb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'How much does this cost to the nearest dollar?\\n\\nDelphi FG0166 Fuel Pump Module\\nDelphi brings 80 years of OE Heritage into each Delphi pump, ensuring quality and fitment for each Delphi part. Part is validated, tested and matched to the right vehicle application Delphi brings 80 years of OE Heritage into each Delphi assembly, ensuring quality and fitment for each Delphi part Always be sure to check and clean fuel tank to avoid unnecessary returns Rigorous OE-testing ensures the pump can withstand extreme temperatures Brand Delphi, Fit Type Vehicle Specific Fit, Dimensions LxWxH 19.7 x 7.7 x 5.1 inches, Weight 2.2 Pounds, Auto Part Position Unknown, Operation Mode Mechanical, Manufacturer Delphi, Model FUEL PUMP, Dimensions 19.7\\n\\nPrice is $227.00'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train[0].prompt"
    ]
@@ -154,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f4aab95e-d719-4476-b6e7-e248120df25a",
    "metadata": {},
    "outputs": [],
@@ -164,18 +145,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "5f95dafd-ab80-464e-ba8a-dec7a2424780",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deleted existing collection: products\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Check if the collection exists and delete it if it does\n",
     "collection_name = \"products\"\n",
@@ -211,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "a87db200-d19d-44bf-acbd-15c45c70f5c9",
    "metadata": {},
    "outputs": [],
@@ -221,140 +194,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "9b23a025-4c35-4d3a-96ad-b956cad37b0a",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Pass in a list of texts, get back a numpy array of vectors\n",
     "\n",
-    "vector = model.encode([\"Well hi there\"])[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8adde63f-e732-4f7c-bba9-f8b2a469f14e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-9.46715921e-02,  4.27619480e-02,  5.51620498e-02, -5.10962738e-04,\n",
-       "        1.16203260e-02, -6.80130497e-02,  2.76405811e-02,  6.06974475e-02,\n",
-       "        2.88530309e-02, -1.74128171e-02, -4.94346656e-02,  2.30993368e-02,\n",
-       "       -1.28614372e-02, -4.31402922e-02,  2.17510257e-02,  4.26548645e-02,\n",
-       "        5.10499887e-02, -7.79727027e-02, -1.23247243e-01,  3.67455557e-02,\n",
-       "        4.54110606e-03,  9.47937742e-02, -5.53098507e-02,  1.70641206e-02,\n",
-       "       -2.92873308e-02, -4.47124951e-02,  2.06784457e-02,  6.39320165e-02,\n",
-       "        2.27427781e-02,  4.87789772e-02, -2.33503035e-03,  4.72859442e-02,\n",
-       "       -2.86258962e-02,  2.30624825e-02,  2.45130397e-02,  3.95681970e-02,\n",
-       "       -4.33176197e-02, -1.02316625e-01,  2.79874774e-03,  2.39304882e-02,\n",
-       "        1.61556154e-02, -8.99078418e-03,  2.07255688e-02,  6.40123338e-02,\n",
-       "        6.89179525e-02, -6.98360875e-02,  2.89764395e-03, -8.10988992e-02,\n",
-       "        1.71123203e-02,  2.50653620e-03, -1.06529057e-01, -4.87733148e-02,\n",
-       "       -1.67762041e-02, -2.28662305e-02,  1.14816584e-01,  4.87413220e-02,\n",
-       "       -1.64962150e-02, -6.90832064e-02,  1.13612078e-01, -7.18485564e-02,\n",
