diff --git a/Word Embeddings.ipynb b/Word Embeddings.ipynb
new file mode 100644
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+++ b/Word Embeddings.ipynb	
@@ -0,0 +1,1384 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Souce : https://www.youtube.com/watch?v=xzHhZh7F25I&t=371s\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install openai -q\n",
+    "!pip install matplotlib\n",
+    "!pip install plotly\n",
+    "!pip install scipy\n",
+    "!pip install pandas\n",
+    "!pip install numpy\n",
+    "!pip install scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from getpass import getpass\n",
+    "import matplotlib\n",
+    "import os\n",
+    "\n",
+    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('words.csv')\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai.embeddings_utils import get_embedding\n",
+    "\n",
+    "df['embedding'] = df['text'].apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
+    "df.to_csv('word_embeddings.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_embedding(\"the fox crossed the road\", engine='text-embedding-ada-002')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('word_embeddings.csv')\n",
+    "df['embedding'] = df['embedding'].apply(eval).apply(np.array)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_term = input('Enter a search term: ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# semantic search\n",
+    "search_term_vector = get_embedding(search_term, engine=\"text-embedding-ada-002\")\n",
+    "search_term_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>text</th>\n",
+       "      <th>embedding</th>\n",
+       "      <th>similarities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>red</td>\n",
+       "      <td>[1.8579006791696884e-05, -0.024676261469721794...</td>\n",
+       "      <td>0.808966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>potatoes</td>\n",
+       "      <td>[0.005025846417993307, -0.031079445034265518, ...</td>\n",
+       "      <td>0.827480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>soda</td>\n",
+       "      <td>[0.02582913264632225, -0.007537944242358208, -...</td>\n",
+       "      <td>0.828346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>cheese</td>\n",
+       "      <td>[-0.0032554215285927057, -0.008896322920918465...</td>\n",
+       "      <td>0.839099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>water</td>\n",
+       "      <td>[0.019031280651688576, -0.01257743313908577, 0...</td>\n",
+       "      <td>0.796891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>blue</td>\n",
+       "      <td>[0.005379225127398968, -0.007354139816015959, ...</td>\n",
+       "      <td>0.787710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>crispy</td>\n",
+       "      <td>[-0.0010056837927550077, -0.005415474995970726...</td>\n",
+       "      <td>0.823643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>hamburger</td>\n",
+       "      <td>[-0.013206875883042812, -0.0018223668448626995...</td>\n",
+       "      <td>0.913614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>[-0.0007566261338070035, -0.0194522924721241, ...</td>\n",
+       "      <td>0.802847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>green</td>\n",
+       "      <td>[0.01538460049778223, -0.010931522585451603, 0...</td>\n",
+       "      <td>0.786308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>milk</td>\n",
+       "      <td>[0.0009292512550018728, -0.019319288432598114,...</td>\n",
+       "      <td>0.815801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>la croix</td>\n",
+       "      <td>[-0.012410983443260193, -0.01145013328641653, ...</td>\n",
+       "      <td>0.768867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>[0.007648157421499491, -0.024834953248500824, ...</td>\n",
+       "      <td>0.802653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>chocolate</td>\n",
+       "      <td>[0.0015591585543006659, -0.013005273416638374,...</td>\n",
+       "      <td>0.830714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>french fries</td>\n",
+       "      <td>[0.0014257068978622556, -0.016548126935958862,...</td>\n",
+       "      <td>0.853867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>latte</td>\n",
+       "      <td>[-0.015675576403737068, -0.003918894100934267,...</td>\n",
+       "      <td>0.797174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>cake</td>\n",
+       "      <td>[-0.013669422827661037, -0.016827937215566635,...</td>\n",
+       "      <td>0.829604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>brown</td>\n",
+       "      <td>[-0.0033683686051517725, -0.015937969088554382...</td>\n",
+       "      <td>0.787217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>cheeseburger</td>\n",
+       "      <td>[-0.01824556663632393, 0.00504859397187829, 0....</td>\n",
+       "      <td>0.886417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>espresso</td>\n",
+       "      <td>[-0.022529492154717445, -0.012672839686274529,...</td>\n",
+       "      <td>0.813151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20</td>\n",
+       "      <td>cheesecake</td>\n",
+       "      <td>[0.011245746165513992, -0.012743037194013596, ...</td>\n",
+       "      <td>0.821882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>black</td>\n",
+       "      <td>[-0.015190942212939262, -0.031228860840201378,...</td>\n",
+       "      <td>0.776495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>22</td>\n",
+       "      <td>mocha</td>\n",
+       "      <td>[-0.012487593106925488, -0.026140518486499786,...</td>\n",
+       "      <td>0.813406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23</td>\n",
+       "      <td>fizzy</td>\n",
+       "      <td>[-0.013046763837337494, -0.010264534503221512,...</td>\n",
+       "      <td>0.806922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>carbon</td>\n",
+       "      <td>[0.004404176026582718, -0.025098854675889015, ...</td>\n",
+       "      <td>0.780440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>25</td>\n",
+       "      <td>banana</td>\n",
+       "      <td>[-0.013975119218230247, -0.03290277719497681, ...</td>\n",
+       "      <td>0.821447</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0          text  \\\n",
+       "0            0           red   \n",
+       "1            1      potatoes   \n",
+       "2            2          soda   \n",
+       "3            3        cheese   \n",
+       "4            4         water   \n",
+       "5            5          blue   \n",
+       "6            6        crispy   \n",
+       "7            7     hamburger   \n",
+       "8            8        coffee   \n",
+       "9            9         green   \n",
+       "10          10          milk   \n",
+       "11          11      la croix   \n",
+       "12          12        yellow   \n",
+       "13          13     chocolate   \n",
+       "14          14  french fries   \n",
+       "15          15         latte   \n",
+       "16          16          cake   \n",
+       "17          17         brown   \n",
+       "18          18  cheeseburger   \n",
+       "19          19      espresso   \n",
+       "20          20    cheesecake   \n",
+       "21          21         black   \n",
+       "22          22         mocha   \n",
+       "23          23         fizzy   \n",
+       "24          24        carbon   \n",
+       "25          25        banana   \n",
+       "\n",
+       "                                            embedding  similarities  \n",
+       "0   [1.8579006791696884e-05, -0.024676261469721794...      0.808966  \n",
+       "1   [0.005025846417993307, -0.031079445034265518, ...      0.827480  \n",
+       "2   [0.02582913264632225, -0.007537944242358208, -...      0.828346  \n",
+       "3   [-0.0032554215285927057, -0.008896322920918465...      0.839099  \n",
+       "4   [0.019031280651688576, -0.01257743313908577, 0...      0.796891  \n",
+       "5   [0.005379225127398968, -0.007354139816015959, ...      0.787710  \n",
+       "6   [-0.0010056837927550077, -0.005415474995970726...      0.823643  \n",
+       "7   [-0.013206875883042812, -0.0018223668448626995...      0.913614  \n",
+       "8   [-0.0007566261338070035, -0.0194522924721241, ...      0.802847  \n",
+       "9   [0.01538460049778223, -0.010931522585451603, 0...      0.786308  \n",
+       "10  [0.0009292512550018728, -0.019319288432598114,...      0.815801  \n",
+       "11  [-0.012410983443260193, -0.01145013328641653, ...      0.768867  \n",
+       "12  [0.007648157421499491, -0.024834953248500824, ...      0.802653  \n",
+       "13  [0.0015591585543006659, -0.013005273416638374,...      0.830714  \n",
+       "14  [0.0014257068978622556, -0.016548126935958862,...      0.853867  \n",
+       "15  [-0.015675576403737068, -0.003918894100934267,...      0.797174  \n",
+       "16  [-0.013669422827661037, -0.016827937215566635,...      0.829604  \n",
+       "17  [-0.0033683686051517725, -0.015937969088554382...      0.787217  \n",
+       "18  [-0.01824556663632393, 0.00504859397187829, 0....      0.886417  \n",
+       "19  [-0.022529492154717445, -0.012672839686274529,...      0.813151  \n",
+       "20  [0.011245746165513992, -0.012743037194013596, ...      0.821882  \n",
+       "21  [-0.015190942212939262, -0.031228860840201378,...      0.776495  \n",
+       "22  [-0.012487593106925488, -0.026140518486499786,...      0.813406  \n",
+       "23  [-0.013046763837337494, -0.010264534503221512,...      0.806922  \n",
+       "24  [0.004404176026582718, -0.025098854675889015, ...      0.780440  \n",
+       "25  [-0.013975119218230247, -0.03290277719497681, ...      0.821447  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from openai.embeddings_utils import cosine_similarity\n",
+    "\n",
+    "df[\"similarities\"] = df['embedding'].apply(lambda x: cosine_similarity(x, search_term_vector))\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>text</th>\n",
+       "      <th>embedding</th>\n",
+       "      <th>similarities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>hamburger</td>\n",
+       "      <td>[-0.013206875883042812, -0.0018223668448626995...</td>\n",
+       "      <td>0.913614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>cheeseburger</td>\n",
+       "      <td>[-0.01824556663632393, 0.00504859397187829, 0....</td>\n",
+       "      <td>0.886417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>french fries</td>\n",
+       "      <td>[0.0014257068978622556, -0.016548126935958862,...</td>\n",
+       "      <td>0.853867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>cheese</td>\n",
+       "      <td>[-0.0032554215285927057, -0.008896322920918465...</td>\n",
+       "      <td>0.839099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>chocolate</td>\n",
+       "      <td>[0.0015591585543006659, -0.013005273416638374,...</td>\n",
+       "      <td>0.830714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>cake</td>\n",
+       "      <td>[-0.013669422827661037, -0.016827937215566635,...</td>\n",
+       "      <td>0.829604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>soda</td>\n",
+       "      <td>[0.02582913264632225, -0.007537944242358208, -...</td>\n",
+       "      <td>0.828346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>potatoes</td>\n",
+       "      <td>[0.005025846417993307, -0.031079445034265518, ...</td>\n",
+       "      <td>0.827480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>crispy</td>\n",
+       "      <td>[-0.0010056837927550077, -0.005415474995970726...</td>\n",
+       "      <td>0.823643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20</td>\n",
+       "      <td>cheesecake</td>\n",
+       "      <td>[0.011245746165513992, -0.012743037194013596, ...</td>\n",
+       "      <td>0.821882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>25</td>\n",
+       "      <td>banana</td>\n",
+       "      <td>[-0.013975119218230247, -0.03290277719497681, ...</td>\n",
+       "      <td>0.821447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>milk</td>\n",
+       "      <td>[0.0009292512550018728, -0.019319288432598114,...</td>\n",
+       "      <td>0.815801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>22</td>\n",
+       "      <td>mocha</td>\n",
+       "      <td>[-0.012487593106925488, -0.026140518486499786,...</td>\n",
+       "      <td>0.813406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>espresso</td>\n",
+       "      <td>[-0.022529492154717445, -0.012672839686274529,...</td>\n",
+       "      <td>0.813151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>red</td>\n",
+       "      <td>[1.8579006791696884e-05, -0.024676261469721794...</td>\n",
+       "      <td>0.808966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23</td>\n",
+       "      <td>fizzy</td>\n",
+       "      <td>[-0.013046763837337494, -0.010264534503221512,...</td>\n",
+       "      <td>0.806922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>[-0.0007566261338070035, -0.0194522924721241, ...</td>\n",
+       "      <td>0.802847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>[0.007648157421499491, -0.024834953248500824, ...</td>\n",
+       "      <td>0.802653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>latte</td>\n",
+       "      <td>[-0.015675576403737068, -0.003918894100934267,...</td>\n",
+       "      <td>0.797174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>water</td>\n",
+       "      <td>[0.019031280651688576, -0.01257743313908577, 0...</td>\n",
+       "      <td>0.796891</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0          text  \\\n",
+       "7            7     hamburger   \n",
+       "18          18  cheeseburger   \n",
+       "14          14  french fries   \n",
+       "3            3        cheese   \n",
+       "13          13     chocolate   \n",
+       "16          16          cake   \n",
+       "2            2          soda   \n",
+       "1            1      potatoes   \n",
+       "6            6        crispy   \n",
+       "20          20    cheesecake   \n",
+       "25          25        banana   \n",
+       "10          10          milk   \n",
+       "22          22         mocha   \n",
+       "19          19      espresso   \n",
+       "0            0           red   \n",
+       "23          23         fizzy   \n",
+       "8            8        coffee   \n",
+       "12          12        yellow   \n",
+       "15          15         latte   \n",
+       "4            4         water   \n",
+       "\n",
+       "                                            embedding  similarities  \n",
+       "7   [-0.013206875883042812, -0.0018223668448626995...      0.913614  \n",
+       "18  [-0.01824556663632393, 0.00504859397187829, 0....      0.886417  \n",
+       "14  [0.0014257068978622556, -0.016548126935958862,...      0.853867  \n",
+       "3   [-0.0032554215285927057, -0.008896322920918465...      0.839099  \n",
+       "13  [0.0015591585543006659, -0.013005273416638374,...      0.830714  \n",
+       "16  [-0.013669422827661037, -0.016827937215566635,...      0.829604  \n",
+       "2   [0.02582913264632225, -0.007537944242358208, -...      0.828346  \n",
+       "1   [0.005025846417993307, -0.031079445034265518, ...      0.827480  \n",
+       "6   [-0.0010056837927550077, -0.005415474995970726...      0.823643  \n",
+       "20  [0.011245746165513992, -0.012743037194013596, ...      0.821882  \n",
+       "25  [-0.013975119218230247, -0.03290277719497681, ...      0.821447  \n",
+       "10  [0.0009292512550018728, -0.019319288432598114,...      0.815801  \n",
+       "22  [-0.012487593106925488, -0.026140518486499786,...      0.813406  \n",
+       "19  [-0.022529492154717445, -0.012672839686274529,...      0.813151  \n",
+       "0   [1.8579006791696884e-05, -0.024676261469721794...      0.808966  \n",
+       "23  [-0.013046763837337494, -0.010264534503221512,...      0.806922  \n",
+       "8   [-0.0007566261338070035, -0.0194522924721241, ...      0.802847  \n",
+       "12  [0.007648157421499491, -0.024834953248500824, ...      0.802653  \n",
+       "15  [-0.015675576403737068, -0.003918894100934267,...      0.797174  \n",
+       "4   [0.019031280651688576, -0.01257743313908577, 0...      0.796891  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(\"similarities\", ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.02160024, -0.03199213, -0.01623621, ..., -0.00423047,\n",
+       "        0.00083153, -0.02900861])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "food_df = df.copy()\n",
+    "\n",
+    "milk_vector = food_df['embedding'][10]\n",
+    "espresso_vector = food_df['embedding'][19]\n",
+    "\n",
+    "milk_espresso_vector = milk_vector + espresso_vector\n",
+    "milk_espresso_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>text</th>\n",
+       "      <th>embedding</th>\n",
+       "      <th>similarities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>espresso</td>\n",
+       "      <td>[-0.022529492154717445, -0.012672839686274529,...</td>\n",
+       "      <td>0.960471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>milk</td>\n",
+       "      <td>[0.0009292512550018728, -0.019319288432598114,...</td>\n",
+       "      <td>0.960471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>latte</td>\n",
+       "      <td>[-0.015675576403737068, -0.003918894100934267,...</td>\n",
+       "      <td>0.922988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>22</td>\n",
+       "      <td>mocha</td>\n",
+       "      <td>[-0.012487593106925488, -0.026140518486499786,...</td>\n",
+       "      <td>0.899328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>[-0.0007566261338070035, -0.0194522924721241, ...</td>\n",
+       "      <td>0.895389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>cheese</td>\n",
+       "      <td>[-0.0032554215285927057, -0.008896322920918465...</td>\n",
+       "      <td>0.884663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>chocolate</td>\n",
+       "      <td>[0.0015591585543006659, -0.013005273416638374,...</td>\n",
+       "      <td>0.883418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>soda</td>\n",
+       "      <td>[0.02582913264632225, -0.007537944242358208, -...</td>\n",
+       "      <td>0.874203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>water</td>\n",
+       "      <td>[0.019031280651688576, -0.01257743313908577, 0...</td>\n",
+       "      <td>0.866025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>hamburger</td>\n",
+       "      <td>[-0.013206875883042812, -0.0018223668448626995...</td>\n",
+       "      <td>0.852707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23</td>\n",
+       "      <td>fizzy</td>\n",
+       "      <td>[-0.013046763837337494, -0.010264534503221512,...</td>\n",
+       "      <td>0.851090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>potatoes</td>\n",
+       "      <td>[0.005025846417993307, -0.031079445034265518, ...</td>\n",
+       "      <td>0.850823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>25</td>\n",
+       "      <td>banana</td>\n",
+       "      <td>[-0.013975119218230247, -0.03290277719497681, ...</td>\n",
+       "      <td>0.848958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20</td>\n",
+       "      <td>cheesecake</td>\n",
+       "      <td>[0.011245746165513992, -0.012743037194013596, ...</td>\n",
+       "      <td>0.846989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>cheeseburger</td>\n",
+       "      <td>[-0.01824556663632393, 0.00504859397187829, 0....</td>\n",
+       "      <td>0.846296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>french fries</td>\n",
+       "      <td>[0.0014257068978622556, -0.016548126935958862,...</td>\n",
+       "      <td>0.842877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>cake</td>\n",
+       "      <td>[-0.013669422827661037, -0.016827937215566635,...</td>\n",
+       "      <td>0.842138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>[0.007648157421499491, -0.024834953248500824, ...</td>\n",
+       "      <td>0.837775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>red</td>\n",
+       "      <td>[1.8579006791696884e-05, -0.024676261469721794...</td>\n",
+       "      <td>0.836119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>crispy</td>\n",
+       "      <td>[-0.0010056837927550077, -0.005415474995970726...</td>\n",
+       "      <td>0.835187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>brown</td>\n",
+       "      <td>[-0.0033683686051517725, -0.015937969088554382...</td>\n",
+       "      <td>0.829839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>green</td>\n",
+       "      <td>[0.01538460049778223, -0.010931522585451603, 0...</td>\n",
+       "      <td>0.828841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>black</td>\n",
+       "      <td>[-0.015190942212939262, -0.031228860840201378,...</td>\n",
+       "      <td>0.824785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>blue</td>\n",
+       "      <td>[0.005379225127398968, -0.007354139816015959, ...</td>\n",
+       "      <td>0.822992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>carbon</td>\n",
+       "      <td>[0.004404176026582718, -0.025098854675889015, ...</td>\n",
+       "      <td>0.816680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>la croix</td>\n",
+       "      <td>[-0.012410983443260193, -0.01145013328641653, ...</td>\n",
+       "      <td>0.802982</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0          text  \\\n",
+       "19          19      espresso   \n",
+       "10          10          milk   \n",
+       "15          15         latte   \n",
+       "22          22         mocha   \n",
+       "8            8        coffee   \n",
+       "3            3        cheese   \n",
+       "13          13     chocolate   \n",
+       "2            2          soda   \n",
+       "4            4         water   \n",
+       "7            7     hamburger   \n",
+       "23          23         fizzy   \n",
+       "1            1      potatoes   \n",
+       "25          25        banana   \n",
+       "20          20    cheesecake   \n",
+       "18          18  cheeseburger   \n",
+       "14          14  french fries   \n",
+       "16          16          cake   \n",
+       "12          12        yellow   \n",
+       "0            0           red   \n",
+       "6            6        crispy   \n",
+       "17          17         brown   \n",
+       "9            9         green   \n",
+       "21          21         black   \n",
+       "5            5          blue   \n",
+       "24          24        carbon   \n",
+       "11          11      la croix   \n",
+       "\n",
+       "                                            embedding  similarities  \n",
+       "19  [-0.022529492154717445, -0.012672839686274529,...      0.960471  \n",
+       "10  [0.0009292512550018728, -0.019319288432598114,...      0.960471  \n",
+       "15  [-0.015675576403737068, -0.003918894100934267,...      0.922988  \n",
+       "22  [-0.012487593106925488, -0.026140518486499786,...      0.899328  \n",
+       "8   [-0.0007566261338070035, -0.0194522924721241, ...      0.895389  \n",
+       "3   [-0.0032554215285927057, -0.008896322920918465...      0.884663  \n",
+       "13  [0.0015591585543006659, -0.013005273416638374,...      0.883418  \n",
+       "2   [0.02582913264632225, -0.007537944242358208, -...      0.874203  \n",
+       "4   [0.019031280651688576, -0.01257743313908577, 0...      0.866025  \n",
+       "7   [-0.013206875883042812, -0.0018223668448626995...      0.852707  \n",
+       "23  [-0.013046763837337494, -0.010264534503221512,...      0.851090  \n",
+       "1   [0.005025846417993307, -0.031079445034265518, ...      0.850823  \n",
+       "25  [-0.013975119218230247, -0.03290277719497681, ...      0.848958  \n",
+       "20  [0.011245746165513992, -0.012743037194013596, ...      0.846989  \n",
+       "18  [-0.01824556663632393, 0.00504859397187829, 0....      0.846296  \n",
+       "14  [0.0014257068978622556, -0.016548126935958862,...      0.842877  \n",
+       "16  [-0.013669422827661037, -0.016827937215566635,...      0.842138  \n",
+       "12  [0.007648157421499491, -0.024834953248500824, ...      0.837775  \n",
+       "0   [1.8579006791696884e-05, -0.024676261469721794...      0.836119  \n",
+       "6   [-0.0010056837927550077, -0.005415474995970726...      0.835187  \n",
+       "17  [-0.0033683686051517725, -0.015937969088554382...      0.829839  \n",
+       "9   [0.01538460049778223, -0.010931522585451603, 0...      0.828841  \n",
+       "21  [-0.015190942212939262, -0.031228860840201378,...      0.824785  \n",
+       "5   [0.005379225127398968, -0.007354139816015959, ...      0.822992  \n",
+       "24  [0.004404176026582718, -0.025098854675889015, ...      0.816680  \n",
+       "11  [-0.012410983443260193, -0.01145013328641653, ...      0.802982  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "food_df[\"similarities\"] = food_df['embedding'].apply(lambda x: cosine_similarity(x, milk_espresso_vector))\n",
+    "food_df.sort_values(\"similarities\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "        vertical-align: top;\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Thank you, Brett. To start, I want to outline ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>With that context, this quarter, the Microsoft...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>It helps them align their spend with demand an...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>We are the platform of choice for customers' S...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Now to data and AI. With our Microsoft Intelli...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Other income and expense should be roughly $10...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>And finally, as a reminder, for Q2 cash flow, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>And FX should decrease COGS and operating expe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>With the high margins in our Windows OEM busin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>And while we continue to help our customers do...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 text\n",
+       "0   Thank you, Brett. To start, I want to outline ...\n",
+       "1   With that context, this quarter, the Microsoft...\n",
+       "2   It helps them align their spend with demand an...\n",
+       "3   We are the platform of choice for customers' S...\n",
+       "4   Now to data and AI. With our Microsoft Intelli...\n",
+       "..                                                ...\n",
+       "57  Other income and expense should be roughly $10...\n",
+       "58  And finally, as a reminder, for Q2 cash flow, ...\n",
+       "59  And FX should decrease COGS and operating expe...\n",
+       "60  With the high margins in our Windows OEM busin...\n",
+       "61  And while we continue to help our customers do...\n",
+       "\n",
+       "[62 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "earnings_df = pd.read_csv('microsoft-earnings.csv')\n",
+    "earnings_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earnings_df['embedding'] = earnings_df['text'].apply(lambda x: get_embedding(x, engine='text-embedding-ada-002'))\n",
+    "earnings_df.to_csv('earnings-embeddings.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earnings_search = input(\"Search earnings for a sentence:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earnings_search_vector = get_embedding(earnings_search, engine=\"text-embedding-ada-002\")\n",
+    "earnings_search_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>embedding</th>\n",
+       "      <th>similarities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Thank you, Brett. To start, I want to outline ...</td>\n",
+       "      <td>[-0.009504559449851513, -0.003731543431058526,...</td>\n",
+       "      <td>0.737052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>With that context, this quarter, the Microsoft...</td>\n",
+       "      <td>[-0.0016425022622570395, -0.028921114280819893...</td>\n",
+       "      <td>0.792230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>It helps them align their spend with demand an...</td>\n",
+       "      <td>[0.008895465172827244, -0.031935952603816986, ...</td>\n",
+       "      <td>0.789605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>We are the platform of choice for customers' S...</td>\n",
+       "      <td>[0.012019618414342403, -0.024120725691318512, ...</td>\n",
+       "      <td>0.790901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Now to data and AI. With our Microsoft Intelli...</td>\n",
+       "      <td>[-0.004754434805363417, 0.0038801338523626328,...</td>\n",
+       "      <td>0.815737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Other income and expense should be roughly $10...</td>\n",
+       "      <td>[-0.01832527294754982, -0.014160438440740108, ...</td>\n",
+       "      <td>0.685018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>And finally, as a reminder, for Q2 cash flow, ...</td>\n",
+       "      <td>[-0.012947804294526577, -0.010494815185666084,...</td>\n",
+       "      <td>0.710533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>And FX should decrease COGS and operating expe...</td>\n",
+       "      <td>[0.0009612650028429925, -0.01565629616379738, ...</td>\n",
+       "      <td>0.761581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>With the high margins in our Windows OEM busin...</td>\n",
+       "      <td>[0.010544494725763798, -0.03846913203597069, -...</td>\n",
+       "      <td>0.747335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>And while we continue to help our customers do...</td>\n",
+       "      <td>[-0.014238400384783745, -0.011069077998399734,...</td>\n",
+       "      <td>0.740366</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 text  \\\n",
+       "0   Thank you, Brett. To start, I want to outline ...   \n",
+       "1   With that context, this quarter, the Microsoft...   \n",
+       "2   It helps them align their spend with demand an...   \n",
+       "3   We are the platform of choice for customers' S...   \n",
+       "4   Now to data and AI. With our Microsoft Intelli...   \n",
+       "..                                                ...   \n",
+       "57  Other income and expense should be roughly $10...   \n",
+       "58  And finally, as a reminder, for Q2 cash flow, ...   \n",
+       "59  And FX should decrease COGS and operating expe...   \n",
+       "60  With the high margins in our Windows OEM busin...   \n",
+       "61  And while we continue to help our customers do...   \n",
+       "\n",
+       "                                            embedding  similarities  \n",
+       "0   [-0.009504559449851513, -0.003731543431058526,...      0.737052  \n",
+       "1   [-0.0016425022622570395, -0.028921114280819893...      0.792230  \n",
+       "2   [0.008895465172827244, -0.031935952603816986, ...      0.789605  \n",
+       "3   [0.012019618414342403, -0.024120725691318512, ...      0.790901  \n",
+       "4   [-0.004754434805363417, 0.0038801338523626328,...      0.815737  \n",
+       "..                                                ...           ...  \n",
+       "57  [-0.01832527294754982, -0.014160438440740108, ...      0.685018  \n",
+       "58  [-0.012947804294526577, -0.010494815185666084,...      0.710533  \n",
+       "59  [0.0009612650028429925, -0.01565629616379738, ...      0.761581  \n",
+       "60  [0.010544494725763798, -0.03846913203597069, -...      0.747335  \n",
+       "61  [-0.014238400384783745, -0.011069077998399734,...      0.740366  \n",
+       "\n",
+       "[62 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "earnings_df[\"similarities\"] = earnings_df['embedding'].apply(lambda x: cosine_similarity(x, earnings_search_vector))\n",
+    "\n",
+    "earnings_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>embedding</th>\n",
+       "      <th>similarities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Cosmos DB now supports postscript SQL, making ...</td>\n",
+       "      <td>[-0.00441406574100256, -0.005979578942060471, ...</td>\n",
+       "      <td>0.842245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Our cloud for sustainability is off to a fast ...</td>\n",
+       "      <td>[0.008848659694194794, -0.016215553507208824, ...</td>\n",
+       "      <td>0.818365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Now to data and AI. With our Microsoft Intelli...</td>\n",
+       "      <td>[-0.004754434805363417, 0.0038801338523626328,...</td>\n",
+       "      <td>0.815737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>All up more than 400,000 organizations now use...</td>\n",
+       "      <td>[-0.0010528546990826726, -0.020452534779906273...</td>\n",
+       "      <td>0.803985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Power Automate has more than seven million mon...</td>\n",
+       "      <td>[-0.025379547849297523, -0.03403877094388008, ...</td>\n",
+       "      <td>0.800751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Thank you, Satya, and good afternoon, everyone...</td>\n",
+       "      <td>[0.013616200536489487, -0.01455166470259428, -...</td>\n",
+       "      <td>0.712389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>And finally, as a reminder, for Q2 cash flow, ...</td>\n",
+       "      <td>[-0.012947804294526577, -0.010494815185666084,...</td>\n",
+       "      <td>0.710533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Operating expenses increased 2% and 5% in cons...</td>\n",
+       "      <td>[0.017512913793325424, 0.008054900914430618, 0...</td>\n",
+       "      <td>0.707797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Other income and expense should be roughly $10...</td>\n",
+       "      <td>[-0.01832527294754982, -0.014160438440740108, ...</td>\n",
+       "      <td>0.685018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>This quarter, other income and expense was $54...</td>\n",
+       "      <td>[-0.030022066086530685, -0.006907156202942133,...</td>\n",
+       "      <td>0.672180</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 text  \\\n",
+       "5   Cosmos DB now supports postscript SQL, making ...   \n",
+       "12  Our cloud for sustainability is off to a fast ...   \n",
+       "4   Now to data and AI. With our Microsoft Intelli...   \n",
+       "11  All up more than 400,000 organizations now use...   \n",
+       "9   Power Automate has more than seven million mon...   \n",
+       "..                                                ...   \n",
+       "29  Thank you, Satya, and good afternoon, everyone...   \n",
+       "58  And finally, as a reminder, for Q2 cash flow, ...   \n",
+       "44  Operating expenses increased 2% and 5% in cons...   \n",
+       "57  Other income and expense should be roughly $10...   \n",
+       "46  This quarter, other income and expense was $54...   \n",
+       "\n",
+       "                                            embedding  similarities  \n",
+       "5   [-0.00441406574100256, -0.005979578942060471, ...      0.842245  \n",
+       "12  [0.008848659694194794, -0.016215553507208824, ...      0.818365  \n",
+       "4   [-0.004754434805363417, 0.0038801338523626328,...      0.815737  \n",
+       "11  [-0.0010528546990826726, -0.020452534779906273...      0.803985  \n",
+       "9   [-0.025379547849297523, -0.03403877094388008, ...      0.800751  \n",
+       "..                                                ...           ...  \n",
+       "29  [0.013616200536489487, -0.01455166470259428, -...      0.712389  \n",
+       "58  [-0.012947804294526577, -0.010494815185666084,...      0.710533  \n",
+       "44  [0.017512913793325424, 0.008054900914430618, 0...      0.707797  \n",
+       "57  [-0.01832527294754982, -0.014160438440740108, ...      0.685018  \n",
+       "46  [-0.030022066086530685, -0.006907156202942133,...      0.672180  \n",
+       "\n",
+       "[62 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "earnings_df.sort_values(\"similarities\", ascending=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/conversationlangchain.ipynb b/conversationlangchain.ipynb
new file mode 100644
index 0000000..077250d
--- /dev/null
+++ b/conversationlangchain.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install google-search-results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "#os.environ[\"SERPAPI_API_KEY\"] = \"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mOpenAI\u001b[0m\n",
+      "Params: {'model_name': 'text-davinci-003', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1, 'request_timeout': None, 'logit_bias': {}}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find the temperature first, then use the calculator to raise it to the .023 power.\n",
+      "Action: Search\n",
+      "Action Input: \"High temperature in SF yesterday\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mHigh: 62.6ºf @3:10 PM Low: 44.06ºf @5:56 AM Approx. Precipitation / Rain Total: in. 1hr.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to use the calculator to raise 62.6 to the .023 power\n",
+      "Action: Calculator\n",
+      "Action Input: 62.6^.023\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 1.0998189786478374\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0998189786478374.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0998189786478374.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "OpenAI.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
+    "# First, let's load the language model we're going to use to control the agent.\n",
+    "llm = OpenAI(temperature=0)\n",
+    "print(llm)\n",
+    "# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+    "\n",
+    "\n",
+    "# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
+    "\n",
+    "# Now let's test it out!\n",
+    "agent.run(\"What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\n",
+      "Current conversation:\n",
+      "\n",
+      "Human: Hi there!\n",
+      "AI:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Hello! How can I assist you today?'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, ConversationChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0,model_name=\"gpt-3.5-turbo\")\n",
+    "conversation = ConversationChain(llm=llm, verbose=True)\n",
+    "\n",
+    "conversation.predict(input=\"Hi there!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: Hi there!\n",
+      "AI: Hello! How can I assist you today?\n",
+      "Human: Just having a conversation with an AI.\n",
+      "AI:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Great! I'm always happy to chat. Is there anything specific you'd like to talk about?\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"Just having a conversation with an AI.\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CHAT MODELS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import (\n",
+    "    AIMessage,\n",
+    "    HumanMessage,\n",
+    "    SystemMessage\n",
+    ")\n",
+    "\n",
+    "chat = ChatOpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "    HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: That model is currently overloaded with other requests. You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID f916d3d53c453210d7bd304f593af697 in your message.).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_messages = [\n",
+    "    [\n",
+    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "        HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
+    "    ],\n",
+    "    [\n",
+    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "        HumanMessage(content=\"Translate this sentence from English to French. I love artificial intelligence.\")\n",
+    "    ],\n",
+    "]\n",
+    "result = chat.generate(batch_messages)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.llm_output['token_usage']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/earnings-embeddings.csv b/earnings-embeddings.csv
new file mode 100644
index 0000000..d693500
--- /dev/null
+++ b/earnings-embeddings.csv
@@ -0,0 +1,63 @@
+,text,embedding
+0,"Thank you, Brett. To start, I want to outline the principles that are guiding us through these changing economic times. First, we will invest behind categories that will drive the long-term secular trend where digital technology as a percentage of world's GDP will continue to increase.Second, we'll prioritize helping our customers get the most value out of their digital spending, so that they can do more with less. And finally, we will be disciplined in managing our cost structure.","[-0.009504559449851513, -0.003731543431058526, 0.006251590326428413, -0.021994352340698242, 0.012168512679636478, 0.01847364939749241, -0.017402714118361473, 0.0010993829928338528, -0.0003465900954324752, -0.01829962246119976, 0.013500490225851536, 0.03614409640431404, -0.005137147381901741, -0.004792439751327038, 0.011003869585692883, 0.00783122144639492, 0.018727997317910194, -0.0015227374387905002, 0.023225929588079453, -0.00789815466850996, -0.01962490752339363, -0.016639672219753265, -0.010026640258729458, -0.0037650102749466896, -0.0025150268338620663, -0.022864488884806633, 0.006536057684570551, 0.0023594063241034746, -0.0028865078929811716, -0.009886080399155617, -0.0005626598722301424, -0.006505937781184912, -0.009009251371026039, 0.008962397463619709, -0.010046720504760742, -0.012074805796146393, -0.012061419896781445, 0.01989264041185379, 0.0175633542239666, -0.0232527032494545, 0.013266222551465034, 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+1,"With that context, this quarter, the Microsoft Cloud again exceeded $25 billion in quarterly revenue, up 24% and 31% in constant currency. And based on current trends continuing, we expect our broader commercial business to grow at around 20% in constant currency this fiscal year, as we manage through the cyclical trends affecting our consumer business. With that, let me highlight our progress starting with Azure. Moving to the cloud is the best way for organizations to do more with less today.","[-0.0016425022622570395, -0.028921114280819893, 0.0223837923258543, -0.010283208452165127, 0.023495135828852654, -0.01826527900993824, -0.023730479180812836, -0.04259719327092171, -0.012048285454511642, -0.040165308862924576, 0.03838715702295303, 0.03475240617990494, 0.0027326007839292288, -0.0014349422417581081, -0.009570639580488205, 0.010793118737637997, 0.013584556058049202, -0.004831081256270409, -0.0022194208577275276, -0.023939674720168114, -0.04149892181158066, 0.004111975897103548, -0.030699266120791435, -0.001670285826548934, -0.020932506769895554, 0.014460557140409946, 0.020932506769895554, -0.013754526153206825, 0.031143803149461746, -0.016970889642834663, -0.005475007463246584, -0.024083495140075684, -0.014120616018772125, 0.01928510144352913, -0.022906778380274773, -0.012316315434873104, -0.007779413368552923, 0.011629896238446236, 0.013741451315581799, -0.0003601656062528491, 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+2,"It helps them align their spend with demand and mitigate risk around increasing energy costs and supply chain constraints. We're also seeing more customers turn to us to build and innovate with infrastructure they already have. With Azure Arc, organizations like Wells Fargo can run Azure services, including containerized applications across on-premises, edge, and multi-cloud environments. We now have more than 8,500 Arc customers, more than double the number a year ago.","[0.008895465172827244, -0.031935952603816986, 0.00646942900493741, 0.0021210680715739727, -0.023410562425851822, 0.010691005736589432, -0.02323237806558609, -0.020833754912018776, 0.0018863457953557372, -0.016721829771995544, 0.031634412705898285, 0.019929131492972374, -0.0015042793238535523, -0.0016619031084701419, -0.024013644084334373, 0.01562531664967537, 0.005835507530719042, -0.022999368607997894, -0.0023832034785300493, -0.01606392115354538, -0.03799419105052948, -0.016296930611133575, -0.004728714004158974, -0.006106209009885788, -0.03810384124517441, 0.013069069012999535, 0.0023660704027861357, -0.005273544229567051, 0.014994820579886436, -0.016269518062472343, 0.01722896657884121, -0.024740083143115044, -0.0002475721703376621, -0.00598627794533968, -0.015666436403989792, -0.0020422562956809998, 0.018078764900565147, -0.003041111398488283, 0.007051951717585325, -0.020381443202495575, 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+3,"We are the platform of choice for customers' SAP workloads in the cloud, companies like Thabani, Munich Re's, Sodexo, Volvo Cars, all run SAP on Azure. We are the only cloud provider with direct and secure access to Oracle databases running an Oracle Cloud infrastructure, making it possible for companies like FedEx, GE, and Marriott to use capabilities from both companies. And with Azure Confidential Computing, we're enabling companies in highly regulated industries, including RBC, to bring their most sensitive applications to the cloud. Just last week, UBS said it will move more than 50% of its applications to Azure.","[0.012019618414342403, -0.024120725691318512, 0.010878773406147957, -0.022667506709694862, -0.016324950382113457, 0.03063984028995037, -0.04017404839396477, -0.02443310059607029, 0.0065666502341628075, -0.03251408785581589, 0.02960764802992344, 0.005632922984659672, 0.02481338195502758, -0.004923290107399225, -0.01876961812376976, 0.0060471585020422935, 0.015944669023156166, -0.0050353375263512135, -6.52017624815926e-05, -0.02955332212150097, -0.026198694482445717, 0.004722963087260723, -0.012576459906995296, 0.018701711669564247, -0.029634810984134674, 0.027923543006181717, 0.019054830074310303, -0.011951711028814316, 0.00511003565043211, -0.021485917270183563, -0.0037077467422932386, -0.013459255918860435, -0.00938480906188488, 0.00644441694021225, 0.00096598343225196, 0.009289738722145557, -0.0014379402855411172, 0.0061863684095442295, -0.012440644204616547, 5.1699851610464975e-05, 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+4,"Now to data and AI. With our Microsoft Intelligent Data Platform, we provide a complete data fabric, helping customers cut integration tax associated with bringing together siloed solutions. Customers like Mercedes-Benz are standardizing on our data stack to process and govern massive amounts of data. Cosmos DB is the go-to database powering the world's most demanding workloads at limitless scale.","[-0.004754434805363417, 0.0038801338523626328, -0.013880373910069466, -0.03773726895451546, -0.027530312538146973, 0.028519831597805023, -0.031529054045677185, -0.007800932973623276, 0.011657346040010452, -0.02534794993698597, 0.01522232498973608, 0.024670196697115898, 0.00017123844008892775, -0.004374893382191658, -0.013453390449285507, 0.023640012368559837, 0.019288839772343636, 0.009746083058416843, 0.0005887976149097085, -0.010905040428042412, -0.002960084704682231, -0.02218962274491787, 0.008736231364309788, 0.01890929974615574, -0.02105099707841873, 0.006543701980262995, 0.02258271910250187, -0.02736765146255493, 0.020129254087805748, -0.02511751279234886, 0.023477351292967796, -0.019397281110286713, -0.0008980222628451884, 0.00045621226308867335, -0.0007150290766730905, 0.010437390767037868, 0.014056590385735035, 0.010247619822621346, 0.006201436743140221, -0.007692492567002773, 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+5,"Cosmos DB now supports postscript SQL, making Azure the first cloud provider to offer a database service that supports both relational and no SQL workloads. And in AI, we are turning the world's most advanced models into platforms for customers. Earlier this month, we brought the power of DALL-E to Azure OpenAI service, helping customers like Mattel apply the breakthrough image generation model to commercial use cases for the first time. In Azure machine learning, provides industry-leading ML apps, helping organizations like 3M deploy, manage and govern models.","[-0.00441406574100256, -0.005979578942060471, -0.006983548868447542, -0.029676679521799088, -0.01683947443962097, 0.026014741510152817, -0.02692682296037674, 0.007099261041730642, 0.013388538733124733, -0.02201247401535511, 0.011135561391711235, 0.03209982067346573, -0.00024312245659530163, -0.007813951931893826, -0.02107316628098488, 0.011707314290106297, 0.012966531328856945, 0.006095290649682283, 