-       "       -9.01571065e-02,  3.91712456e-06, -8.66769403e-02, -4.05916385e-02,\n",
-       "        3.71317938e-02, -1.77618619e-02, -5.57464026e-02, -4.57097329e-02,\n",
-       "       -5.43141440e-02, -4.00611758e-02, -4.60227989e-02,  2.82194205e-02,\n",
-       "       -2.33606398e-02,  1.44406883e-02, -1.52777461e-02, -4.34328429e-02,\n",
-       "        6.81274384e-02,  8.21894556e-02,  7.83890672e-03, -2.85973065e-02,\n",
-       "        6.14309646e-02, -4.92684618e-02,  3.27055180e-03, -2.72683166e-02,\n",
-       "       -4.59346883e-02, -2.60167848e-02,  5.70650175e-02, -5.86463953e-04,\n",
-       "       -4.37076613e-02, -2.47807417e-04, -8.73549953e-02,  3.48776393e-02,\n",
-       "        1.39327236e-02, -1.60043724e-02,  2.86958776e-02, -9.48595777e-02,\n",
-       "        9.40612778e-02,  5.92685342e-02, -8.65014344e-02,  1.45011380e-01,\n",
-       "        2.35388409e-02,  3.43324952e-02,  2.51516962e-04,  7.15541244e-02,\n",
-       "       -3.12182512e-02,  3.86665650e-02, -2.47745048e-02,  6.52674213e-02,\n",
-       "       -8.28817189e-02, -2.80247182e-02,  9.34544671e-03, -7.85543211e-03,\n",
-       "        5.30728251e-02,  2.96895411e-02,  3.27329561e-02,  4.94737104e-02,\n",
-       "        2.52208579e-02,  4.67068404e-02,  5.03403395e-02, -7.23745152e-02,\n",
-       "        2.54435297e-02, -3.67216468e-02,  1.27570340e-02,  1.46063734e-02,\n",
-       "        2.11492609e-02, -5.56909367e-02, -9.18510836e-03, -5.63397912e-34,\n",
-       "        9.58769619e-02,  5.94578013e-02,  5.11445254e-02,  3.36360112e-02,\n",
-       "       -1.34414928e-02, -2.77202837e-02, -3.48436125e-02,  1.80352535e-02,\n",
-       "       -2.53210980e-02,  6.73587900e-03,  5.47841080e-02, -3.60574126e-02,\n",
-       "       -5.20869941e-02, -2.90345643e-02,  4.38962830e-03,  6.50023222e-02,\n",
-       "        3.07485200e-02,  2.00220738e-02,  1.73043029e-03,  2.96725915e-03,\n",
-       "        3.40953190e-03, -6.78145364e-02,  3.41304727e-02,  8.37869197e-03,\n",
-       "        5.39904200e-02,  2.70389449e-02,  7.84119442e-02, -1.30136222e-01,\n",
-       "        4.84649912e-02,  5.14179170e-02, -7.94680975e-03,  5.57883596e-03,\n",
-       "       -5.31026050e-02,  3.81299518e-02, -3.05512808e-02, -7.69778788e-02,\n",
-       "        1.20531386e-02, -4.08992954e-02, -8.69358853e-02,  6.38056174e-02,\n",
-       "        1.68674774e-02,  1.68734661e-03,  6.28894269e-02, -1.67711563e-02,\n",
-       "        2.15586051e-02,  7.10083405e-04,  2.81031127e-03, -8.89794994e-03,\n",
-       "       -1.80887729e-02, -2.16216948e-02, -5.59149943e-02,  1.78774409e-02,\n",
-       "       -9.27092806e-02,  7.27912923e-03, -1.27753615e-01, -4.86937575e-02,\n",
-       "        1.45872515e-02, -1.62751433e-02,  6.75622374e-02,  3.87702174e-02,\n",
-       "        7.23295361e-02,  9.14991871e-02, -9.65291932e-02,  4.84791324e-02,\n",
-       "       -1.06274165e-01, -1.05042597e-02,  8.90350714e-02, -8.07525739e-02,\n",
-       "        7.87081569e-02, -2.04917882e-02, -5.55080660e-02, -3.31532657e-02,\n",
-       "       -2.14429274e-02,  4.94700260e-02, -7.05119222e-02,  6.63999170e-02,\n",
-       "        7.39671215e-02, -2.70118006e-03,  1.62262432e-02, -3.98229137e-02,\n",