0.004669312387704849, -0.0038491198793053627, -0.011155981570482254, 0.00030991199309937656, -0.002001133980229497, 0.005475892219692469, -0.009107201360166073, 0.025279629975557327, 0.016240494325757027, -0.015791261568665504, 0.02103232592344284, -0.0019926256500184536, 0.025402149185538292, -0.0013468515826389194, -0.0035904699470847845, -0.013184341602027416, -0.01246965117752552, -0.0013434482971206307, 0.016553597524762154, 0.021141231060028076, 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+6,"All of, Azure ML revenue has increased more than 100% for four quarters in a row. Now on to developers. We have the most complete platform for developers to build cloud-native applications. Four years since our acquisition, GitHub is now at $1 billion in annual recurring revenue.","[-0.012622406706213951, -0.023502416908740997, 0.00590885616838932, -0.014311601407825947, 0.01063394546508789, -0.015162849798798561, -0.01461751852184534, -0.012230034917593002, -0.01577468402683735, -0.03841255232691765, 0.03128335252404213, 0.01957869715988636, 0.013420451432466507, -0.00970954354852438, -0.015894390642642975, -0.004888023715466261, 0.011844313703477383, -0.003040882758796215, -0.011698005720973015, 0.002952765440568328, -0.026588190346956253, 0.008911498822271824, -0.02958085760474205, -0.01856783963739872, -0.014883534051477909, 0.03109714202582836, 0.0021131557878106833, -0.014564315788447857, 0.020988576114177704, 0.007920593954622746, 0.007388563826680183, -0.007056045345962048, 0.004605383146554232, 0.023249702528119087, -0.023169897496700287, -0.00017176666005980223, -0.009636390022933483, 0.02279747650027275, -0.0015919329598546028, 0.012436197139322758, 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+7,And GitHub's developer-first ethos has never been stronger. More than 90 million people now use the service to build software for any cloud on any platform up three times. GitHub advanced security is helping organizations improve their security posture by bringing features directly into the developer's workflow. Toyota North America chose the offering this quarter to help its developers build and secure many of its most critical applications.,"[-0.0005771665018983185, -0.019794421270489693, -0.007520240731537342, -0.03417918458580971, 0.003507396439090371, 0.00931662879884243, -0.033687397837638855, -0.005836553871631622, -0.009589843451976776, -0.03008096292614937, 0.022540230304002762, -0.008148634806275368, 0.004781261086463928, -0.03144703805446625, -0.015163428150117397, -0.009972344152629375, 0.019521206617355347, -0.027867920696735382, -0.005238896235823631, -0.014835570007562637, -0.012554225511848927, 0.00999966636300087, -0.016406556591391563, -0.0005861313547939062, -0.02394728735089302, 0.026515508070588112, -0.005911687854677439, -0.014412086457014084, 0.004924698732793331, -0.023141304031014442, 0.02662479318678379, -0.013469495810568333, -0.004371438641101122, 0.007233364973217249, -0.018182452768087387, -0.008333055302500725, 0.0022318244446069, -0.008374037221074104, 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+8,"Now on to Power Platform. We are helping customers save time and money with our end-to-end suite spanning Low-Code/No-Code tools, robotic process automation, virtual agents, and business intelligence. Power BI is the market leader in business intelligence in the cloud and is growing faster than competition, as companies like Walmart standardize on the tool for reporting and analytics. Power Apps is the market leader in Low-Code/No-Code tools and has nearly 15 million monthly active users, up more than 50% compared to a year ago.","[-0.02147563174366951, -0.013491101562976837, 0.0026441619265824556, -0.0061075943522155285, -0.016358548775315285, 0.007165259215980768, -0.015660153701901436, -0.034919723868370056, 0.01134219579398632, -0.04437491297721863, 0.015740737318992615, 0.01380672212690115, -0.004959272686392069, -0.003268687753006816, -0.008891099132597446, 0.01637197844684124, 0.003589344909414649, -0.004123213700950146, -0.006631390191614628, 0.008105405606329441, -0.010522925294935703, 0.011933145113289356, -0.0023067165166139603, 0.010160297155380249, -0.019434170797467232, 0.004982776008546352, 0.014182780869305134, -0.024188624694943428, 0.023678259924054146, -0.013262780383229256, 0.014693145640194416, -0.019071541726589203, -3.3812746551120654e-05, -0.002561899134889245, -0.01799708977341652, 0.007373434491455555, 0.004160148091614246, 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+9,"Power Automate has more than seven million monthly active users and is being used by companies like Brown-Forman, Komatsu, Mass, T-Mobile to digitize manual business processes and save thousands of hours of employee time. And we're going further with new AI-powered capabilities and power automate that turn natural language into advanced workflows. Now on to Dynamics 365. From customer experience and service to finance and supply chain, we continue to take share across all categories we serve.","[-0.025379547849297523, -0.03403877094388008, -0.010677897371351719, 0.0026694743428379297, -0.02086642012000084, 0.01878657378256321, -0.023503609001636505, -0.002829201053828001, -0.00022748304763808846, -0.03009658120572567, 0.011677038855850697, 0.00909422431141138, 0.004061135463416576, -0.01544251013547182, 0.0028224040288478136, 0.01184696052223444, 0.01723688654601574, -0.01195571105927229, 0.001147822942584753, -0.023449232801795006, -0.006208949256688356, 0.005478284787386656, -0.003850432112812996, 0.007157113868743181, -0.029226580634713173, 0.012499460950493813, 0.017644697800278664, -0.03632252290844917, 0.02234813943505287, -0.02533876523375511, 0.002499552443623543, -0.012213991954922676, -0.009026255458593369, 0.0031146700493991375, -0.005128245335072279, 0.015727978199720383, 0.008387348614633083, 0.016829073429107666, 0.016394073143601418, 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+10,"For example, Lufthansa Cargo chose us to centralize customer information and related shipments. CBRE is optimizing its field service operations, gaining cost efficiencies. Darden is using our solutions to increase both guest frequency and spend at its restaurants. And Tillamook is scaling its growth and improving supply chain visibility.","[0.006830708123743534, -0.010010579600930214, -0.00037234576302580535, -0.017477260902523994, -0.0056068869307637215, 0.010161838494241238, -0.019484877586364746, 0.0030509578064084053, 0.008360483683645725, -0.03033425658941269, -0.000996073242276907, 0.01892109587788582, 0.008236726745963097, 0.0007683257572352886, 0.006311615463346243, 0.012540725991129875, 0.02345885895192623, -0.021519996225833893, 0.00793420895934105, -0.019938653334975243, -0.021272482350468636, -0.04138989746570587, 0.00043938090675510466, 0.015785913914442062, -0.011578170582652092, 1.5201075257209595e-05, 0.03234187141060829, -0.02260630950331688, 0.02229003980755806, 0.002801724709570408, 0.011750055477023125, 0.010986885987222195, -0.010498732328414917, 0.012403218075633049, -0.012980751693248749, -0.018426066264510155, 0.013620163314044476, -0.015400891192257404, 0.020804954692721367, -0.015552150085568428, 0.006040037143975496, 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+11,"All up more than 400,000 organizations now use our business applications. Now on to Industry Solutions. We are seeing increased adoption of our industry and cross-industry clouds. Bank of Queensland chose our cloud for financial services to deliver new digital experiences for its customers.","[-0.0010528546990826726, -0.020452534779906273, 0.006680888123810291, 0.0037400678265839815, -0.0237861480563879, 0.016968635842204094, -0.0066672260873019695, -0.0234855767339468, -0.0016437512822449207, -0.030549010261893272, -0.007179563865065575, -0.0008854909683577716, 0.026914825662970543, -0.005946537014096975, -0.020274924114346504, 0.03057633526623249, 0.014140530489385128, -0.012685490772128105, -0.010909385047852993, -0.02583550103008747, -0.020220275968313217, 0.012193646281957626, -0.015192531049251556, 0.0005328315892256796, -0.015698038041591644, 0.00985055323690176, 0.007521122694015503, -0.018539806827902794, 0.01071128062903881, 0.0023806642275303602, 0.008409175090491772, 0.020725782960653305, -0.01292458176612854, 0.016982298344373703, -0.0068653300404548645, -0.0330628827214241, 0.00043762210407294333, 0.008942007087171078, -9.430222416995093e-05, 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+12,"Our cloud for sustainability is off to a fast start as organizations like Telstra use the solution to track their environmental footprint. New updates provide insights on hard-to-measure Scope 3 carbon emissions, and we are seeing record growth in healthcare, driven, in part, by our Nuance DAX ambient intelligence solutions, which automatically documents patient encounters at the point of care. Physicians tell us DAX dramatically improves their productivity, and it's quickly becoming an on-ramp to our broader healthcare offerings. Now on to new systems of work, Microsoft 365, Teams, and Viva uniquely enable employees to thrive in today's digitally connected distributed world of work.","[0.008848659694194794, -0.016215553507208824, 0.0044627720490098, -0.017291931435465813, -0.008946511894464493, 0.033521462231874466, -0.03354942053556442, -0.03027835115790367, -0.01376924104988575, -0.024924423545598984, 0.021541524678468704, 0.0138181671500206, 9.190051059704274e-05, 0.0020549020264297724, -0.005720875225961208, 0.0007509303977712989, 0.02379213087260723, -0.02762235701084137, 0.003430078737437725, -0.026713727042078972, -0.043474458158016205, -0.005448285955935717, -0.009512658230960369, 0.029719194397330284, -0.011993917636573315, -0.002399133052676916, 0.01096646673977375, -0.003106816206127405, 0.009890089742839336, -0.015782207250595093, 0.013482673093676567, -0.01582414284348488, -0.006671442184597254, -0.004735360853374004, -0.008079818449914455, 0.01884359121322632, -0.0022209016606211662, 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+13,"Microsoft 365 is the cloud-first platform that supports all the ways people work and every type of worker reducing cost and complexity for IT. The new Microsoft 365 app brings together our productivity apps with third-party content, as well as personalized recommendations. Microsoft Teams is the de facto standard for collaboration and has become essential to how hundreds of millions of people meet, call, chat, collaborate and do business. As we emerge from the pandemic, we are retaining users we have gained and are seeing increased engagement, too.","[-0.011315170675516129, -0.020855411887168884, -0.017971152439713478, -0.0243139136582613, 0.009866515174508095, 0.013520780019462109, -0.013651289977133274, -0.01974608190357685, -0.003455237951129675, -0.019863540306687355, 0.006023011635988951, 0.010492960922420025, -0.008691930212080479, 0.008770235814154148, -0.02772020921111107, 0.008437437005341053, 0.023256786167621613, -0.021521009504795074, 0.002670549787580967, -0.03338431939482689, -0.02440526895225048, 0.00253025209531188, 0.012665943242609501, -0.003314940258860588, -0.030878538265824318, 0.012267889454960823, 0.013964512385427952, -0.018702005967497826, 0.04147590696811676, -0.014382142573595047, 0.01207212544977665, -0.00709971459582448, 0.005771780852228403, 0.0006884374306537211, -0.005080080591142178, 0.0006549943354912102, 0.005500973667949438, -0.002406268147751689, 0.007497768383473158, 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+14,"Users interact with Teams 1,500 times per month on average. In a typical day, the average commercial user spends more time in Teams chat than they do in email, and the number of users who use four or more features within Teams increased over 20% year over year. Teams is becoming a ubiquitous platform for business process. Monthly active enterprise users running third party and custom applications within Teams increased nearly 60% year over year, and over 55% of our enterprise customers who use Teams today also buy Teams Rooms or Teams Phone.","[-0.013855385594069958, -0.014995800331234932, -0.01472052838653326, -0.020566793158650398, 0.012754295952618122, -0.017630552873015404, -0.01090603694319725, -0.024184660986065865, -0.0018843063153326511, -0.01866610161960125, -0.006180524360388517, 0.016857167705893517, -0.006770394276827574, -0.01361943781375885, -0.010165422223508358, -0.004686187952756882, 0.006537723354995251, 0.002015388337895274, 0.006855597719550133, -0.01898069866001606, -0.03292784094810486, -0.002538078697398305, -0.02894294448196888, 0.014890935271978378, -0.0389576219022274, 0.01034238375723362, 0.019242864102125168, -0.02037017047405243, 0.007628982421010733, -0.018023798242211342, 0.017932040616869926, -0.016503246501088142, -0.018456369638442993, 0.0033049092162400484, 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+15,"Teams Phone provides the best-in-class calling. PSTN users have grown by double digits for five quarters in a row. We are bringing Teams Rooms to a growing hardware ecosystem, including Cisco's devices and peripherals, which will now run Teams natively. And we are creating a new category with Microsoft Places to help organizations evolve and manage the space for hybrid and in-person work.","[0.005196182522922754, -0.016121840104460716, -0.0032595789525657892, -0.011445275507867336, -0.003999693319201469, 0.01356477104127407, 0.0030048976186662912, -0.0066046216525137424, -0.0044988002628088, -0.012874226085841656, -0.0017443106044083834, 0.020456550642848015, -0.004286850802600384, -0.00040210591396316886, -0.00428343191742897, 7.878668839111924e-06, 0.008375425823032856, 0.0031484763603657484, 0.022425631061196327, -0.0155065031722188, -0.008970252238214016, -0.005493595730513334, 0.004382569808512926, 0.0038492772728204727, -0.023068316280841827, 0.009688145481050014, 0.00122725625988096, -0.0033826464787125587, 0.02243930473923683, -0.010337668471038342, 0.012361444532871246, -0.006960149854421616, -0.020046325400471687, -0.02070268616080284, -0.02587151899933815, -0.006960149854421616, 0.010385528206825256, -0.002341700717806816, 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+16,"Just like Outlook calendar orchestrates when people can meet and collaborate, Places will do the same for where. We also announced Teams Premium, addressing enterprise demands for advanced meeting features like additional security options and intelligent meeting recaps. All this innovation is driving growth across Microsoft 365. Leaders in every industry from Fannie Mae and Land O'Lakes to Rabobank continue to turn to our premium E5 offerings for advanced security, compliance, voice, and analytics.","[-0.023598868399858475, -0.03185022249817848, -0.00901460275053978, -0.01804295741021633, -0.019418183714151382, 0.01673649251461029, -0.02827463485300541, -0.01291336677968502, -0.011716920882463455, -0.02951233834028244, 0.02364012412726879, 0.0012892738450318575, -0.012177621014416218, 0.0013210759498178959, -0.0046070050448179245, -0.00462763337418437, 0.014701159670948982, -0.029182283207774162, -0.0002514084044378251, -0.024148957803845406, -0.006535758730024099, -0.008010688237845898, 0.01116683054715395, 0.004383530933409929, -0.011118697933852673, 0.004971439950168133, 0.009406541474163532, -0.018496781587600708, 0.01919814758002758, 0.0011878509540110826, 0.021536029875278473, -0.011104945093393326, -0.0042081899009644985, -0.020683391019701958, -0.011930080130696297, -0.012459542602300644, 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+17,"We've also built a completely new suite for our employee experience platform, Microsoft Viva, which now has more than 20 million monthly active users at companies like Finastra, SES, and Unilever. And we are extending Viva to meet role-specific needs. Viva Sales is helping salespeople at companies like Adobe, Crayon, and PwC reclaim their time by bringing customer interactions across Teams and Outlook directly into their CRM system. Now on to Windows.","[-0.03199895843863487, -0.02947564609348774, -0.0066167693585157394, -0.011230138130486012, -0.032442618161439896, 0.004492055159062147, -0.043062724173069, -0.029614288359880447, 0.004956511314958334, -0.016290631145238876, 0.008082926273345947, 0.007368911523371935, 0.015126025304198265, -0.01473782304674387, -0.007854164578020573, -0.008117587305605412, 0.006173109635710716, -0.03718423470854759, 0.0009453765815123916, -0.04134354367852211, -0.00235520931892097, -0.002382938051596284, 0.004637631122022867, 0.0012928522191941738, -0.030751166865229607, -0.013150352984666824, -0.0023326799273490906, -0.01656791940331459, 0.023098036646842957, -0.020602449774742126, 0.003102152142673731, -0.010536919347941875, -0.010523054748773575, 0.0031940035987645388, -0.020131060853600502, 0.0052337986417114735, -0.010141785256564617, 0.010627037845551968, 0.015153753571212292, -0.003289321204647422, 0.007972011342644691, 0.012380880303680897, 0.0025718400720506907, 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+18,"Despite the drop in PC shipments during the quarter, Windows continues to see usage growth. All up, there are nearly 20% more monthly active Windows devices than pre-pandemic. And on average, Windows 10 and Windows 11 users are spending 8.5% more time on their PCs than they were two and a half years ago. And we are seeing larger commercial deployments of Windows 11.","[0.009205316193401814, -0.01999434269964695, 0.005942213349044323, -0.03365384414792061, 0.010789027437567711, -0.016312217339873314, -0.03716440498828888, -0.018885746598243713, -0.007898755371570587, -0.01652337796986103, 0.022488687187433243, 0.032967571169137955, -0.023386122658848763, 0.02813725359737873, -0.008360670879483223, 0.020086726173758507, 0.012352940626442432, 0.005740950349718332, 0.016721341758966446, -0.022013574838638306, -0.012630090117454529, -0.009581447578966618, -0.015005655586719513, 0.009145927615463734, -0.023386122658848763, 0.023927224799990654, 0.01599547453224659, -0.01588989421725273, 0.023518098518252373, -0.01504524890333414, 0.0031311274506151676, -0.024851055815815926, -0.02024509757757187, 0.0010294117964804173, -0.028322020545601845, 0.0027467478066682816, -0.0020802696235477924, 0.005856429226696491, 0.014504147693514824, -0.0027335500344634056, 0.028559576719999313, 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+19,"Accenture, for example, has deployed Windows 11 to more than 450,000 employees' PCs, up from just 25,000, seven months ago, and L'Oreal has deployed the operating system to 85,000 employees. Now to security. Security continues to be a top priority for every organization. We're the only company with integrated end-to-end tools spanning security, compliance, identity and device management, and privacy across all clouds and platforms.","[0.01380225084722042, -0.027254054322838783, 0.003672962309792638, -0.028440184891223907, 0.002205462194979191, 0.026553159579634666, -0.03844142705202103, -0.008909461088478565, -0.00077460752800107, -0.023978715762495995, 0.035125650465488434, 0.004033518955111504, -0.015365786850452423, -0.0027530367951840162, -0.027065351605415344, 0.019881172105669975, 0.009704708121716976, -0.01713150553405285, -0.004437881987541914, -0.022199518978595734, -0.03183683380484581, -0.016538439318537712, -0.0007181652472354472, 0.01918027736246586, -0.018075017258524895, -0.003807749832049012, 0.017697613686323166, -0.022967807948589325, 0.03337341174483299, -0.02622966840863228, 0.014462710358202457, -0.008457922376692295, -0.006129466462880373, 0.015702756121754646, -0.013627027161419392, 0.00799290556460619, 0.006102508865296841, 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+20,"More than 860,000 organizations across every industry from BP and Fuji Film to ING Bank, iHeartMedia, and Lumen Technologies now use our security solutions, up 33% year over year. They can save up to 60% when they consolidate our security stack, and the number of customers with more than four workloads have increased 50% year over year. More organizations are choosing both our XDR and cloud-native SIM to secure their entire digital estate. The number of E5 customers who also purchased Sentinel increased 44% year over year.","[0.011292602866888046, -0.0127616161480546, -0.012316038832068443, -0.03848675265908241, -0.013583149760961533, 0.031246118247509003, -0.028168847784399986, -0.0032600255217403173, -0.001530802226625383, -0.009809665381908417, 0.01675092615187168, 0.025356141850352287, 0.012622373178601265, -0.006906450260430574, -0.010192584246397018, 0.028711896389722824, 0.006610558833926916, 0.004619384650141001, 0.013137572444975376, -0.008347614668309689, -0.016235727816820145, -0.008410274051129818, -0.01291478332132101, 0.027054905891418457, -0.016500288620591164, -0.012580600567162037, 0.0031817012932151556, -0.007087466306984425, 0.00871660839766264, -0.008953321725130081, 0.012497054412961006, -0.013854673132300377, -0.00908560212701559, 0.015442042611539364, -0.015177481807768345, 0.002412384143099189, -0.0030250530689954758, 0.0020277253352105618, 0.02381054498255253, -0.027416937053203583, 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+21,"And as threats become more sophisticated, we are innovating to protect customers. New capabilities in Defender help secure the entire DevOps life cycle and manage security posture across clouds. And Entra now provides comprehensive identity governance for both on-premise and cloud-based user directories. Now on to LinkedIn.","[-0.009703855030238628, -0.017891043797135353, 0.007042667828500271, -0.04089381545782089, -0.006249226629734039, 0.0454157255589962, -0.03684937208890915, -0.006547644734382629, -0.00010686001769499853, -0.015419439412653446, 0.018354469910264015, 0.0028964111115783453, 0.019674530252814293, 0.00010428177483845502, 0.005964851472526789, 0.004514890722930431, 0.007674612104892731, -0.022146135568618774, -0.0033493046648800373, -0.026850609108805656, -0.015236877836287022, -0.012105243280529976, 0.0004182242264505476, -0.0005722606438212097, -0.021977616474032402, -0.008819133043289185, 0.010019827634096146, -0.009963653981685638, 0.014773452654480934, -0.0017439905786886811, 0.013116354122757912, 0.0011120463022962213, 0.002064351225271821, -0.018480857834219933, -0.0038548598531633615, -0.0080046271905303, 0.0019291853532195091, -0.00768865505233407, 0.018115734681487083, 0.010349842719733715, 0.014843668788671494, 0.009696833789348602, -0.0023469708394259214, 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+22,"We once again saw record engagement among our more than 875 million members, with international growth increasing at nearly 2x the pace as in the United States. There are now more than 150 million subscriptions to newsletters on LinkedIn, up 4x year over year. New integrations between Viva and LinkedIn Learning helped companies invest in their existing employees by providing access to courses directly in the flow of work. Members added 365 million skills to their profiles over the last 12 months, up 43% year over year.","[-0.04005807638168335, -0.01202403288334608, 0.02031988836824894, -0.0575355589389801, -0.026599619537591934, -0.00472632423043251, -0.03381800651550293, -0.001137788174673915, -0.0035397852770984173, -0.021483290940523148, -0.005701335147023201, 0.010351641103625298, 0.008824675343930721, 0.0009973205160349607, 0.013603881001472473, 0.007251436822116375, 0.006801940500736237, -0.025859272107481956, 0.0338708870112896, -0.02369111217558384, -0.02369111217558384, 0.012585903517901897, -0.0036455492954701185, 0.0004346235073171556, -0.02267313562333584, 0.013055230490863323, 0.005939303897321224, 0.0008043840061873198, 0.03204645961523056, -0.018654106184840202, -0.004250386729836464, 0.006881263572722673, -0.014806944876909256, 0.00760838994756341, -0.03143831714987755, -0.017926979809999466, -0.010450795292854309, -0.0156794972717762, 0.02958744950592518, 0.014436771161854267, 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+23,"And with our acquisition of EduBrite, they will also soon be able to earn professional certificates from trusted partners directly on the platform. We launched the next-generation sales navigator this quarter, helping sellers increase win rates and deal sizes by better understanding and evaluating customer interest. Finally, LinkedIn Marketing Solution