-       "        5.94092607e-02, -7.14372285e-03, -3.33479457e-02,  2.30419375e-02,\n",
-       "        1.87185761e-02, -6.15724660e-02, -8.55441322e-04, -1.05786659e-01,\n",
-       "       -8.43619034e-02, -3.92993316e-02, -3.16447318e-02,  6.60644025e-02,\n",
-       "        9.41815823e-02, -8.35982785e-02,  9.50878393e-03,  1.25504937e-34,\n",
-       "        6.38198331e-02,  2.11371221e-02, -1.65899675e-02,  1.88641250e-02,\n",
-       "       -5.57018854e-02,  1.82811334e-03, -1.37586696e-02,  8.16279203e-02,\n",
-       "       -9.13297161e-02,  7.06856027e-02,  6.79991618e-02, -5.44536524e-02,\n",
-       "        3.80394608e-02,  3.80505901e-03,  1.03689805e-01,  7.32792250e-04,\n",
-       "        2.95661930e-02,  4.19423953e-02, -1.20444328e-01,  1.24932425e-02,\n",
-       "       -5.53506613e-02,  1.75228491e-02, -2.28164811e-02, -5.79299554e-02,\n",
-       "        9.42929648e-03, -5.42278960e-03, -3.94944148e-03,  2.82348841e-02,\n",
-       "       -1.28066897e-01, -1.31305009e-02,  7.42957145e-02, -1.74529813e-02,\n",
-       "       -9.72758904e-02,  8.25622585e-03,  2.06900928e-02, -5.29770693e-03,\n",
-       "       -1.37696080e-02, -3.50973643e-02,  1.74977854e-02, -1.76232997e-02,\n",
-       "       -6.50825426e-02, -3.84675786e-02, -8.76396820e-02,  3.21291834e-02,\n",
-       "        2.55022198e-03, -2.09378973e-02,  5.55310138e-02,  2.57095750e-02,\n",
-       "       -2.94735264e-02,  1.25047946e-02, -6.83466196e-02, -8.00624415e-02,\n",
-       "       -1.46906544e-02,  1.03744324e-02, -8.51863101e-02, -1.10539049e-02,\n",
-       "        2.14596409e-02,  4.08609174e-02,  3.31646428e-02, -2.76757460e-02,\n",
-       "       -2.01877337e-02,  8.98879580e-03,  3.92048508e-02,  1.15103319e-01,\n",
-       "        5.50440997e-02,  2.72754990e-02, -1.09526694e-01, -1.72622949e-02,\n",
-       "        1.33438502e-02, -1.73702314e-02, -5.04373619e-03, -2.00292896e-02,\n",
-       "        1.16672359e-01, -1.84322968e-02,  3.70628126e-02,  1.60885658e-02,\n",
-       "        3.48830372e-02,  5.50574400e-02, -6.60797628e-03,  7.06828609e-02,\n",
-       "        4.07849252e-02, -1.43314507e-02, -2.85441079e-03,  2.74251588e-02,\n",
-       "       -4.26768996e-02,  1.26583334e-02,  3.34343277e-02,  1.62644926e-02,\n",
-       "        1.19263111e-02, -2.92118900e-02,  2.73978021e-02,  3.44304889e-02,\n",
-       "        2.52832547e-02,  3.07514369e-02,  3.22557390e-02, -1.74628472e-08,\n",
-       "       -1.52690830e-02,  5.37678460e-03,  1.41246513e-01,  5.08366451e-02,\n",
-       "        5.32256104e-02,  9.67938006e-02,  4.33674715e-02, -6.48309989e-03,\n",
-       "        1.58604085e-02,  4.05631103e-02,  6.94985166e-02,  6.04905337e-02,\n",
-       "       -6.26189336e-02, -3.96144278e-02,  1.10648885e-01,  1.67735182e-02,\n",
-       "       -7.68693630e-03,  2.59615108e-02, -5.28793186e-02, -2.22318210e-02,\n",
-       "        1.74595844e-02,  4.75339778e-02,  3.27674635e-02, -4.59685028e-02,\n",
-       "        2.01770533e-02, -1.60875805e-02, -1.58613976e-02, -1.66658163e-02,\n",
-       "       -3.05246692e-02, -3.87907438e-02, -1.27654579e-02,  6.57610297e-02,\n",
-       "       -2.22502891e-02, -9.44992620e-03,  2.32080184e-02,  2.66038440e-02,\n",
-       "        2.14203820e-02, -7.54579064e-03,  8.84752199e-02, -9.43513960e-02,\n",
-       "       -5.74870482e-02, -7.77097791e-02,  1.95792271e-03, -1.50347762e-02,\n",
-       "       -8.08496401e-03,  1.88217536e-02,  8.42519756e-03, -3.78591903e-02,\n",
-       "        1.24534657e-02, -7.94995651e-02, -2.15790682e-02,  1.20276492e-02,\n",
-       "        1.74870938e-02,  8.74478668e-02,  6.64091483e-02,  3.13736796e-02,\n",
-       "       -1.00629032e-02,  2.07700878e-02, -5.20163439e-02, -8.91334843e-03,\n",
-       "        1.48542315e-01, -2.51267431e-03,  9.93156135e-02,  2.34929714e-02],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "vector = model.encode([\"Well hi there\"])[0]\n",
     "vector"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "id": "8adde63f-e732-4f7c-bba9-f8b2a469f14e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quick sidebar - extra to the videos - a function to compare vectors\n",
+    "\n",
+    "import numpy as np\n",
+    "def cosine_similarity(a, b):\n",
+    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n",
+    "\n",
+    "def how_similar(text1, text2):\n",
+    "    vector1, vector2 = model.encode([text1, text2])\n",
+    "    similarity = cosine_similarity(vector1, vector2)\n",
+    "    print(f\"Similarity between {text1} and {text2} is {similarity*100:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f9c8d19-6993-42e7-afd6-4c61ffc19419",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# And let's see how adding a few words to the context can change things up!\n",
+    "\n",
+    "how_similar(\"Java\", \"C++\")\n",
+    "how_similar(\"Java\", \"mug\")\n",
+    "how_similar(\"Cup of Java\", \"mug\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "38de1bf8-c9b5-45b4-9f4b-86af93b3f80d",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# OK back to the main story - let's make something we can vectorize\n",
+    "\n",
     "def description(item):\n",
     "    text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n",
     "    return text.split(\"\\n\\nPrice is $\")[0]"
@@ -362,21 +256,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "8c1205bd-4692-44ef-8ea4-69f255354537",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Delphi FG0166 Fuel Pump Module\\nDelphi brings 80 years of OE Heritage into each Delphi pump, ensuring quality and fitment for each Delphi part. Part is validated, tested and matched to the right vehicle application Delphi brings 80 years of OE Heritage into each Delphi assembly, ensuring quality and fitment for each Delphi part Always be sure to check and clean fuel tank to avoid unnecessary returns Rigorous OE-testing ensures the pump can withstand extreme temperatures Brand Delphi, Fit Type Vehicle Specific Fit, Dimensions LxWxH 19.7 x 7.7 x 5.1 inches, Weight 2.2 Pounds, Auto Part Position Unknown, Operation Mode Mechanical, Manufacturer Delphi, Model FUEL PUMP, Dimensions 19.7'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "description(train[0])"
    ]
@@ -399,18 +282,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "8c79e2fe-1f50-4ebf-9a93-34f3088f2996",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 20/20 [01:01<00:00,  3.05s/it]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "NUMBER_OF_DOCUMENTS = len(train)\n",
     "\n",