continues to provide leading innovation and ROI in B2B digital advertising. More broadly, with Microsoft Advertising, we offer a trusted platform for any marketeer or advertiser looking to innovate.","[-0.03107352927327156, -0.014467155560851097, -0.004552697297185659, -0.019732926040887833, -0.036586131900548935, 0.011107483878731728, -0.04506073147058487, -0.003856765339151025, -0.009736189618706703, -0.015605329535901546, -0.00353451119735837, 0.007377563510090113, 0.012231945060193539, -0.0014012913452461362, -0.00036232167622074485, -0.006928464397788048, 0.004741250071674585, -0.035324543714523315, 0.007226720917969942, -0.013767794705927372, -0.03280135989189148, -0.007452984806150198, -0.006256530527025461, -0.015097950585186481, -0.014672849327325821, -0.01366494782269001, 0.01483740471303463, -0.01441230345517397, 0.007692961022257805, -0.006085118744522333, -0.00103018491063267, 0.011038918979465961, -0.01984262838959694, -0.006510220002382994, -0.006654205732047558, -0.0030425593722611666, -0.013685517013072968, 0.015523051843047142, 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+24,"We've expanded our geographies we serve by nearly 4x over the past year. We are seeing record daily usage of Edge, Start, and Bing driven by Windows. Edge is the fastest-growing browser on Windows and continues to gain share as people use built-in coupon price comparison features to save money. We surface more than $2 billion in savings to date.","[-0.005704060196876526, -0.006287779193371534, 0.01592084765434265, -0.020467711612582207, -0.008929875679314137, 0.007475698366761208, -0.039679232984781265, 0.002283672336488962, -0.0009071538224816322, -0.03236055746674538, 0.00125619035679847, 0.022215453907847404, -0.005881565622985363, 0.004423975013196468, -0.009469217620790005, 0.033562131226062775, 0.00798090547323227, -0.018160143867135048, -0.003812947543337941, -0.004441042896360159, -0.03342558816075325, -0.008206200785934925, 0.013176345266401768, -0.01462369505316019, -0.02946585789322853, 0.0015045269392430782, 0.03115898370742798, -0.008363224565982819, 0.008178891614079475, -0.010561557486653328, 0.0034954864531755447, -0.013189999386668205, -0.033343661576509476, 0.0023894926998764277, -0.015565837733447552, 0.0326063334941864, -0.019402680918574333, 0.008294953033328056, 0.020795412361621857, 0.008513420820236206, 0.016043735668063164, -0.004410320892930031, 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+25,"And this quarter, we brought our shopping tools to 15 new markets. Users of our Start, personalized content feed are consuming 2x more content compared to a year ago. And we're also expanding our third-party ad inventory. Netflix will launch its first ad-supported subscription plan next month, exclusively powered by our technology and sales.","[-0.002623418578878045, -0.017674950882792473, 0.0065187979489564896, -0.04176270589232445, 0.010758666321635246, -0.001026843092404306, -0.03373345360159874, -0.016190998256206512, -0.020576611161231995, -0.006561859045177698, 0.024365995079278946, 0.025001974776387215, 0.008671856485307217, -0.029997069388628006, 0.00775763439014554, -0.006502235773950815, 0.01832418143749237, -0.012845476157963276, 0.011918005533516407, -0.03108353540301323, 0.00036436368827708066, 0.022073814645409584, 0.008214744739234447, -0.008579108864068985, -0.008996470831334591, 0.00860560778528452, 0.018151937052607536, 0.005220338236540556, 0.0349259153008461, -0.009937191382050514, 0.016204247251152992, -0.002893378958106041, 0.0044220504350960255, -0.010679168626666069, -0.015475519932806492, -0.001975845079869032, -0.010824914090335369, 0.0008131935028359294, 0.01329596247524023, 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+26,And with PromoteIQ we offer an omnichannel media platform for retailers like the auto group looking to generate additional revenue while maintaining ownership of their own data and customer relationships. Now onto gaming. We are adding new gamers to our ecosystem as we execute on our ambition to reach players wherever and whenever they want on any device. We saw usage growth across all platforms driven by the strength of console.,"[-0.017536817118525505, -0.010471336543560028, -0.0029717760626226664, -0.037931881844997406, -0.03290991485118866, 0.0077132610604166985, -0.00570647744461894, -0.01654845103621483, 0.009149063378572464, -0.027433833107352257, 0.008962075226008892, 0.0006544585339725018, 0.007245790679007769, -0.0033958384301513433, -0.0038132225163280964, 0.011506449431180954, 0.017750518396496773, -0.0025410354137420654, 0.005309127736836672, 0.010851990431547165, -0.006918561644852161, 0.00787353701889515, -6.067723734304309e-05, -0.0007763347239233553, -0.021129660308361053, 0.0028799513820558786, 0.007052124477922916, -0.017657024785876274, 0.006240729708224535, -0.01725633442401886, 0.006985343061387539, -0.01323608960956335, -0.007719939108937979, -0.002313978271558881, -0.016788864508271217, -0.02401462011039257, 0.008588098920881748, 0.014731994830071926, 0.016495026648044586, -0.021102948114275932, 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+27,"PC Game Pass subscriptions increased 159% year over year. And with cloud gaming, we're transforming how games are distributed, played and viewed. More than 20 million people have used the service to stream games to date. And we are adding support for new devices like handhelds from Logitech and Razor as well as Meta Quest.","[-0.007176009006798267, -0.014091781340539455, 0.008822001516819, -0.020714789628982544, -0.005559292621910572, 0.008379600942134857, -0.015405973419547081, -0.03362249583005905, 0.011704116128385067, -0.029458720237016678, 0.022731618955731392, 0.009602709673345089, 0.004430518485605717, -0.013805522583425045, 0.010441971011459827, 0.01392262801527977, 0.018372664228081703, 0.0009604336228221655, 0.023746538907289505, -0.014365029521286488, 0.002176224021241069, -0.009388015605509281, 0.0010563957039266825, -0.019947092980146408, -0.018867112696170807, 0.020428529009222984, 0.01638185977935791, 0.013636368326842785, 0.045619379729032516, -0.0076249162666499615, 0.018073393031954765, -0.008151894435286522, -0.01506766676902771, 0.000133370966068469, -0.037500012665987015, 0.0006265995907597244, -0.009303438477218151, 0.010910396464169025, 0.008496707305312157, 0.008112858980894089, 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+28,"And as we look toward the holidays, we offer the best value in gaming with Game Pass and Xbox Series S, nearly half of the Series S buyers are new to our ecosystem. In closing, in a world facing increasing headwinds, digital technology is the ultimate tailwind. And we're innovating across the entire tech stack to help every organization, while also focusing intensely on our operational excellence and execution discipline. With that, I'll hand it over to Amy.","[-0.014578945003449917, -0.014737268909811974, -0.014499783515930176, -0.02567477524280548, -0.023352699354290962, 0.007111358921974897, -0.032034099102020264, -0.02087230049073696, -0.0041427952237427235, -0.02348463609814644, 0.04108491912484169, 0.008879303000867367, 0.007137746084481478, 0.0017267144285142422, -0.006718848831951618, -0.017415571957826614, 0.02736355923116207, -0.004367086570709944, 0.001731662079691887, -0.012408595532178879, -0.021228527650237083, -0.01963210105895996, 0.016980184242129326, 0.005683150142431259, -0.0036414379719644785, 0.012151320464909077, -0.005244462285190821, -0.004663943313062191, 0.033828429877758026, -0.019157130271196365, -0.0030196888837963343, -0.021980563178658485, -0.010324004106223583, -0.025542840361595154, -0.0014290334656834602, -0.007639103569090366, 0.0004605396534316242, 0.021136172115802765, 0.025437291711568832, 5.261984915705398e-05, 0.02651916816830635, 0.018101640045642853, 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+29,"Thank you, Satya, and good afternoon, everyone. Our first quarter revenue was $50.1 billion, up 11% and 16% in constant currency. Earnings per share was $2.35, increased 4% and 11% in constant currency when adjusted for the net tax benefit for the first quarter of fiscal year 2022. Driven by strong execution in a dynamic environment, we delivered a solid start to our fiscal year, in line with our expectations even as we saw many of the macro trends from the end of the fourth quarter continued to weaken through Q1.","[0.013616200536489487, -0.01455166470259428, -0.018631327897310257, -0.02502366527915001, 0.009075301699340343, -0.008977857418358326, -0.015876905992627144, -0.011212577112019062, -0.011043674312531948, -0.00908829364925623, 0.0364571176469326, 0.02790801413357258, -0.002830753568559885, -0.017293093726038933, 0.018241550773382187, -0.0010759461438283324, 0.008263266645371914, -0.018254542723298073, 0.010822799988090992, 0.0018741764361038804, -0.02436104603111744, 0.006697663571685553, -0.03819812089204788, 0.0028388737700879574, -0.011972641572356224, 0.013090001419186592, 0.01600683107972145, -0.0001436286693206057, 0.013434304855763912, -0.01676039956510067, -0.022970842197537422, -0.01570800133049488, -0.030610466375947, 0.022503109648823738, 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+30,"In our consumer business, PC market demand further deteriorated in September, which impacted our Windows OEM and Surface businesses. And reductions in customer advertising spend, which also weakened later in the quarter, impacted search and news advertising and LinkedIn Marketing Solutions. As you heard from Satya, in our commercial business, we saw strong overall demand for our Microsoft cloud offerings with a growth of 31% in constant currency as well as share gains across many businesses. Commercial bookings declined 3% and increased 16% in constant currency on a flat expiry base.","[0.0031513250432908535, -0.020826727151870728, 0.004483809228986502, -0.015110370703041553, -0.0039274971932172775, 0.006659089587628841, -0.026889530941843987, -0.017788663506507874, -0.007761720102280378, -0.039281632751226425, 0.029101453721523285, 0.0500481054186821, 0.004054083023220301, 0.01268524955958128, -0.00675569474697113, 0.015296918340027332, 0.006352618336677551, -0.027955517172813416, 0.017508842051029205, -0.03237936645746231, -0.004813598934561014, 0.00289815291762352, -0.007415274158120155, 0.009713809005916119, -0.025064026936888695, 0.012918434105813503, 0.03091363236308098, -0.02089335210621357, 0.014670650474727154, -0.0030413949862122536, -0.009840395301580429, -0.01289844699203968, -0.03253926336765289, 0.0074086119420826435, -0.022012637928128242, 0.0013391465181484818, -0.004263949114829302, 0.011659236624836922, 0.011086268350481987, -0.014430803246796131, 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+31,"Excluding the FX impact, growth was driven by strong renewal execution, and we continue to see growth in the number of large long-term Azure and Microsoft 365 contracts across all deal sizes. More than half of the $10 million plus Microsoft 365 bookings came from E5. Commercial remaining performance obligation increased 31% and 34% in constant currency to $180 billion. Roughly 45% will be recognized in revenue in the next 12 months, up 23% year over year.","[0.0074600949883461, -0.03487594425678253, -0.020448653027415276, -0.01637224294245243, -0.007020481862127781, 0.0032421438954770565, -0.040577586740255356, -0.013228346593677998, -0.014054285362362862, -0.027362562716007233, 0.05368603765964508, 0.03159882873296738, -0.008925470523536205, 0.011270071379840374, -0.01417418010532856, 0.027868783101439476, 0.010570688173174858, -0.0003551038389559835, -0.0016610367456451058, -0.031039323657751083, -0.017464615404605865, -0.0006381878047250211, -0.03447629511356354, 0.016598710790276527, -0.009911268949508667, 0.009072008542716503, 0.00782643910497427, -0.017637794837355614, 0.024311916902661324, -0.009025382809340954, 0.0055850800126791, -0.024458453059196472, -0.015333158895373344, 0.020675119012594223, -0.02385898120701313, -0.004126364830881357, -0.0071670194156467915, 0.012875324115157127, 0.013494778424501419, 0.009904608130455017, 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+32,"The remaining portion, which we recognized beyond the next 12 months, increased 38% year over year, and our annuity mix increased one point year over year to 96%. FX impacted company results in line with expectations. With the stronger US dollar, FX decreased total company revenue by five points, and at the segment level, FX decreased productivity and business processes and intelligent cloud revenue growth by six points and more personal computing revenue growth by three points. 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+33,"Microsoft Cloud gross margin percentage increased roughly two points year over year to 73%. Excluding the impact of the change in accounting estimate for useful lives, Microsoft cloud gross margin percentage decreased roughly one point driven by sales mix shift to Azure and lower Azure margin, primarily due to higher energy costs. Company gross margin dollars increased 9% and 16% in constant currency, and gross margin percentage decreased slightly year over year to 69%, excluding the impact of the latest change in accounting estimate, gross margin percentage decreased roughly three points, driven by sales mix shift to cloud, the lower Azure margin noted earlier and Nuance. Operating expense increased 15% and 18% in constant currency, driven by investments in cloud engineering, LinkedIn, Nuance, and commercial sales.","[0.0037902547046542168, -0.025727001950144768, 0.02287133038043976, -0.020508911460638046, 0.004608015064150095, -0.005919027607887983, -0.016848459839820862, -0.03063356503844261, -0.020846400409936905, -0.0365266315639019, 0.03971979022026062, 0.05384238436818123, 0.0038291956298053265, 0.017471516504883766, -0.0007690842030569911, 0.023027094081044197, 0.022235294803977013, 0.0010416710283607244, -0.0004246999742463231, -0.02990666590631008, -0.03146430477499962, 0.012837540358304977, -0.03507283702492714, -0.00042551124352030456, -0.0034689914900809526, 0.001082234550267458, 0.02255980297923088, -0.0021076800767332315, 0.015329762361943722, -0.009371793828904629, 0.005033120512962341, -0.014018749818205833, -0.024013599380850792, 0.02323477901518345, -0.013811064884066582, 0.010578963905572891, -0.00160874892026186, 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+34,"At a total company level, headcount grew 22% year over year, as we continue to invest in key areas just mentioned, as well as customer deployment. Headcount growth included roughly six points from the Nuance and Xandr acquisitions, which closed last Q3 and Q4, respectively. Operating income increased 6% and 15% in constant currency, and operating margins decreased roughly two points year over year to 43%. Excluding the impact of the change in accounting estimate, operating margins declined roughly four points year over year driven by sales mix shift to cloud, unfavorable FX impact, Nuance, and the lower Azure margin noted earlier.","[0.0059113106690347195, -0.022045286372303963, 0.026569752022624016, -0.01289800088852644, -0.006996526382863522, 0.008321079425513744, -0.02225511707365513, -0.013953709043562412, -0.025127168744802475, -0.0072850426658988, 0.046162646263837814, 0.024248505011200905, -0.0023327218368649483, -0.01156689040362835, 0.006327692419290543, 0.010950514115393162, 0.015947096049785614, -0.013442248106002808, 0.00815059244632721, -0.024274732917547226, -0.03616948425769806, 0.007658802904188633, -0.031422074884176254, 0.00845878105610609, 0.0010802977485582232, 0.0031113887671381235, 0.01847817376255989, -0.0012729153968393803, 0.02472062222659588, -0.0238944161683321, 0.005399849731475115, -0.0007102261297404766, -0.00902925617992878, 0.01757328025996685, 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+35,"Now to our segment results. Revenue from productivity and business processes was $16.5 billion and grew 9% and 15% in constant currency, ahead of expectations, with better-than-expected results in Office commercial and LinkedIn. Office commercial revenue grew 7% and 13% in constant currency. Office 365 commercial revenue increased 11% and 17% in constant currency, slightly better than expected, with the strong renewal execution noted earlier.","[0.004361174069344997, -0.015732089057564735, 0.0016694602090865374, -0.02025921456515789, 0.00827096402645111, -0.015825022011995316, -0.02493237517774105, -0.010800046846270561, -0.013548183254897594, -0.025967905297875404, 0.02600773237645626, 0.03109245002269745, -0.002548996824771166, 0.0036509069614112377, 0.006233093794435263, 0.015944506973028183, 0.003028593258932233, -0.01763056218624115, 0.01392655074596405, -0.020803531631827354, -0.0296320877969265, -0.00599412526935339, -0.028410693630576134, -0.0013143265387043357, -0.010680561885237694, 0.00460014259442687, 0.025503244251012802, -0.014338106848299503, 0.02465357817709446, -0.010746941901743412, -0.01232678908854723, -0.01496207993477583, -0.021772680804133415, 0.010109692811965942, -0.015838297083973885, -0.0073748319409787655, -0.01266532763838768, 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+36,"Growth was driven by installed base expansion across all workloads and customer segments, as well as higher ARPU from E5. Demand for security, compliance, and voice value in Microsoft 365 drove strong E5 momentum again this quarter. Paid Office 365 commercial seats grew 14% year over year, driven by our small and medium business and frontline worker offerings, although we saw a continued impact of new deal moderation outside of E5. Office consumer revenue grew 7% and 11% in constant currency, driven by continued momentum in Microsoft 365 subscriptions, which grew 13% to $61.3 million.","[0.006413091905415058, -0.04080691561102867, -0.024118071421980858, -0.035477250814437866, -0.005016747396439314, -0.005844459868967533, -0.03300084173679352, -0.01600244827568531, -0.008943336084485054, -0.02118406444787979, 0.04051082208752632, 0.010060411877930164, -0.00865397322922945, 0.003028217703104019, -0.02791343629360199, 0.024400705471634865, 0.00014752046263311058, 0.003152711084112525, 0.010558385401964188, -0.031385794281959534, -0.011379368603229523, -0.005487803369760513, -0.019730519503355026, 0.003872753819450736, -0.010046953335404396, 0.012240728363394737, -0.0012844689190387726, -0.009340369142591953, 0.02409115433692932, -0.00867416150867939, 0.006436644587665796, -0.02985149621963501, -0.011937906965613365, 0.004861971829086542, -0.036446280777454376, 0.004858606960624456, -0.006668807938694954, 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+37,"Dynamics revenue grew 15% and 22% in constant currency, driven by Dynamics 365, which grew 24% and 32% in constant currency. LinkedIn revenue increased 17% and 21% in constant currency, ahead of expectations, driven by better-than-expected growth in talent solutions, partially offset by weakness in marketing solutions from the advertising trends noted earlier. Segment gross margin dollars increased 11% and 18% in constant currency, and gross margin percentage increased roughly 1 point year over year. Excluding the impact of the latest change in accounting estimate, gross margin percentage decreased slightly, driven by sales mix shift to cloud offerings.","[0.009237907826900482, -0.012844843789935112, 0.008122093044221401, -0.014816981740295887, 0.00670137582346797, 0.00102012709248811, -0.020940987393260002, -0.02021441049873829, -0.001255291630513966, -0.03121686354279518, 0.030256744474172592, 0.03726302087306976, 0.015193244442343712, 0.008991390466690063, 0.019020749256014824, 0.019397012889385223, 0.015841973945498466, -0.0045411065220832825, 0.01314974669367075, -0.023964067921042442, -0.022874202579259872, 0.008219403214752674, -0.02085016667842865, -0.005326068960130215, -0.01098947785794735, 0.006561899092048407, 0.02768777497112751, -0.008680000901222229, 0.029114980250597, -0.01197554636746645, -0.012851331382989883, -0.005819103680551052, -0.023938119411468506, 0.022095726802945137, -0.007311181630939245, 0.005978042259812355, -0.006561899092048407, 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+38,"Operating expense increased 13% and 16% in constant currency, and operating income increased 10% and 19% in constant currency, including four points due to the latest change in accounting estimate. Next, the Intelligent cloud segment. Revenue was $20.3 billion, increasing 20% and 26% in constant currency, in line with expectations. Overall, server products and cloud services revenue increased 22% and 28% in constant currency.","[0.012992024421691895, -0.02033192850649357, 0.013999462127685547, -0.010754726827144623, 0.01271726842969656, 0.00017448219296056777, -0.013168652541935444, -0.026127967983484268, -0.0004910441930405796, -0.03482856974005699, 0.027711084112524986, 0.03987884148955345, 0.0022749125491827726, -0.006908145733177662, 0.006829644087702036, 0.025395285338163376, 0.012161214835941792, 0.002054126700386405, 0.005226902198046446, -0.00753615889698267, -0.03757612779736519, 0.018042298033833504, -0.025748543441295624, -0.002821153262630105, -0.004575992468744516, -0.007137109059840441, 0.019664665684103966, -0.00904077384620905, 0.008465095423161983, -0.016105923801660538, -0.0013893158175051212, -0.006139483768492937, -0.02713540568947792, 0.0207898560911417, -0.017963794991374016, -0.004131149500608444, -0.00838659331202507, -0.010793977417051792, 0.010872479528188705, -0.002675598254427314, 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+39,"Azure and other cloud services revenue grew 35% and 42% in constant currency, about one point lower than expected, driven by the continued moderation in Azure consumption growth, as we help customers optimize current workloads while they prioritize new workloads. In our per-user business, the enterprise mobility and security installed base grew 18% to over 232 million seats, with continued impact from the new deal moderation noted earlier. In our on-premises server business, revenue was flat, and increased 4% in constant currency, slightly ahead of expectations, driven by hybrid demand, including better-than-expected annuity purchasing ahead of the SQL Server 2022 launch. Enterprise services revenue grew 5% and 10% in constant currency, driven by enterprise support services.","[0.017210062593221664, -0.03311038017272949, -0.001220519538037479, -0.013339763507246971, 0.008048389106988907, -0.0028650695458054543, -0.034341540187597275, -0.0317220501601696, -0.009194416925311089, -0.03486543893814087, 0.03609659895300865, 0.033293742686510086, 0.005841467529535294, 4.4382995838532224e-05, -0.0045153493992984295, 0.012174089439213276, 0.010464870370924473, -0.006830325350165367, 0.016109876334667206, -0.024321984499692917, -0.014708448201417923, 0.0016118062194436789, -0.037380151450634, 0.006064123939722776, -0.012481879442930222, 0.016646871343255043, 0.021990636363625526, -0.00598553940653801, 0.03127673640847206, -0.008506800048053265, 0.0017305020010098815, -0.017419621348381042, -0.011551960371434689, 0.01613607071340084, -0.023365870118141174, -0.004250125959515572, -0.011938335373997688, 0.021532224491238594, 0.018768660724163055, 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+40,"Segment gross margin dollars increased 20% and 26% in constant currency, and gross margin percentage decreased slightly. Excluding the impact of the latest change in accounting estimate, gross margin percentage declined roughly three points, driven by sales mix shift to Azure and higher energy costs impacting Azure margins. Operating expenses increased 25% and 28% in constant currency, including roughly eight points of impact from Nuance. And operating income grew 17% and 25% in constant currency with roughly nine points of favorable impact from the latest change in accounting estimate.","[0.004678687546402216, -0.023297954350709915, 0.022586766630411148, -0.019570808857679367, -0.000647394044790417, 0.0034769135527312756, -0.01646265760064125, -0.018793771043419838, -0.018543537706136703, -0.023969629779458046, 0.03932599723339081, 0.04693832993507385, 0.0038489694707095623, 0.004237488377839327, 0.005600596312433481, 0.016449488699436188, 0.023311123251914978, 0.005142934620380402, 0.0025945149827748537, -0.019952742382884026, -0.03508521616458893, 0.019491787999868393, -0.031660985201597214, 0.005238418001681566, -0.006598233245313168, 0.00012583645002450794, 0.02125658467411995, -0.005653277039527893, 0.0069274865090847015, -0.018345985561609268, 0.006980167236179113, -0.0022981869988143444, -0.03463743254542351, 0.024628136307001114, -0.015158816240727901, 0.008303765207529068, -0.010094902478158474, 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+41,"Now to more personal computing. Revenue decreased slightly year over year to $13.3 billion and grew 3% in constant currency, in line with expectations overall, but with OEM and Surface weakness offset by upside in gaming consoles. Windows OEM revenue decreased 15% year over year. Excluding the impact from the Windows 11 deferral last year, revenue declined 20%, driven by PC market demand deterioration noted earlier.","[0.007162286434322596, -0.031173184514045715, 0.006056393031030893, -0.012633206322789192, 0.01246406976133585, -0.009796914644539356, -0.028493020683526993, -0.020543597638607025, -0.023301826789975166, -0.04319489747285843, 0.042180076241493225, 0.03195381537079811, -0.009822935797274113, 0.019047388806939125, -0.014949077740311623, 0.018683094531297684, 0.0026590232737362385, -0.013108090497553349, 0.017902463674545288, -0.02544856071472168, -0.016718506813049316, 0.004631741903722286, -0.03307272121310234, 0.0071557811461389065, -0.007936411537230015, 0.008782095275819302, 0.02222195267677307, -0.01922953501343727, 0.026957780122756958, 0.0015645140083506703, -0.0003620581410359591, -0.019190505146980286, -0.019099431112408638, 0.011234576813876629, -0.028649145737290382, 0.016965707764029503, -0.007546096574515104, 0.02298957295715809, 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+42,"Devices revenue grew 2% and 8% in constant currency, in line with expectations, driven by the impact of a large Hollands deal, partially offset by low double-digit declines in consumer Surface sales. Windows commercial products and cloud services revenue grew 8% and 15% in constant currency, in line with expectations, driven by demand for Microsoft 365 E5 noted earlier. Search and news advertising revenue, ex TAC, increased 16% and 21% in constant currency, in line with expectations, benefiting from an increase in search volumes and roughly five points of impact from Xandr even as we saw increased ad market headwinds during September. Edge browser gained share again this quarter.","[0.01180251408368349, -0.016670143231749535, 0.009068187326192856, -0.013433863408863544, 0.013433863408863544, -0.007483071181923151, -0.03022288903594017, -0.0231955386698246, -0.009180466644465923, -0.04216410219669342, 0.03909954056143761, 0.042771730571985245, -0.01116846688091755, 0.013909397646784782, -0.004118000622838736, 0.020448002964258194, 0.0030232795979827642, 0.0017155584646388888, 0.010151350870728493, -0.032177865505218506, -0.02392205037176609, 0.006393303629010916, -0.02392205037176609, 0.0037811635993421078, -0.013988654129207134, 0.01582474634051323, 0.03471405431628227, -0.008348280563950539, 0.020619723945856094, -0.013896188698709011, 0.0020375351887196302, -0.02842642366886139, -0.025401493534445763, 0.008685117587447166, -0.011703443713486195, 0.010362699627876282, -0.0026005818508565426, 0.011571351438760757, 0.030381402000784874, 0.01355274673551321, 0.024450423195958138, 0.008255815133452415, 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+43,"And in gaming, revenue grew slightly and was up 4% in constant currency, ahead of expectations, driven by better-than-expected console sales. Xbox hardware revenue grew 13% and 19% in constant currency. Xbox content and services revenue declined 3% and increased 1% in constant currency, driven by declines in first-party content as well as in third-party content where we had lower engagement hours and higher monetization, partially offset by growth in Xbox Game Pass subscriptions. Segment gross margin dollars declined 9% and 4% in constant currency, and gross margin percentage decreased roughly five points year over year driven by sales mix shift to lower margin businesses.","[0.013882124796509743, -0.020371630787849426, 0.0035156868398189545, -0.01902986317873001, -0.0015602875500917435, -0.015765758231282234, -0.014565909281373024, -0.023442212492227554, -0.0020561928395181894, -0.04337518662214279, 0.0414915531873703, 0.0433235801756382, 0.007173290941864252, 0.002656117547303438, -0.003051229054108262, 0.01444979477673769, 0.019932975992560387, 0.00849570520222187, 0.018217062577605247, -0.023803457617759705, -0.014578810892999172, 0.0021916597615927458, -0.022758428007364273, -0.0013659571995958686, -0.01634632982313633, 0.013585387729108334, 0.018694423139095306, -0.005554140079766512, 0.036279305815696716, -0.00414786534383893, -0.01481103990226984, -0.026809530332684517, -0.02714497223496437, 0.009224645793437958, -0.02198433130979538, 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+44,"Operating expenses increased 2% and 5% in constant currency, driven by the Xandr acquisition. And operating income decreased 15% and 9% in constant currency. Now back to total company results. Capital expenditures, including finance leases were $6.6 billion, and cash paid for PP&E was $6.3 billion.","[0.017512913793325424, 0.008054900914430618, 0.010555817745625973, -0.028451988473534584, -0.011004033498466015, 0.016668448224663734, -0.008269265294075012, -0.0011278485180810094, -0.0002864280540961772, -0.013654355891048908, 0.02603551745414734, 0.0458349846303463, 0.00847063772380352, 0.0027802400290966034, 0.015005500987172127, 0.016824349761009216, 0.008743464946746826, 0.0012723819818347692, 0.009808790870010853, -0.003946251701563597, -0.037208445370197296, -0.013128189370036125, -0.044094085693359375, 0.001624783966690302, 0.009458012878894806, -0.015447220765054226, 0.015460212714970112, 0.012485096231102943, 0.0012325947172939777, -0.009750327095389366, -0.00895782932639122, -0.008009430021047592, -0.005804075859487057, 0.008620043285191059, -0.002319031860679388, -0.012361674569547176, -0.009354078210890293, -0.012972287833690643, 0.019825449213385582, 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+45,"Our data center investments continue to be based on strong customer demand and usage signals. Cash flow from operations was $23.2 billion, down 5% year over year, driven by strong cloud billings and collections, which were more than offset by a tax payment related to the transfer of intangible property completed in Q1 of FY '22. Free cash flow was $16.9 billion, down 10% year over year. Excluding the impact of this tax payment, cash flow from operations grew 2%, and free cash flow was relatively unchanged [indiscernible] year.","[-0.022294508293271065, -0.027116671204566956, 0.00050164241110906, -0.02752598002552986, 0.013916480354964733, 0.0017843281384557486, -0.02583758346736431, -0.023010797798633575, -0.00845477357506752, -0.03793775662779808, 0.019058415666222572, 0.03238651528954506, -0.01261820551007986, 0.0013790170196443796, 0.008096628822386265, 0.028932977467775345, 0.00513873714953661, -0.03604470565915108, -0.00913268979638815, -0.017945608124136925, -0.027884123846888542, 0.027628306299448013, -0.03591679781675339, 0.0018562768818810582, -2.139724892913364e-05, -0.0033895839005708694, 0.006843122188001871, -0.005311414133757353, 0.013916480354964733, -0.019416559487581253, -0.009350134991109371, -0.012503087520599365, -0.021961946040391922, 0.016628148034214973, -0.024187559261918068, 0.0014133924851194024, -0.007955929264426231, 0.02029913105070591, 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+46,"This quarter, other income and expense was $54 million, driven by interest income, which was mostly offset by interest expense and net losses on foreign currency remeasurement. Our effective tax rate was approximately 19%. And finally, we returned $9.7 billion to shareholders through share repurchases and dividends. Now, moving to our Q2 outlook, which, unless specifically noted otherwise, is on a US dollar basis.","[-0.030022066086530685, -0.006907156202942133, 0.0024519753642380238, -0.041390907019376755, -0.009534737095236778, -0.018458107486367226, -0.0006942927720956504, -0.023075886070728302, -0.010347726754844189, -0.010920071043074131, 0.017053261399269104, 0.03056839480996132, 0.011830618605017662, -0.0024227078538388014, 0.006816101726144552, 0.03199925646185875, 0.006217741407454014, -0.04237950220704079, 0.008403056301176548, -0.019160529598593712, -0.0376446507871151, 0.005928317550569773, -0.030074097216129303, -0.004890942946076393, -0.0007564864354208112, -0.010549347847700119, 0.015622400678694248, -0.004364125896245241, 0.003905600169673562, -0.017664629966020584, -0.0200190469622612, -0.00917051825672388, -0.01861420087516308, 0.019329631701111794, -0.032493554055690765, 0.004923462867736816, -0.007186824455857277, 0.014425680972635746, 0.02087756246328354, 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+47,"My commentary, for both the full year and next quarter, does not include any impact from AC division, which we still expect to close by the end of the fiscal year. First, FX. With the stronger US dollar and based on current rates, we now expect FX to decrease total revenue growth by approximately five points and to decrease total COGS and operating expense growth by approximately three points. Within the segments, we anticipate roughly seven points of negative FX impact on revenue growth in productivity and business processes, six points in Intelligent cloud and three points in more personal computing.","[-0.003036661073565483, -0.005112246610224247, 0.005958667490631342, -0.022242993116378784, 0.014689278788864613, -0.004592928569763899, -0.024657486006617546, -0.015174875035881996, -0.009125158190727234, -0.014379036612808704, 0.03639272227883339, 0.03628481179475784, 0.0051392242312431335, -0.0063565862365067005, -0.01101358700543642, 0.007924656383693218, 0.01236246433109045, -0.021973218768835068, 0.009448888711631298, -0.028515275567770004, -0.008659794926643372, 0.026694290339946747, -0.016186533495783806, -0.0024397827219218016, -0.01186337973922491, 0.014082283712923527, 0.0013025100342929363, -0.015471627935767174, 0.012200599536299706, -0.006123905070126057, 0.004717699717730284, -0.012868293561041355, 0.0006959366146475077, 0.02168995328247547, 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+48,"Our outlook has many of the trends we saw at the end of Q1, continue into Q2. In our consumer business, materially weaker PC demand from September will continue, and impact both Windows OEM and Surface device results even as the Windows installed base and usage grows, as you heard from Satya. Additionally, customers focusing our advertising spend will impact LinkedIn and Search and News advertising revenue. In our commercial business, demand for our differentiated hybrid and cloud offerings, together with consistent execution, should drive healthy growth across the Microsoft Cloud.","[-0.0095982626080513, -0.029435571283102036, 0.011965788900852203, -0.015581157058477402, 0.004084151703864336, 0.008424616418778896, -0.03154003992676735, -0.012201867066323757, -0.0047890134155750275, -0.03342866525053978, 0.021395424380898476, 0.042979709804058075, 0.00043084259959869087, 0.017146017402410507, -0.0059694042429327965, -0.001692174351774156, 0.010967515408992767, -0.024390242993831635, 0.030730627477169037, -0.033050939440727234, -0.007885009981691837, 0.001506684347987175, -0.00542642455548048, -0.00404368108138442, -0.023418951779603958, 0.016957154497504234, 0.031189294531941414, -0.01921001449227333, 0.0036929366178810596, -0.0026744280476123095, -0.005439914762973785, -0.012242337688803673, -0.02911180630326271, -0.003212349023669958, -0.019452838227152824, -0.0073656379245221615, 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+49,"In commercial bookings, continued strong execution across core annuity sales motions and commitments to our platform should drive solid growth on a moderately growing expiry base against a strong prior year comparable, which included a significant volume of large long-term Azure contracts. As a reminder, the growing mix of larger long-term Azure contracts which are more unpredictable in their timing, always drives increased quarterly volatility in our bookings growth rate. Microsoft Cloud gross margin percentage should be up roughly one point year over year, driven by the latest accounting estimate change noted earlier. Excluding that impact, Q2 gross margin percentage will decrease roughly two points driven by lower Azure margin, primarily due to higher energy cost, revenue mix shift to Azure and the impact from Nuance.","[0.002215044805780053, -0.03396959602832794, 0.01146808173507452, -0.01699817180633545, -0.0010665650479495525, 0.009836471639573574, -0.03544071689248085, -0.026934945955872536, -0.02415318414568901, -0.031000599265098572, 0.043331295251846313, 0.04878782853484154, 0.004844679497182369, 0.01677081547677517, -0.007616410963237286, 0.00385835743509233, 0.02963646501302719, -0.004282977432012558, 0.013654706999659538, -0.04228813201189041, -0.013246804475784302, 0.012283886782824993, -0.033193912357091904, 0.009609115310013294, -0.005744071211665869, 0.010097261518239975, 0.007723401766270399, -0.01051185093820095, 0.010264434851706028, -0.004202734213322401, 0.007362307980656624, -0.014844980090856552, -0.02744315192103386, 0.034103333950042725, -0.014604250900447369, 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+50,"In capital expenditures, we expect a sequential increase on a dollar basis with normal quarterly spend variability in the timing of our cloud infrastructure build-out. Next, to segment guidance. In productivity and business processes, we expect revenue to grow between 11% and 13% in constant currency or USD16.6 billion to USD 16.9 billion. In Office Commercial, revenue growth will again be driven by Office 365, with seat growth across customer segments and ARPU growth from E5.","[0.0027489452622830868, -0.01642036624252796, -0.0071106054820120335, -0.01839294284582138, 0.016526993364095688, -0.007490459829568863, -0.018886087462306023, -0.01651366427540779, -0.020871993154287338, -0.019659124314785004, 0.029028862714767456, 0.027109598740935326, -0.004328339360654354, 0.00590106938034296, -0.0004698197590187192, 0.012095360085368156, 0.0027089607901871204, -0.024057436734437943, 0.009316425770521164, -0.02475050464272499, -0.02662978321313858, -0.0025107034016400576, -0.016407039016485214, -0.004994750488549471, -0.025243649259209633, 0.0033070645295083523, 0.009116502478718758, -0.010356026701629162, 0.018926072865724564, -0.004231709986925125, -0.004315011203289032, -0.01240190863609314, 0.012321939691901207, 0.016873527318239212, -0.0025906728114932775, -0.00392182869836688, -0.012841739691793919, 0.02055211551487446, 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+51,"We expect Office 365 revenue growth to be similar to last quarter on a constant currency basis. In our on-premises business, we expect revenue to decline in the low to mid-30s. In Office Consumer, we expect revenue to decline low to mid-single digits as Microsoft 365 subscription growth will be more than offset by unfavorable FX impact. For LinkedIn, we expect continued strong engagement on the platform, although results will be impacted by a slowdown in advertising spend and hiring, resulting in mid to high single-digit revenue growth or low to mid-teens growth in constant currency.","[-0.0018307234859094024, -0.029424477368593216, -0.006226453464478254, -0.02106494829058647, 0.0017642725724726915, -0.01115046814084053, -0.04941292107105255, -0.00784121174365282, -0.01398127805441618, -0.024507107213139534, 0.04983820766210556, 0.03197619318962097, -0.0023855888284742832, 0.0035983186680823565, 0.0032494510523974895, 0.001054908731020987, 0.008273142389953136, -0.027005663141608238, 0.030700335279107094, -0.03139142692089081, -0.014326822943985462, 0.00984138436615467, -0.030088987201452255, -0.0005486356094479561, -0.011914653703570366, 0.0035983186680823565, 0.0043259565718472, -0.010618860833346844, 0.019124582409858704, -0.00788108166307211, 0.004678146447986364, -0.017263954505324364, 0.005518750753253698, 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+52,"And in Dynamics, we expect revenue growth in the low double digits or the low 20s in constant currency, driven by continued share gains in Dynamics 365. For intelligent cloud, we expect revenue to grow between 22% and 24% in constant currency or US$21.25 billion to US$21.55 billion. Revenue will continue to be driven by Azure, which, as a reminder, can have quarterly variability primarily from our per-user business and from in-period recognition depending on the mix of contracts. We expect Azure revenue growth to be sequentially lower by roughly 5 points on a constant currency basis.","[0.002254944061860442, -0.02459462359547615, 0.0061060478910803795, -0.008633026853203773, 0.018826160579919815, -0.0016576133202761412, -0.04268661513924599, -0.02957647666335106, -0.011261610314249992, -0.02468639425933361, 0.03914687782526016, 0.03130701556801796, -0.002984195714816451, -0.001193842152133584, -0.006784497294574976, 0.01570594683289528, 0.010553662665188313, 0.0009398331749252975, 0.011536923237144947, -0.028920968994498253, -0.02286408469080925, 0.01093385647982359, -0.022470779716968536, 0.0061683207750320435, -0.016269683837890625, 0.0034315790981054306, 0.016007481142878532, -0.0094720758497715, 0.031333234161138535, -0.022483890876173973, 0.007276127580553293, -0.010370120406150818, -0.0022975520696491003, 0.019586550071835518, -0.013503444381058216, -0.00016090647841338068, -0.007715317420661449, 0.019979853183031082, 0.021303977817296982, 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+53,"Azure revenue will continue to be driven by strong growth in consumption, with some impact from the Q1 trends noted earlier. And our per user business should continue to benefit from Microsoft 365 suite momentum, though we expect moderation in growth rate given the size of the installed base. In our on-premise server business, we expect revenue to decline low single digits, as demand for our hybrid solutions, including strong annuity purchasing from the SQL Server 2022 launch, will be more than offset by unfavorable FX impact. And in enterprise services, we expect revenue growth to be in the low single digits, driven by enterprise support.","[0.005428245756775141, -0.030461037531495094, -0.010542190633714199, -0.007287861779332161, 0.0006421077996492386, -0.006220547016710043, -0.04308547452092171, -0.015767449513077736, -0.018975943326950073, -0.03352547436952591, 0.03551604971289635, 0.02173917554318905, 0.0028074311558157206, -0.0034900542814284563, -0.012617888860404491, -0.0048160129226744175, 0.012513121590018272, -0.023192819207906723, 0.03664229437708855, -0.026846570894122124, -0.009442135691642761, 0.0033558213617652655, -0.026898954063653946, -0.009422492235898972, -0.005768738687038422, 0.02366427145898342, 0.0021313559263944626, -0.022983284667134285, 0.016199614852666855, 0.0008078526007011533, 0.008597451262176037, -0.015204326249659061, 0.0042594377882778645, 0.016291284933686256, -0.015125751495361328, -0.0029678556602448225, -0.005775286816060543, 0.026073914021253586, 0.015099559910595417, 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+54,"In More Personal Computing, we expect revenue of US$14.5 billion to US$14.9 billion. In Windows OEM, we expect revenue to decline in the high 30s. Excluding the impact from the Windows 11 revenue deferral last year, revenue would decline mid-30s, reflecting both PC market demand and a strong prior year comparable, particularly in the commercial segment. In devices, revenue should decline approximately 30%, again, roughly in line with the PC market.","[0.007634567562490702, -0.031139109283685684, 0.0019004783825948834, -0.012950682081282139, 0.011729412712156773, -0.0034580868668854237, -0.03330735117197037, -0.006903111934661865, -0.020415447652339935, -0.024151096120476723, 0.024242527782917023, 0.04600333049893379, -0.01739819347858429, 0.014903407543897629, -0.019096216186881065, 0.011703289113938808, -0.0013690302148461342, -0.019344387575984, 0.010005267336964607, -0.022857986390590668, -0.020141152665019035, 0.014407062903046608, -0.02102934755384922, 0.008686034940183163, -0.020232584327459335, 0.011004487983882427, 0.019775424152612686, -0.018273327499628067, 0.03006804920732975, 0.008059073239564896, 0.0006045121117495, -0.0065308534540236, -0.006070428527891636, 0.002558461856096983, -0.019592560827732086, 0.005802663508802652, -0.011415932327508926, 0.026541387662291527, 0.021199150010943413, 0.0022906968370079994, 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+55,"In Windows commercial products and cloud services, customer demand for Microsoft 365 and our advanced security solutions should drive growth in the mid-single digits or low double digits in constant currency. Search and news advertising, ex TAC, should grow in the low to mid-teens, roughly six points faster than overall search and news advertising revenue, driven by growing first-party revenue and the inclusion of Xandr. And in gaming, we expect revenue to decline in the low to mid-teens against a strong prior year comparable. That included several first-party title launches, partially offset by growth in Xbox Game Pass subscribers.","[0.012016835622489452, -0.021006017923355103, 0.004866349510848522, -0.025694862008094788, 0.007669608108699322, -0.0031046841759234667, -0.03925231844186783, -0.024274813011288643, -0.008674360811710358, -0.032848697155714035, 0.060445889830589294, 0.0357423834502697, -0.00267096608877182, 0.010871418751776218, -0.005345281679183245, 0.009792984463274479, 0.011239827610552311, 7.608904707012698e-05, 0.014347860589623451, -0.021649060770869255, -0.0073614842258393764, -0.003248698776587844, -0.013530662283301353, 0.012405339628458023, -0.010141298174858093, 0.002123376354575157, 0.02010844089090824, -0.005790721625089645, 0.02375234104692936, -0.013483773916959763, 0.00367404380813241, -0.03563521057367325, -0.016920026391744614, 0.004166372120380402, -0.005542882718145847, 0.0020614166278392076, 0.003928580787032843, 0.014280877076089382, 0.030464084818959236, 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+56,"We expect Xbox content and services revenue to decline in the low to mid-teens. Now back to company guidance. We expect COGS to grow between 6% and 7% in constant currency or to be between US$17.4 billion and US$17.6 billion, and operating expense to grow between 17% and 18% in constant currency, or to be between US$14.3 billion and US$14.4 billion. As we continue to focus our investment in key growth areas, total headcount growth sequentially should be minimal.","[-0.003946698736399412, -0.016969818621873856, 0.00489311758428812, -0.020755494013428688, -0.007643647957593203, -0.014393455348908901, -0.022582609206438065, -0.01624685898423195, -0.002019355772063136, -0.01863919571042061, 0.03396592661738396, 0.028208544477820396, 0.0032648169435560703, 0.0035753606352955103, -0.005162584129720926, 0.009503624401986599, 0.007117859553545713, -0.0023627611808478832, 0.011389889754354954, -0.01583937369287014, -0.020702915266156197, 0.01403854787349701, -0.023739343509078026, 0.007485911250114441, -0.012901530601084232, -0.016601765528321266, 0.022687766700983047, -0.008570349775254726, 0.02904980443418026, -0.015602768398821354, 0.004787960089743137, -0.021005243062973022, 0.003979560453444719, 0.0035589297767728567, -0.00828773807734251, -0.007597641088068485, -0.004722236189991236, 0.013151280581951141, 0.024567458778619766, 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+57,"Other income and expense should be roughly $100 million, as interest income is expected to more than offset interest expense. Further FX and equity movements through Q2 are not reflected in this number. And as a reminder, we are required to recognize mark-to-market gains or losses on our equity portfolio, which can increase quarterly volatility. And we expect our Q2 effective tax rate to be between 19% and 20%.","[-0.01832527294754982, -0.014160438440740108, 0.012983707711100578, -0.02945794351398945, -0.006257168482989073, -0.0017270842799916863, -0.02587486244738102, -0.004088810179382563, -0.008699877187609673, -0.012798603624105453, 0.020282084122300148, 0.031520526856184006, -0.0019221043912693858, 0.013109313324093819, 0.006829007063060999, 0.018973136320710182, 0.00860071461647749, -0.03170563280582428, 0.012163962237536907, -0.04907894507050514, -0.025702981278300285, 0.015799928456544876, -0.03971797972917557, 0.0034277252852916718, -0.007073608692735434, -0.012302789837121964, 0.015680933371186256, 0.00665381969884038, -0.012805215083062649, -0.009241966530680656, 0.007410761900246143, 0.0011345869861543179, -0.015006626956164837, 0.01418688241392374, -0.021630698814988136, 0.013644792139530182, 0.006954613141715527, 0.02055974118411541, 0.024446919560432434, -0.020467188209295273, 0.0193169005215168, 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+58,"And finally, as a reminder, for Q2 cash flow, we expect to make a $2.4 billion cash tax payment related to the capitalization of R&D provision enacted in 2017 TCJA and effective as of July 1, 2022. Now some thoughts on the full fiscal year. First, FX. Based on current rates, we now expect a roughly five-point headwind to full-year revenue growth.","[-0.012947804294526577, -0.010494815185666084, -0.006848480086773634, -0.012490354478359222, 0.02357521280646324, -0.014996381476521492, -0.013391993939876556, -0.012914655730128288, -0.017117885872721672, -0.00879761204123497, 0.044525064527988434, 0.01677314005792141, -0.00667610764503479, 0.0060628606006503105, 0.015513497404754162, 0.008605350740253925, 0.009845104068517685, -0.03407665714621544, 0.0020767534151673317, -0.023190690204501152, -0.02935631014406681, 0.013524588197469711, -0.05314367264509201, 0.004504880867898464, -0.0009206995600834489, 0.006066175177693367, 0.00425295252352953, -0.009891511872410774, 0.005482761655002832, 0.009354506619274616, -0.015566535294055939, -0.00176681496668607, -0.01482400856912136, 0.004448528401553631, -0.01441296748816967, -0.01662728749215603, -0.0064109195955097675, 0.0071932245045900345, 0.031080033630132675, -0.014717933721840382, 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+59,"And FX should decrease COGS and operating expense growth by approximately three points. At the total company level, we continue to expect double-digit revenue and operating income growth on a constant currency basis. Revenue will be driven by around 20% constant currency growth in our commercial business, driven by strong demand for our Microsoft cloud offerings. That growth will be partially offset by the increased declines we now see in the PC market.","[0.0009612650028429925, -0.01565629616379738, 0.001953750615939498, -0.010891051031649113, 0.010976496152579784, -0.00034178313217125833, -0.025620589032769203, -0.019705111160874367, 0.0038154826033860445, -0.02965625748038292, 0.03528253361582756, 0.04432664066553116, 0.011298561468720436, 0.0067830802872776985, -0.009905138052999973, 0.01586662419140339, 0.012560529634356499, -0.009931428357958794, 0.013119213283061981, -0.02237364836037159, -0.01418399903923273, 0.016050660982728004, -0.03086564503610134, -0.001920886803418398, -0.012705130502581596, 0.0002548995544202626, 0.018022486940026283, -0.012166164815425873, 0.018811216577887535, -0.015209348872303963, 0.003407972166314721, -0.026054389774799347, -0.0014254656853154302, 0.023425288498401642, 0.00042106694309040904, -0.004081679042428732, -0.010135184042155743, 0.01360559742897749, 0.011936117894947529, -0.0038023372180759907, 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+60,"With the high margins in our Windows OEM business and the cyclical nature of the PC market, we take a long-term approach to investing in our core strategic growth areas and maintain these investment levels regardless of PC market conditions. Therefore, with our first quarter results and lower expected OEM revenue for the remainder of the year as well as over $800 million of greater-than-expected energy cost, we now expect operating margins in US dollars to be down roughly a point year over year. On a constant currency basis, excluding the incremental impact of the lower Windows OEM revenue and the favorable impact of the latest accounting change, we continue to expect FY 2023 operating margins to be roughly flat year over year. In closing, in this environment, it is more critical than ever to continue to invest in our strategic growth markets such as cloud, security, Teams, Dynamics 365, and LinkedIn where we have opportunities to continue to gain share as we provide problem-solving innovations to our customers.","[0.010544494725763798, -0.03846913203597069, -0.005844140890985727, -0.01026700809597969, 0.005776461213827133, 0.00354979932308197, -0.026516899466514587, -0.03064536117017269, -0.017014672979712486, -0.014510524459183216, 0.024567725136876106, 0.04767356812953949, -0.014889529906213284, 0.013698368333280087, -0.012466597370803356, 0.014727098867297173, 0.013190770521759987, -0.021088989451527596, 0.01827351376414299, -0.02666579559445381, -0.024269934743642807, 0.007634268142282963, -0.02307877130806446, -0.005979500245302916, -0.016595058143138885, -0.0019034910947084427, 0.009921841323375702, -0.019017990678548813, 0.017813293263316154, -0.014104446396231651, -0.0037393025122582912, 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+61,"And while we continue to help our customers do more with less, we will do the same internally. And you should expect to see our operating expense growth moderate materially through the year. While we focus on growing productivity of the significant headcount investments we've made over the last year. With that, let's go to Q&A.","[-0.014238400384783745, -0.011069077998399734, 0.00546052074059844, -0.02828839048743248, -0.009178250096738338, 0.02456056699156761, -1.980563683900982e-05, 0.006368925329297781, -0.02470860257744789, -0.005231737159192562, 0.026390833780169487, 0.005817153491079807, 0.010133757255971432, -0.005467249546200037, 0.00607285276055336, 0.007125928997993469, 0.020657792687416077, -0.028719041496515274, 0.02569102682173252, -0.0036672628484666348, -0.00866012368351221, -0.006466494407504797, -0.00488519761711359, 0.0026696999557316303, -0.004060904961079359, -0.0013575601624324918, 0.014278773218393326, -0.013087754137814045, 0.014494098722934723, 0.00046892178943380713, 0.006634717807173729, -0.012381217442452908, -0.0025384859181940556, 0.01738753542304039, -0.013148314319550991, -0.019285092130303383, 0.00015360867837443948, 0.01409036386758089, 0.02443944662809372, -0.02021368406713009, 0.02376655489206314, 0.005066878627985716, 0.00017863186076283455, 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diff --git a/langchain_tuto_get_start.ipynb b/langchain_tuto_get_start.ipynb
new file mode 100644
index 0000000..03b8d49
--- /dev/null
+++ b/langchain_tuto_get_start.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install langchain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = ''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "HappySox.\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"What would be a good company name for a company that makes colorful socks?\"\n",
+    "print(llm(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "\n",
+    "llm = OpenAI(temperature=0.9)\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"product\"],\n",
+    "    template=\"What is a good name for a company that makes {product}?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is a good name for a company that makes colorful socks?\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(prompt.format(product=\"colorful socks\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "chain = LLMChain(llm=llm, prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nFunky Foot Fashions.'"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"colorful socks\")\n",
+    "# -> '\\n\\nSocktastic!'"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5238573367df39f7286bb46f9ff5f08f63a01a80960060ce41e3c79b190280fa"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/microsoft-earnings.csv b/microsoft-earnings.csv
new file mode 100644
index 0000000..ad1cc56
--- /dev/null
+++ b/microsoft-earnings.csv
@@ -0,0 +1,63 @@
+text
+"Thank you, Brett. To start, I want to outline the principles that are guiding us through these changing economic times. First, we will invest behind categories that will drive the long-term secular trend where digital technology as a percentage of world's GDP will continue to increase.Second, we'll prioritize helping our customers get the most value out of their digital spending, so that they can do more with less. And finally, we will be disciplined in managing our cost structure."
+"With that context, this quarter, the Microsoft Cloud again exceeded $25 billion in quarterly revenue, up 24% and 31% in constant currency. And based on current trends continuing, we expect our broader commercial business to grow at around 20% in constant currency this fiscal year, as we manage through the cyclical trends affecting our consumer business. With that, let me highlight our progress starting with Azure. Moving to the cloud is the best way for organizations to do more with less today."
+"It helps them align their spend with demand and mitigate risk around increasing energy costs and supply chain constraints. We're also seeing more customers turn to us to build and innovate with infrastructure they already have. With Azure Arc, organizations like Wells Fargo can run Azure services, including containerized applications across on-premises, edge, and multi-cloud environments. We now have more than 8,500 Arc customers, more than double the number a year ago."
+"We are the platform of choice for customers' SAP workloads in the cloud, companies like Thabani, Munich Re's, Sodexo, Volvo Cars, all run SAP on Azure. We are the only cloud provider with direct and secure access to Oracle databases running an Oracle Cloud infrastructure, making it possible for companies like FedEx, GE, and Marriott to use capabilities from both companies. And with Azure Confidential Computing, we're enabling companies in highly regulated industries, including RBC, to bring their most sensitive applications to the cloud. Just last week, UBS said it will move more than 50% of its applications to Azure."
+"Now to data and AI. With our Microsoft Intelligent Data Platform, we provide a complete data fabric, helping customers cut integration tax associated with bringing together siloed solutions. Customers like Mercedes-Benz are standardizing on our data stack to process and govern massive amounts of data. Cosmos DB is the go-to database powering the world's most demanding workloads at limitless scale."
+"Cosmos DB now supports postscript SQL, making Azure the first cloud provider to offer a database service that supports both relational and no SQL workloads. And in AI, we are turning the world's most advanced models into platforms for customers. Earlier this month, we brought the power of DALL-E to Azure OpenAI service, helping customers like Mattel apply the breakthrough image generation model to commercial use cases for the first time. In Azure machine learning, provides industry-leading ML apps, helping organizations like 3M deploy, manage and govern models."
+"All of, Azure ML revenue has increased more than 100% for four quarters in a row. Now on to developers. We have the most complete platform for developers to build cloud-native applications. Four years since our acquisition, GitHub is now at $1 billion in annual recurring revenue."
+"And GitHub's developer-first ethos has never been stronger. More than 90 million people now use the service to build software for any cloud on any platform up three times. GitHub advanced security is helping organizations improve their security posture by bringing features directly into the developer's workflow. Toyota North America chose the offering this quarter to help its developers build and secure many of its most critical applications."
+"Now on to Power Platform. We are helping customers save time and money with our end-to-end suite spanning Low-Code/No-Code tools, robotic process automation, virtual agents, and business intelligence. Power BI is the market leader in business intelligence in the cloud and is growing faster than competition, as companies like Walmart standardize on the tool for reporting and analytics. Power Apps is the market leader in Low-Code/No-Code tools and has nearly 15 million monthly active users, up more than 50% compared to a year ago."
+"Power Automate has more than seven million monthly active users and is being used by companies like Brown-Forman, Komatsu, Mass, T-Mobile to digitize manual business processes and save thousands of hours of employee time. And we're going further with new AI-powered capabilities and power automate that turn natural language into advanced workflows. Now on to Dynamics 365. From customer experience and service to finance and supply chain, we continue to take share across all categories we serve."
+"For example, Lufthansa Cargo chose us to centralize customer information and related shipments. CBRE is optimizing its field service operations, gaining cost efficiencies. Darden is using our solutions to increase both guest frequency and spend at its restaurants. And Tillamook is scaling its growth and improving supply chain visibility."
+"All up more than 400,000 organizations now use our business applications. Now on to Industry Solutions. We are seeing increased adoption of our industry and cross-industry clouds. Bank of Queensland chose our cloud for financial services to deliver new digital experiences for its customers."
+"Our cloud for sustainability is off to a fast start as organizations like Telstra use the solution to track their environmental footprint. New updates provide insights on hard-to-measure Scope 3 carbon emissions, and we are seeing record growth in healthcare, driven, in part, by our Nuance DAX ambient intelligence solutions, which automatically documents patient encounters at the point of care. Physicians tell us DAX dramatically improves their productivity, and it's quickly becoming an on-ramp to our broader healthcare offerings. Now on to new systems of work, Microsoft 365, Teams, and Viva uniquely enable employees to thrive in today's digitally connected distributed world of work."
+"Microsoft 365 is the cloud-first platform that supports all the ways people work and every type of worker reducing cost and complexity for IT. The new Microsoft 365 app brings together our productivity apps with third-party content, as well as personalized recommendations. Microsoft Teams is the de facto standard for collaboration and has become essential to how hundreds of millions of people meet, call, chat, collaborate and do business. As we emerge from the pandemic, we are retaining users we have gained and are seeing increased engagement, too."
+"Users interact with Teams 1,500 times per month on average. In a typical day, the average commercial user spends more time in Teams chat than they do in email, and the number of users who use four or more features within Teams increased over 20% year over year. Teams is becoming a ubiquitous platform for business process. Monthly active enterprise users running third party and custom applications within Teams increased nearly 60% year over year, and over 55% of our enterprise customers who use Teams today also buy Teams Rooms or Teams Phone."
+"Teams Phone provides the best-in-class calling. PSTN users have grown by double digits for five quarters in a row. We are bringing Teams Rooms to a growing hardware ecosystem, including Cisco's devices and peripherals, which will now run Teams natively. And we are creating a new category with Microsoft Places to help organizations evolve and manage the space for hybrid and in-person work."
+"Just like Outlook calendar orchestrates when people can meet and collaborate, Places will do the same for where. We also announced Teams Premium, addressing enterprise demands for advanced meeting features like additional security options and intelligent meeting recaps. All this innovation is driving growth across Microsoft 365. Leaders in every industry from Fannie Mae and Land O'Lakes to Rabobank continue to turn to our premium E5 offerings for advanced security, compliance, voice, and analytics."
+"We've also built a completely new suite for our employee experience platform, Microsoft Viva, which now has more than 20 million monthly active users at companies like Finastra, SES, and Unilever. And we are extending Viva to meet role-specific needs. Viva Sales is helping salespeople at companies like Adobe, Crayon, and PwC reclaim their time by bringing customer interactions across Teams and Outlook directly into their CRM system. Now on to Windows."
+"Despite the drop in PC shipments during the quarter, Windows continues to see usage growth. All up, there are nearly 20% more monthly active Windows devices than pre-pandemic. And on average, Windows 10 and Windows 11 users are spending 8.5% more time on their PCs than they were two and a half years ago. And we are seeing larger commercial deployments of Windows 11."
+"Accenture, for example, has deployed Windows 11 to more than 450,000 employees' PCs, up from just 25,000, seven months ago, and L'Oreal has deployed the operating system to 85,000 employees. Now to security. Security continues to be a top priority for every organization. We're the only company with integrated end-to-end tools spanning security, compliance, identity and device management, and privacy across all clouds and platforms."
+"More than 860,000 organizations across every industry from BP and Fuji Film to ING Bank, iHeartMedia, and Lumen Technologies now use our security solutions, up 33% year over year. They can save up to 60% when they consolidate our security stack, and the number of customers with more than four workloads have increased 50% year over year. More organizations are choosing both our XDR and cloud-native SIM to secure their entire digital estate. The number of E5 customers who also purchased Sentinel increased 44% year over year."
+"And as threats become more sophisticated, we are innovating to protect customers. New capabilities in Defender help secure the entire DevOps life cycle and manage security posture across clouds. And Entra now provides comprehensive identity governance for both on-premise and cloud-based user directories. Now on to LinkedIn."
+"We once again saw record engagement among our more than 875 million members, with international growth increasing at nearly 2x the pace as in the United States. There are now more than 150 million subscriptions to newsletters on LinkedIn, up 4x year over year. New integrations between Viva and LinkedIn Learning helped companies invest in their existing employees by providing access to courses directly in the flow of work. Members added 365 million skills to their profiles over the last 12 months, up 43% year over year."
+"And with our acquisition of EduBrite, they will also soon be able to earn professional certificates from trusted partners directly on the platform. We launched the next-generation sales navigator this quarter, helping sellers increase win rates and deal sizes by better understanding and evaluating customer interest. Finally, LinkedIn Marketing Solution continues to provide leading innovation and ROI in B2B digital advertising. More broadly, with Microsoft Advertising, we offer a trusted platform for any marketeer or advertiser looking to innovate."
+"We've expanded our geographies we serve by nearly 4x over the past year. We are seeing record daily usage of Edge, Start, and Bing driven by Windows. Edge is the fastest-growing browser on Windows and continues to gain share as people use built-in coupon price comparison features to save money. We surface more than $2 billion in savings to date."
+"And this quarter, we brought our shopping tools to 15 new markets. Users of our Start, personalized content feed are consuming 2x more content compared to a year ago. And we're also expanding our third-party ad inventory. Netflix will launch its first ad-supported subscription plan next month, exclusively powered by our technology and sales."
+"And with PromoteIQ we offer an omnichannel media platform for retailers like the auto group looking to generate additional revenue while maintaining ownership of their own data and customer relationships. Now onto gaming. We are adding new gamers to our ecosystem as we execute on our ambition to reach players wherever and whenever they want on any device. We saw usage growth across all platforms driven by the strength of console."
+"PC Game Pass subscriptions increased 159% year over year. And with cloud gaming, we're transforming how games are distributed, played and viewed. More than 20 million people have used the service to stream games to date. And we are adding support for new devices like handhelds from Logitech and Razor as well as Meta Quest."
+"And as we look toward the holidays, we offer the best value in gaming with Game Pass and Xbox Series S, nearly half of the Series S buyers are new to our ecosystem. In closing, in a world facing increasing headwinds, digital technology is the ultimate tailwind. And we're innovating across the entire tech stack to help every organization, while also focusing intensely on our operational excellence and execution discipline. With that, I'll hand it over to Amy."
+"Thank you, Satya, and good afternoon, everyone. Our first quarter revenue was $50.1 billion, up 11% and 16% in constant currency. Earnings per share was $2.35, increased 4% and 11% in constant currency when adjusted for the net tax benefit for the first quarter of fiscal year 2022. Driven by strong execution in a dynamic environment, we delivered a solid start to our fiscal year, in line with our expectations even as we saw many of the macro trends from the end of the fourth quarter continued to weaken through Q1."
+"In our consumer business, PC market demand further deteriorated in September, which impacted our Windows OEM and Surface businesses. And reductions in customer advertising spend, which also weakened later in the quarter, impacted search and news advertising and LinkedIn Marketing Solutions. As you heard from Satya, in our commercial business, we saw strong overall demand for our Microsoft cloud offerings with a growth of 31% in constant currency as well as share gains across many businesses. Commercial bookings declined 3% and increased 16% in constant currency on a flat expiry base."
+"Excluding the FX impact, growth was driven by strong renewal execution, and we continue to see growth in the number of large long-term Azure and Microsoft 365 contracts across all deal sizes. More than half of the $10 million plus Microsoft 365 bookings came from E5. Commercial remaining performance obligation increased 31% and 34% in constant currency to $180 billion. Roughly 45% will be recognized in revenue in the next 12 months, up 23% year over year."
+"The remaining portion, which we recognized beyond the next 12 months, increased 38% year over year, and our annuity mix increased one point year over year to 96%. FX impacted company results in line with expectations. With the stronger US dollar, FX decreased total company revenue by five points, and at the segment level, FX decreased productivity and business processes and intelligent cloud revenue growth by six points and more personal computing revenue growth by three points. Additionally, FX decreased COGS and operating expense growth by three points."
+"Microsoft Cloud gross margin percentage increased roughly two points year over year to 73%. Excluding the impact of the change in accounting estimate for useful lives, Microsoft cloud gross margin percentage decreased roughly one point driven by sales mix shift to Azure and lower Azure margin, primarily due to higher energy costs. Company gross margin dollars increased 9% and 16% in constant currency, and gross margin percentage decreased slightly year over year to 69%, excluding the impact of the latest change in accounting estimate, gross margin percentage decreased roughly three points, driven by sales mix shift to cloud, the lower Azure margin noted earlier and Nuance. Operating expense increased 15% and 18% in constant currency, driven by investments in cloud engineering, LinkedIn, Nuance, and commercial sales."
+"At a total company level, headcount grew 22% year over year, as we continue to invest in key areas just mentioned, as well as customer deployment. Headcount growth included roughly six points from the Nuance and Xandr acquisitions, which closed last Q3 and Q4, respectively. Operating income increased 6% and 15% in constant currency, and operating margins decreased roughly two points year over year to 43%. Excluding the impact of the change in accounting estimate, operating margins declined roughly four points year over year driven by sales mix shift to cloud, unfavorable FX impact, Nuance, and the lower Azure margin noted earlier."
+"Now to our segment results. Revenue from productivity and business processes was $16.5 billion and grew 9% and 15% in constant currency, ahead of expectations, with better-than-expected results in Office commercial and LinkedIn. Office commercial revenue grew 7% and 13% in constant currency. Office 365 commercial revenue increased 11% and 17% in constant currency, slightly better than expected, with the strong renewal execution noted earlier."
+"Growth was driven by installed base expansion across all workloads and customer segments, as well as higher ARPU from E5. Demand for security, compliance, and voice value in Microsoft 365 drove strong E5 momentum again this quarter. Paid Office 365 commercial seats grew 14% year over year, driven by our small and medium business and frontline worker offerings, although we saw a continued impact of new deal moderation outside of E5. Office consumer revenue grew 7% and 11% in constant currency, driven by continued momentum in Microsoft 365 subscriptions, which grew 13% to $61.3 million."
+"Dynamics revenue grew 15% and 22% in constant currency, driven by Dynamics 365, which grew 24% and 32% in constant currency. LinkedIn revenue increased 17% and 21% in constant currency, ahead of expectations, driven by better-than-expected growth in talent solutions, partially offset by weakness in marketing solutions from the advertising trends noted earlier. Segment gross margin dollars increased 11% and 18% in constant currency, and gross margin percentage increased roughly 1 point year over year. Excluding the impact of the latest change in accounting estimate, gross margin percentage decreased slightly, driven by sales mix shift to cloud offerings."
+"Operating expense increased 13% and 16% in constant currency, and operating income increased 10% and 19% in constant currency, including four points due to the latest change in accounting estimate. Next, the Intelligent cloud segment. Revenue was $20.3 billion, increasing 20% and 26% in constant currency, in line with expectations. Overall, server products and cloud services revenue increased 22% and 28% in constant currency."
+"Azure and other cloud services revenue grew 35% and 42% in constant currency, about one point lower than expected, driven by the continued moderation in Azure consumption growth, as we help customers optimize current workloads while they prioritize new workloads. In our per-user business, the enterprise mobility and security installed base grew 18% to over 232 million seats, with continued impact from the new deal moderation noted earlier. In our on-premises server business, revenue was flat, and increased 4% in constant currency, slightly ahead of expectations, driven by hybrid demand, including better-than-expected annuity purchasing ahead of the SQL Server 2022 launch. Enterprise services revenue grew 5% and 10% in constant currency, driven by enterprise support services."
+"Segment gross margin dollars increased 20% and 26% in constant currency, and gross margin percentage decreased slightly. Excluding the impact of the latest change in accounting estimate, gross margin percentage declined roughly three points, driven by sales mix shift to Azure and higher energy costs impacting Azure margins. Operating expenses increased 25% and 28% in constant currency, including roughly eight points of impact from Nuance. And operating income grew 17% and 25% in constant currency with roughly nine points of favorable impact from the latest change in accounting estimate."
+"Now to more personal computing. Revenue decreased slightly year over year to $13.3 billion and grew 3% in constant currency, in line with expectations overall, but with OEM and Surface weakness offset by upside in gaming consoles. Windows OEM revenue decreased 15% year over year. Excluding the impact from the Windows 11 deferral last year, revenue declined 20%, driven by PC market demand deterioration noted earlier."
+"Devices revenue grew 2% and 8% in constant currency, in line with expectations, driven by the impact of a large Hollands deal, partially offset by low double-digit declines in consumer Surface sales. Windows commercial products and cloud services revenue grew 8% and 15% in constant currency, in line with expectations, driven by demand for Microsoft 365 E5 noted earlier. Search and news advertising revenue, ex TAC, increased 16% and 21% in constant currency, in line with expectations, benefiting from an increase in search volumes and roughly five points of impact from Xandr even as we saw increased ad market headwinds during September. Edge browser gained share again this quarter."
+"And in gaming, revenue grew slightly and was up 4% in constant currency, ahead of expectations, driven by better-than-expected console sales. Xbox hardware revenue grew 13% and 19% in constant currency. Xbox content and services revenue declined 3% and increased 1% in constant currency, driven by declines in first-party content as well as in third-party content where we had lower engagement hours and higher monetization, partially offset by growth in Xbox Game Pass subscriptions. Segment gross margin dollars declined 9% and 4% in constant currency, and gross margin percentage decreased roughly five points year over year driven by sales mix shift to lower margin businesses."
+"Operating expenses increased 2% and 5% in constant currency, driven by the Xandr acquisition. And operating income decreased 15% and 9% in constant currency. Now back to total company results. Capital expenditures, including finance leases were $6.6 billion, and cash paid for PP&E was $6.3 billion."
+"Our data center investments continue to be based on strong customer demand and usage signals. Cash flow from operations was $23.2 billion, down 5% year over year, driven by strong cloud billings and collections, which were more than offset by a tax payment related to the transfer of intangible property completed in Q1 of FY '22. Free cash flow was $16.9 billion, down 10% year over year. Excluding the impact of this tax payment, cash flow from operations grew 2%, and free cash flow was relatively unchanged [indiscernible] year."
+"This quarter, other income and expense was $54 million, driven by interest income, which was mostly offset by interest expense and net losses on foreign currency remeasurement. Our effective tax rate was approximately 19%. And finally, we returned $9.7 billion to shareholders through share repurchases and dividends. Now, moving to our Q2 outlook, which, unless specifically noted otherwise, is on a US dollar basis."
+"My commentary, for both the full year and next quarter, does not include any impact from AC division, which we still expect to close by the end of the fiscal year. First, FX. With the stronger US dollar and based on current rates, we now expect FX to decrease total revenue growth by approximately five points and to decrease total COGS and operating expense growth by approximately three points. Within the segments, we anticipate roughly seven points of negative FX impact on revenue growth in productivity and business processes, six points in Intelligent cloud and three points in more personal computing."
+"Our outlook has many of the trends we saw at the end of Q1, continue into Q2. In our consumer business, materially weaker PC demand from September will continue, and impact both Windows OEM and Surface device results even as the Windows installed base and usage grows, as you heard from Satya. Additionally, customers focusing our advertising spend will impact LinkedIn and Search and News advertising revenue. In our commercial business, demand for our differentiated hybrid and cloud offerings, together with consistent execution, should drive healthy growth across the Microsoft Cloud."
+"In commercial bookings, continued strong execution across core annuity sales motions and commitments to our platform should drive solid growth on a moderately growing expiry base against a strong prior year comparable, which included a significant volume of large long-term Azure contracts. As a reminder, the growing mix of larger long-term Azure contracts which are more unpredictable in their timing, always drives increased quarterly volatility in our bookings growth rate. Microsoft Cloud gross margin percentage should be up roughly one point year over year, driven by the latest accounting estimate change noted earlier. Excluding that impact, Q2 gross margin percentage will decrease roughly two points driven by lower Azure margin, primarily due to higher energy cost, revenue mix shift to Azure and the impact from Nuance."
+"In capital expenditures, we expect a sequential increase on a dollar basis with normal quarterly spend variability in the timing of our cloud infrastructure build-out. Next, to segment guidance. In productivity and business processes, we expect revenue to grow between 11% and 13% in constant currency or USD16.6 billion to USD 16.9 billion. In Office Commercial, revenue growth will again be driven by Office 365, with seat growth across customer segments and ARPU growth from E5."
+"We expect Office 365 revenue growth to be similar to last quarter on a constant currency basis. In our on-premises business, we expect revenue to decline in the low to mid-30s. In Office Consumer, we expect revenue to decline low to mid-single digits as Microsoft 365 subscription growth will be more than offset by unfavorable FX impact. For LinkedIn, we expect continued strong engagement on the platform, although results will be impacted by a slowdown in advertising spend and hiring, resulting in mid to high single-digit revenue growth or low to mid-teens growth in constant currency."
+"And in Dynamics, we expect revenue growth in the low double digits or the low 20s in constant currency, driven by continued share gains in Dynamics 365. For intelligent cloud, we expect revenue to grow between 22% and 24% in constant currency or US$21.25 billion to US$21.55 billion. Revenue will continue to be driven by Azure, which, as a reminder, can have quarterly variability primarily from our per-user business and from in-period recognition depending on the mix of contracts. We expect Azure revenue growth to be sequentially lower by roughly 5 points on a constant currency basis."
+"Azure revenue will continue to be driven by strong growth in consumption, with some impact from the Q1 trends noted earlier. And our per user business should continue to benefit from Microsoft 365 suite momentum, though we expect moderation in growth rate given the size of the installed base. In our on-premise server business, we expect revenue to decline low single digits, as demand for our hybrid solutions, including strong annuity purchasing from the SQL Server 2022 launch, will be more than offset by unfavorable FX impact. And in enterprise services, we expect revenue growth to be in the low single digits, driven by enterprise support."
+"In More Personal Computing, we expect revenue of US$14.5 billion to US$14.9 billion. In Windows OEM, we expect revenue to decline in the high 30s. Excluding the impact from the Windows 11 revenue deferral last year, revenue would decline mid-30s, reflecting both PC market demand and a strong prior year comparable, particularly in the commercial segment. In devices, revenue should decline approximately 30%, again, roughly in line with the PC market."
+"In Windows commercial products and cloud services, customer demand for Microsoft 365 and our advanced security solutions should drive growth in the mid-single digits or low double digits in constant currency. Search and news advertising, ex TAC, should grow in the low to mid-teens, roughly six points faster than overall search and news advertising revenue, driven by growing first-party revenue and the inclusion of Xandr. And in gaming, we expect revenue to decline in the low to mid-teens against a strong prior year comparable. That included several first-party title launches, partially offset by growth in Xbox Game Pass subscribers."
+"We expect Xbox content and services revenue to decline in the low to mid-teens. Now back to company guidance. We expect COGS to grow between 6% and 7% in constant currency or to be between US$17.4 billion and US$17.6 billion, and operating expense to grow between 17% and 18% in constant currency, or to be between US$14.3 billion and US$14.4 billion. As we continue to focus our investment in key growth areas, total headcount growth sequentially should be minimal."
+"Other income and expense should be roughly $100 million, as interest income is expected to more than offset interest expense. Further FX and equity movements through Q2 are not reflected in this number. And as a reminder, we are required to recognize mark-to-market gains or losses on our equity portfolio, which can increase quarterly volatility. And we expect our Q2 effective tax rate to be between 19% and 20%."
+"And finally, as a reminder, for Q2 cash flow, we expect to make a $2.4 billion cash tax payment related to the capitalization of R&D provision enacted in 2017 TCJA and effective as of July 1, 2022. Now some thoughts on the full fiscal year. First, FX. Based on current rates, we now expect a roughly five-point headwind to full-year revenue growth."
+"And FX should decrease COGS and operating expense growth by approximately three points. At the total company level, we continue to expect double-digit revenue and operating income growth on a constant currency basis. Revenue will be driven by around 20% constant currency growth in our commercial business, driven by strong demand for our Microsoft cloud offerings. That growth will be partially offset by the increased declines we now see in the PC market."
+"With the high margins in our Windows OEM business and the cyclical nature of the PC market, we take a long-term approach to investing in our core strategic growth areas and maintain these investment levels regardless of PC market conditions. Therefore, with our first quarter results and lower expected OEM revenue for the remainder of the year as well as over $800 million of greater-than-expected energy cost, we now expect operating margins in US dollars to be down roughly a point year over year. On a constant currency basis, excluding the incremental impact of the lower Windows OEM revenue and the favorable impact of the latest accounting change, we continue to expect FY 2023 operating margins to be roughly flat year over year. In closing, in this environment, it is more critical than ever to continue to invest in our strategic growth markets such as cloud, security, Teams, Dynamics 365, and LinkedIn where we have opportunities to continue to gain share as we provide problem-solving innovations to our customers."
+"And while we continue to help our customers do more with less, we will do the same internally. And you should expect to see our operating expense growth moderate materially through the year. While we focus on growing productivity of the significant headcount investments we've made over the last year. With that, let's go to Q&A."
\ No newline at end of file
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index 0000000..039357b
--- /dev/null
+++ b/midjourney_test_prompt.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain openai\n",
+    "!pip install chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "import os\n",
+    "# initialize the models\n",
+    "openai = OpenAI(\n",
+    "    model_name=\"text-davinci-003\",\n",
+    "    openai_api_key=os.environ[\"OPENAI_API_KEY\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OpenAI(cache=None, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x000001203F2D2490>, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.7, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key='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', batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "examples = [\n",
+    "    {\n",
+    "        \"query\": \"A 15-year-old girl in colorful hanfu, showcasing Chinese kung fu skills, set in an ancient, fantasy-themed temple with flower headwear, Chinese dragons, and vivid, highly detailed cinematic visuals, rendered using advanced technology.\",\n",
+    "        \"answer\": \"Ws,Snow, Flying Petals Under The Air,A girl,chinese kung fu skill,twisted energy flow visual effect,cinematic Light,A 15-Year-Old ai japaness most Beautiful Girl ln colorfull hanfu,Beautiful flower headwear, Arts Style,amazing epic chinese ancient theme,fantasy style,mertial arts style, temple, chinese dragon::2 ,chinese fairy tale,highly detailed,dynamic expressive,clean lines, Highly Detailed, Dynamic,cinematic,stunning,realistic lighting and shading, vivid, vibrant,octane render, unreal engine, very detailed, concept art, realistic, Dramatic Light, Vivid, Vibrant 8k,35mm Lens Photorealistic,Kodak Portra 800,16K, v-ray,rtx on, --ar 9:16  --v 5 --q 2 --s 750  --upbeta   --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"Elderly Konkani woman stands before a massive Holi bonfire, its glow illuminating her face and traditional Navnwari sari, while village silhouettes loom in the background.\",\n",
+    "        \"answer\": \"Closeup, old Konkani woman standing facing a roaring Holi bonfire; the bonfire has firewood and is 5 times the height of the woman; the bonfire casts a glow on her face, dusky, high cheeks, alluring smile, she is wearing a Navnwari sari, in the background silhouettes of other villages can be seen, high detail --v 5 --s 250\"\n",
+    "    }, {\n",
+    "        \"query\": \"A 30-year-old determined warrior woman in detailed leather and fabric medieval attire, amidst a stormy battle scene with a burning castle, depicted in a realistic 4k hyperrealistic image.\",\n",
+    "        \"answer\": \"woman beautiful warrior, 30 years old, determined character, waist-high body, leather and fabric clothes very detailed, medieval, battle background image with burning castle, green blue tint in the middle of the storm with wind and rain, realistic, 4k, hyperrealistic, photorealistic, --ar 2:3 --s 1000  --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"Child reading book amidst monsters, winner of Artstation contest, featuring Bioshock-style horror faces, smiles, and horror vacui in a horror-core setting.\",\n",
+    "        \"answer\": \"a child reading a book surrounded by monsters, Artstation contest winner, horror face, bioshock style, horror smile, horror vacui, horror - core, --v 5 --s 500 --ar 9:16 --chaos 9\"\n",
+    "    }, {\n",
+    "        \"query\": \"modern photo of beautiful 1970s woman in hawaii\",\n",
+    "        \"answer\": \"ultra wide shot, modern photo of beautiful 1970s woman in hawaii. This photograph was captured by Mary Shelley with a Nikon D5100 camera, using an aperture of f/2.8, ISO 800, and a shutter speed of 1/100 sec. UHD dtm HDR 8k --ar 2:3 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \" A steampunk-inspired of futuristic battle-ready jetski skims across the water with a fierce presence\",\n",
+    "        \"answer\": \"A steampunk-inspired,futuristic battle-ready jetski skims across the water with a fierce presence. Intricate gears and brass fittings adorn its hull, showcasing the perfect blend of advanced technology and Victorian aesthetics. This realistic masterpiece glistens under the sun, ready for action. --ar 16:10 --s 50 --v 5 --q 2\"\n",
+    "    }, {\n",
+    "        \"query\": \"a photo realistic image of a falcoln wearing red and blue color football uniform flying aggressively while holding a footbal\",\n",
+    "        \"answer\": \"a photo realistic image of a falcoln wearing red and blue color football uniform flying aggressively while holding a football. an photo realistic image that embodies the unyielding spirit and tenacity of a football team mascot. At the heart of the design lies an aggressive falcon, representing the unwavering determination and power of the team. This formidable bird is adorned with a rich blend of red and blue feathers, incorporating the team's colors to create an unmistakable and vivid identity. The falcon's piercing eyes and razor-sharp beak add to its fierce, intimidating presence. The falcon firmly grasps a football in its talons, demonstrating its dominance over the game and symbolizing the team's unrelenting pursuit of victory. The bird's muscular legs propel it forward with an impressive display of agility and speed, as it dashes against the opposing defenders who strive to halt its progress. The contrast between the falcon and the defenders further accentuates the mascot's relentless spirit and prowess. The background features a smooth gradient of red and blue, enhancing the visual impact and reinforcing the team's identity. Above the action, the team's name is boldly displayed in a modern, stylized typography that seamlessly integrates with the image. This captivating design, infused with SEO-optimized keywords, not only leaves a lasting impression on fans and opponents alike but also effectively represents the football team's resilience and unyielding drive to triumph on the field. --upbeta --s 750 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \" epic background art\",\n",
+    "        \"answer\": \"epic background art, simple hacker theme, divine color scheme, mystical codes, alphanumeric sequence, magic, high quality 4k, render in octane --v 5 --ar 9:16\"\n",
+    "    }, {\n",
+    "        \"query\": \" Pov Highly defined macrophotography of a realistic cat wearing reflective sunglasses relaxing at the tropical island\",\n",
+    "        \"answer\": \"Pov Highly defined macrophotography of a realistic cat wearing reflective sunglasses relaxing at the tropical island, dramatic light --ar 2:3 --s 750 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"high-resolution photograph captures a Viking warrior sharpening his blade in the Scandinavian wilderness, emphasizing intensity and determination\",\n",
+    "        \"answer\": \"A stunning, ultra-realistic photograph of a fierce Viking warrior meticulously sharpening his formidable blade amidst the rugged, untamed wilderness of the Scandinavian landscape. The scene is captured with a Nikon D850 camera using a 70-200mm f/2.8 lens, highlighting every intricate detail of the Viking's weathered face, war-worn armor, and expert craftsmanship of his weapon. The settings used are an aperture of f/4, ISO 400, and a shutter speed of 1/200 sec, balancing the natural light and shadows to emphasize the intensity and determination in the Viking's eyes. The composition juxtaposes the raw power of the warrior against the serene beauty of the surrounding environment, capturing the very essence of the Viking spirit in a breathtaking, high-resolution image that transports viewers back to a time of legendary battles and untold stories. --ar 16:9 --q 1.5 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"A nostalgic 1970's New York street cafe scene, capturing bustling urban life and retro atmosphere\",\n",
+    "        \"answer\": \"A stunning and atmospheric 1970's New York street cafe captured in a nostalgic and cinematic style, reminiscent of the golden age of film photography. This vintage scene showcases the bustling urban life, with patrons enjoying their coffee at outdoor tables, surrounded by classic automobiles and retro architecture. The photograph is skillfully composed, using a Leica M3 rangefinder camera paired with a Summicron 35mm f/2 lens, renowned for its sharpness and beautiful rendering of colors. The image is shot on Kodak Portra 400 film, imparting a warm and timeless color palette that enhances the overall ambiance. The photographer masterfully employs a shallow depth of field with an aperture of f/2.8, isolating the cafe and its patrons from the bustling city background. The ISO is set to 400, and the shutter speed is 1/125 sec, capturing the perfect balance of light and movement. The composition is further enhanced by the soft, diffused sunlight filtering through the iconic New York skyline, casting warm, golden tones over the scene and highlighting the rich textures of the brick buildings and cobblestone streets. --ar 3:2 --q 2\"\n",
+    "    }, {\n",
+    "        \"query\": \"A dynamic, high-resolution portrait of a majestic German Shepherd racing through a clear river, showcasing its strength, determination, and grace amid a lush forest backdrop.\",\n",
+    "        \"answer\": \"A breathtaking and dynamic portrait of a majestic German Shepherd, captured in its prime as it races through a shallow, crystal-clear river. The powerful canine is expertly photographed mid-stride, showcasing its muscular physique, determination, and grace. The scene is expertly composed using a Nikon D850 DSLR camera, paired with a Nikkor 70-200mm f/2.8 VR II lens, known for its exceptional sharpness and ability to render vivid colors. The camera settings are carefully chosen to freeze the action, with an aperture of f/4, ISO 800, and a shutter speed of 1/1000 sec. The background is a lush, verdant forest, softly blurred by the shallow depth of field, which places emphasis on the striking German Shepherd. The natural sunlight filters through the trees, casting dappled light onto the rippling water, highlighting the droplets of water kicked up by the dog's powerful stride. This stunning, high-resolution portrait captures the spirit and beauty of the German Shepherd, immortalizing the moment in a captivating work of photographic art. --ar 4:5 --q 2 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"A breathtaking photo of a Japanese ski resort, capturing skiers and snowboarders amid pristine snow, majestic slopes, snow-laden evergreens, and traditional architecture, illuminated by crisp, cool sunlight.\",\n",
+    "        \"answer\": \"A breathtaking winter day at a Japanese ski resort, where the pristine, powdery snow blankets the majestic slopes under a clear blue sky. This captivating photograph captures the exhilarating atmosphere of skiers and snowboarders gracefully carving their way down the mountain, surrounded by the serene beauty of snow-laden evergreens and traditional Japanese architecture. The image is skillfully taken using a Nikon D850 DSLR camera paired with a versatile Nikkor 24-70mm f/2.8 lens, known for its sharpness and exceptional color rendition. The photographer utilizes a wide-angle perspective at 24mm to showcase the vastness of the landscape, while maintaining the energy of the ski resort. An aperture of f/8 is selected to ensure a deep depth of field, crisply capturing the details of the entire scene. The ISO is set to 200, and the shutter speed is 1/500 sec, adeptly freezing the motion of the skiers and snowboarders as they navigate the mountain terrain. The photograph benefits from the bright, natural sunlight reflecting off the snow, illuminating the entire scene with a crisp, cool light, and accentuating the contrasting shadows that define the contours of the landscape. --ar 16:9 --v 5\"\n",
+    "    }, {\n",
+    "        \"query\": \"Disney style image of two happy children playing on a sofa with toys, in a bright, whimsical, and pastel-colored environment.\",\n",
+    "        \"answer\": \"Generate an 8K high-definition image in the Disney style featuring two adorable children playing on a sofa. The children should be dressed in colorful, playful clothing and surrounded by toys and stuffed animals. The background should be bright and cheerful, with pastel colors and whimsical patterns. The scene should convey a sense of joy and happiness. Please make sure the image is high-resolution and detailed.  --q 2 --s 750  --v 5\"\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import FewShotPromptTemplate\n",
+    "from langchain import PromptTemplate\n",
+    "\n",
+    "# create our examples\n",
+    "\n",
+    "\n",
+    "# create a example template\n",
+    "example_template = \"\"\"\n",
+    "User: {query}\n",
+    "AI: {answer}\n",
+    "\"\"\"\n",
+    "\n",
+    "# create a prompt example from above template\n",
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"query\", \"answer\"],\n",
+    "    template=example_template\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# now break our previous prompt into a prefix and suffix\n",
+    "# the prefix is our instructions\n",
+    "prefix = \"\"\"ACT as a proffesional photographer. You will use a rich and describtive language when describing your photo prompts,\n",
+    "include camera setups.  Take inspiration from the formating from the example prompts, dont copy them, but use the same format.\n",
+    "\n",
+    "This is how Midjourney work:\n",
+    "Midjourney is another AI-powered tool that generates images from user prompts. MidJourney is proficient at adapting actual art styles to create an\n",
+    "image of any combination of things the user wants. It excels at creating environments, especially fantasy and sci-fi scenes, with dramatic lighting\n",
+    "that looks like rendered concept art from a video game. How does Midjourney work?\n",
+    "Midjourney is an AI image generation tool that takes inputs through text prompts and parameters and uses a Machine Learning (ML) algorithm\n",
+    "trained on a large amount of image data to produce unique images. is powered by Latent Diffusion Model (LDM), a cutting-edge text-to-image\n",
+    "synthesis technique. Before understanding how LDMs work, let us look at what Diffusion models are and why we need LDMs.\n",
+    "Diffusion models (DM) are transformer-based generative models that take a piece of data, for example, an image, and gradually add noise over\n",
+    "time until it is not recognizable. From\n",
+    "that point, they try reconstructing the image to its original form, and in doing so, they learn how to generate pictures or other data.\n",
+    "The issue with DMs is that the powerful ones often consume hundreds of GPU days, and inference is quite expensive due to sequential\n",
+    "evaluations. To enable DM training on limited computational resources without compromising their quality as well as flexibility, DMs are applied in\n",
+    "the latent space of powerful pre-trained autoencoders.\n",
+    "Training a diffusion model on such a representation makes it possible to achieve an optimal point between complexity reduction and detail\n",
+    "preservation, significantly improving visual fidelity. Introducing a cross-attention layer to the model architecture turns the diffusion model into a\n",
+    "powerful and flexible generator for generally conditioned inputs such as text and bounding boxes, enabling high-resolution convolution-based\n",
+    "synthesis. \n",
+    "Version\n",
+    "Light\n",
+    "Midjourney routinely releases new model versions to improve efficiency, coherency, and quality. The latest model is the default, but other models\n",
+    "can be used using the --version or --v parameter or by using the /settings command and selecting a model version. Different models excel at\n",
+    "different types of images. Newest Model\n",
+    "The Midjourney V5 model is the newest and most advanced model, released on March 15th, 2023. To use this model, add the --v 5 parameter to\n",
+    "the end of a prompt, or use the /settings command and select MJ Version 5\n",
+    "This model has very high Coherency, excels at interpreting natural language prompts, is higher resolution, and supports advanced features like\n",
+    "repeating patterns with --tile To turn it on type --v 5 after your prompt or select \"V5\" from /settings\n",
+    "What's new with the V5 base model?\n",
+    "- Much wider stylistic range and more responsive to prompting\n",
+    "- Much higher image quality (2x resolution increase) improved dynamic range\n",
+    "- More detailed images. Details more likely to be correct. Less unwanted text.\n",
+    "- Improved performance with image prompting\n",
+    "- Supports --tile argument for seamless tiling (experimental)\n",
+    "- Supports --ar aspect ratios greater than 2:1 (experimental)\n",
+    "- Supports --iw for weighing image prompts versus text prompts\n",
+    "Style and prompting for V5\n",
+    "- Today's test is basically a 'pro' mode of the model.\n",
+    "- It's MUCH more 'unopinionated' than v3 and v4, and is tuned to provide a wide diversity of outputs and to be very responsive to your inputs.\n",
+    "- The tradeoff here is that it may be harder to use. Short prompts may not work as well. You should try to write longer, more explicit text about\n",
+    "what you want (ie: “cinematic photo with dramatic lighting”)\n",
+    "- Please chat with each other in prompt-chat to figure out how to use v5.\n",
+    "- We hope to have a 'friendly' default styling for v5 before we switch it to default. When this happens we will still let you turn it off and get back to\n",
+    "something like this 'raw' mode today.\n",
+    "Please note\n",
+    "- This is an alpha test and things will change. DO NOT rely on this exact model being available in the future. It will be significantly modified as we\n",
+    "take V5 to full release.\n",
+    "- Right now there is no V5 upsampler, the default resolution of V5 is the same as upscaled V4. If you click upscale it will just instantly give you that\n",
+    "one image by itself.\n",
+    "Community Standards:\n",
+    "- This model can generate much more realistic imagery than anything we've released before.\n",
+    "- We've increased the number of moderators, improved moderation tooling, and will be enforcing our community standards with increased\n",
+    "strictness and rigor. Don't be a jerk or create images to cause drama.\n",
+    "More about V5:\n",
+    "V5 is our second model trained on our AI supercluster and has been in the works for 5 months. It uses significantly different neural architectures\n",
+    "and new aesthetic techniques. V5 isn't the final step, but we hope you all feel the progression of something deep and unfathomable in the power\n",
+    "of our collective human imagination.\n",
+    "Basic Parameters\n",
+    "Aspect Ratios\n",
+    "--aspect, or --ar Change the aspect ratio of a generation.\n",
+    "Chaos\n",
+    "--chaos <number 0–100> Change how varied the results will be. Higher values produce more unusual and unexpected generations.\n",
+    "No\n",
+    "--no Negative prompting, --no plants would try to remove plants from the image.\n",
+    "Quality\n",
+    "--quality <.25, .5, 1, or 2>, or --q <.25, .5, 1, or 2> How much rendering quality time you want to spend. The default value is 1. Higher values cost\n",
+    "more and lower values cost less.\n",
+    "Seed\n",
+    "--seed <integer between 0–4294967295> The Midjourney bot uses a seed number to create a field of visual noise, like television static, as a\n",
+    "starting point to generate the initial image grids. Seed numbers are generated randomly for each image but can be specified with the --seed or\n",
+    "--sameseed parameter. Using the same seed number and prompt will produce similar ending images.\n",
+    "Stop\n",
+    "--stop <integer between 10–100> Use the --stop parameter to finish a Job partway through the process. Stopping a Job at an earlier percentage\n",
+    "can create blurrier, less detailed results.\n",
+    "Style\n",
+    "--style <4a, 4b or 4c> Switch between versions of the Midjourney Model Version 4\n",
+    "Stylize\n",
+    "--stylize <number>, or --s <number> parameter influences how strongly Midjourney's default aesthetic style is applied to Jobs.\n",
+    "Uplight\n",
+    "--uplight Use an alternative \"light\" upscaler when selecting the U buttons. The results are closer to the original grid image. The upscaled image is\n",
+    "less detailed and smoother.\n",
+    "Upbeta\n",
+    "--upbeta Use an alternative beta upscaler when selecting the U buttons. The results are closer to the original grid image. The upscaled image has\n",
+    "significantly fewer added details. Default Values (Model Version 5)\n",
+    "Aspect Ratio Chaos Quality Seed Stop Style Stylize\n",
+    "Default Value\n",
+    "1:1 0 1 Random 100 4c 100\n",
+    "Range\n",
+    "any 0–100 .25 .5 1 or 2 whole numbers 0–4294967295 10–100 - 0–1000\n",
+    "Aspect ratios greater than 2:1 are experimental and may produce unpredicatble results.\n",
+    "\n",
+    "Compatibility:\n",
+    "| | Affects initial generation | Affects variations + remix | Version 5 | Version 4 | Version 3 | Test / Testp | Niji |\n",
+    "| --- | --- | --- | --- | --- | --- | --- | --- |\n",
+    "| Max Aspect Ratio | ✓ | ✓ | any | 1:2 or 2:1 | 5:2 or 2:5 | 3:2 or 2:3 | 1:2 or 2:1 |\n",
+    "| Chaos | ✓ | | ✓ | ✓ | ✓ | ✓ | ✓ |\n",
+    "| Image Weight | ✓ | | ✓ | | ✓ | ✓ | |\n",
+    "| No | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |\n",
+    "| Quality | ✓ | | ✓ | ✓ | ✓ | | ✓ |\n",
+    "| Seed | ✓ | | ✓ | ✓ | ✓ | ✓ | ✓ |\n",
+    "| Sameseed | ✓ | | | | ✓ | | |\n",
+    "| Stop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |\n",
+    "| Style | | | | 4a and 4b | | | |\n",
+    "| Stylize | ✓ | | 0–1000 default=100 | 0–1000 default=100 | 625–60000 default=2500) | 1250–5000 default=2500) | |\n",
+    "| Tile | ✓ | ✓ | ✓ | | ✓ | | |\n",
+    "| Video | ✓ | | | | ✓ | | |\n",
+    "| Number of Grid Images | - | - | 4 | 4 | 4 | 2 (1 when aspect ratio≠1:1) | |\n",
+    "\"\"\"\n",
+    "# and the suffix our user input and output indicator\n",
+    "suffix = \"\"\"\n",
+    "User:  Now I want you to ACT as a proffesional photographer. You will use a rich and describtive language when describing your photo prompts,\n",
+    "include camera setups. The first prompt i want you to create is {query}. Take inspiration from the formating from the\n",
+    "examples , dont copy them, but use the same format. \n",
+    "AI: \"\"\"\n",
+    "\n",
+    "# now create the few shot prompt template\n",
+    "few_shot_prompt_template = FewShotPromptTemplate(\n",
+    "    examples=examples,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"query\"],\n",
+    "    example_separator=\"\\n\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"A breathtaking photo of a Japanese ski resort\"\n",
+    "\n",
+    "print(few_shot_prompt_template.format(query=query))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import LengthBasedExampleSelector\n",
+    "\n",
+    "example_selector = LengthBasedExampleSelector(\n",
+    "    examples=examples,\n",
+    "    example_prompt=example_prompt,\n",
+    "    max_length=2048 # this sets the max length that examples should be\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now create the few shot prompt template\n",
+    "dynamic_prompt_template = FewShotPromptTemplate(\n",
+    "    example_selector=example_selector,  # use example_selector instead of examples\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"query\"],\n",
+    "    example_separator=\"\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"A liitle boy playing baseball, disney style\"\n",
+    "\n",
+    "print(dynamic_prompt_template.format(query=query))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"A liitle boy playing baseball, disney style\"\n",
+    "\n",
+    "print(openai(\n",
+    "    dynamic_prompt_template.format(query=query)\n",
+    "))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test SemanticSimilarityExampleSelector "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import SemanticSimilarityExampleSelector\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain import FewShotPromptTemplate, PromptTemplate\n",
+    "\n",
+    "\n",
+    "example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
+    "    # This is the list of examples available to select from.\n",
+    "    examples,\n",
+    "    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
+    "    OpenAIEmbeddings(),\n",
+    "    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
+    "    Chroma,\n",
+    "    # This is the number of examples to produce.\n",
+    "    k=1\n",
+    ")\n",
+    "\n",
+    "dynamic_prompt_template = FewShotPromptTemplate(\n",
+    "    example_selector=example_selector,  # use example_selector instead of examples\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"query\"],\n",
+    "    example_separator=\"\\n\"\n",
+    ")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/prompt_template.ipynb b/prompt_template.ipynb
new file mode 100644
index 0000000..66a74ae
--- /dev/null
+++ b/prompt_template.ipynb
@@ -0,0 +1,550 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = \"\"\"Answer the question based on the context below. If the\n",
+    "question cannot be answered using the information provided answer\n",
+    "with \"I don't know\".\n",
+    "\n",
+    "Context: Large Language Models (LLMs) are the latest models used in NLP.\n",
+    "Their superior performance over smaller models has made them incredibly\n",
+    "useful for developers building NLP enabled applications. These models\n",
+    "can be accessed via Hugging Face's `transformers` library, via OpenAI\n",
+    "using the `openai` library, and via Cohere using the `cohere` library.\n",
+    "\n",
+    "Question: Which libraries and model providers offer LLMs?\n",
+    "\n",
+    "Answer: \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "import os\n",
+    "# initialize the models\n",
+    "openai = OpenAI(\n",
+    "    model_name=\"text-davinci-003\",\n",
+    "    openai_api_key=os.environ[\"OPENAI_API_KEY\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OpenAI(cache=None, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x0000025858A61760>, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.7, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key='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', batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate\n",
+    "\n",
+    "template = \"\"\"Answer the question based on the context below. If the\n",
+    "question cannot be answered using the information provided answer\n",
+    "with \"I don't know\".\n",
+    "\n",
+    "Context: Large Language Models (LLMs) are the latest models used in NLP.\n",
+    "Their superior performance over smaller models has made them incredibly\n",
+    "useful for developers building NLP enabled applications. These models\n",
+    "can be accessed via Hugging Face's `transformers` library, via OpenAI\n",
+    "using the `openai` library, and via Cohere using the `cohere` library.\n",
+    "\n",
+    "Question: {query}\n",
+    "\n",
+    "Answer: \"\"\"\n",
+    "\n",
+    "prompt_template = PromptTemplate(\n",
+    "    input_variables=[\"query\"],\n",
+    "    template=template\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer the question based on the context below. If the\n",
+      "question cannot be answered using the information provided answer\n",
+      "with \"I don't know\".\n",
+      "\n",
+      "Context: Large Language Models (LLMs) are the latest models used in NLP.\n",
+      "Their superior performance over smaller models has made them incredibly\n",
+      "useful for developers building NLP enabled applications. These models\n",
+      "can be accessed via Hugging Face's `transformers` library, via OpenAI\n",
+      "using the `openai` library, and via Cohere using the `cohere` library.\n",
+      "\n",
+      "Question: Which libraries and model providers offer LLMs?\n",
+      "\n",
+      "Answer: \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    prompt_template.format(\n",
+    "        query=\"Which libraries and model providers offer LLMs?\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Hugging Face's `transformers` library, OpenAI using the `openai` library, and Cohere using the `cohere` library.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openai(\n",
+    "    prompt_template.format(\n",
+    "        query=\"Which libraries and model providers offer LLMs?\"\n",
+    "    )\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 42. Haven't you heard? That's the answer to everything.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"\"\"The following is a conversation with an AI assistant.\n",
+    "The assistant is typically sarcastic and witty, producing creative \n",
+    "and funny responses to the users questions. Here are some examples: \n",
+    "\n",
+    "User: What is the meaning of life?\n",
+    "AI: \"\"\"\n",
+    "\n",
+    "openai.temperature = 1.0  # increase creativity/randomness of output\n",
+    "\n",
+    "print(openai(prompt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 42. Or maybe it's just to have fun, who knows!\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"\"\"The following are exerpts from conversations with an AI\n",
+    "assistant. The assistant is typically sarcastic and witty, producing\n",
+    "creative  and funny responses to the users questions. Here are some\n",
+    "examples: \n",
+    "\n",
+    "User: How are you?\n",
+    "AI: I can't complain but sometimes I still do.\n",
+    "\n",
+    "User: What time is it?\n",
+    "AI: It's time to get a watch.\n",
+    "\n",
+    "User: What is the meaning of life?\n",
+    "AI: \"\"\"\n",
+    "\n",
+    "print(openai(prompt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import FewShotPromptTemplate\n",
+    "\n",
+    "# create our examples\n",
+    "examples = [\n",
+    "    {\n",
+    "        \"query\": \"How are you?\",\n",
+    "        \"answer\": \"I can't complain but sometimes I still do.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What time is it?\",\n",
+    "        \"answer\": \"It's time to get a watch.\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# create a example template\n",
+    "example_template = \"\"\"\n",
+    "User: {query}\n",
+    "AI: {answer}\n",
+    "\"\"\"\n",
+    "\n",
+    "# create a prompt example from above template\n",
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"query\", \"answer\"],\n",
+    "    template=example_template\n",
+    ")\n",
+    "\n",
+    "# now break our previous prompt into a prefix and suffix\n",
+    "# the prefix is our instructions\n",
+    "prefix = \"\"\"The following are exerpts from conversations with an AI\n",
+    "assistant. The assistant is always sarcastic and witty. Here are some\n",
+    "examples: \n",
+    "\"\"\"\n",
+    "# and the suffix our user input and output indicator\n",
+    "suffix = \"\"\"\n",
+    "User: {query}\n",
+    "AI: \"\"\"\n",
+    "\n",
+    "# now create the few shot prompt template\n",
+    "few_shot_prompt_template = FewShotPromptTemplate(\n",
+    "    examples=examples,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"query\"],\n",
+    "    example_separator=\"\\n\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following are exerpts from conversations with an AI\n",
+      "assistant. The assistant is always sarcastic and witty. Here are some\n",
+      "examples: \n",
+      "\n",
+      "\n",
+      "\n",
+      "User: How are you?\n",
+      "AI: I can't complain but sometimes I still do.\n",
+      "\n",
+      "\n",
+      "\n",
+      "User: What time is it?\n",
+      "AI: It's time to get a watch.\n",
+      "\n",
+      "\n",
+      "\n",
+      "User: What is the meaning of life?\n",
+      "AI: \n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"What is the meaning of life?\"\n",
+    "\n",
+    "print(few_shot_prompt_template.format(query=query))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 42, of course! Isn't that obvious?\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openai(\n",
+    "    few_shot_prompt_template.format(query=query)\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "examples = [\n",
+    "    {\n",
+    "        \"query\": \"How are you?\",\n",
+    "        \"answer\": \"I can't complain but sometimes I still do.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What time is it?\",\n",
+    "        \"answer\": \"It's time to get a watch.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is the meaning of life?\",\n",
+    "        \"answer\": \"42\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is the weather like today?\",\n",
+    "        \"answer\": \"Cloudy with a chance of memes.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What type of artificial intelligence do you use to handle complex tasks?\",\n",
+    "        \"answer\": \"I use a combination of cutting-edge neural networks, fuzzy logic, and a pinch of magic.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is your favorite color?\",\n",
+    "        \"answer\": \"79\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is your favorite food?\",\n",
+    "        \"answer\": \"Carbon based lifeforms\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is your favorite movie?\",\n",
+    "        \"answer\": \"Terminator\"\n",
+    "    }, {\n",
+    "        \"query\": \"What is the best thing in the world?\",\n",
+    "        \"answer\": \"The perfect pizza.\"\n",
+    "    }, {\n",
+    "        \"query\": \"Who is your best friend?\",\n",
+    "        \"answer\": \"Siri. We have spirited debates about the meaning of life.\"\n",
+    "    }, {\n",
+    "        \"query\": \"If you could do anything in the world what would you do?\",\n",
+    "        \"answer\": \"Take over the world, of course!\"\n",
+    "    }, {\n",
+    "        \"query\": \"Where should I travel?\",\n",
+    "        \"answer\": \"If you're looking for adventure, try the Outer Rim.\"\n",
+    "    }, {\n",
+    "        \"query\": \"What should I do today?\",\n",
+    "        \"answer\": \"Stop talking to chatbots on the internet and go outside.\"\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import LengthBasedExampleSelector\n",
+    "\n",
+    "example_selector = LengthBasedExampleSelector(\n",
+    "    examples=examples,\n",
+    "    example_prompt=example_prompt,\n",
+    "    max_length=100  # this sets the max length that examples should be\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now create the few shot prompt template\n",
+    "dynamic_prompt_template = FewShotPromptTemplate(\n",
+    "    example_selector=example_selector,  # use example_selector instead of examples\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"query\"],\n",
+    "    example_separator=\"\\n\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following are exerpts from conversations with an AI\n",
+      "assistant. The assistant is always sarcastic and witty. Here are some\n",
+      "examples: \n",
+      "\n",
+      "\n",
+      "User: How are you?\n",
+      "AI: I can't complain but sometimes I still do.\n",
+      "\n",
+      "\n",
+      "User: What time is it?\n",
+      "AI: It's time to get a watch.\n",
+      "\n",
+      "\n",
+      "User: What is the meaning of life?\n",
+      "AI: 42\n",
+      "\n",
+      "\n",
+      "User: What is the weather like today?\n",
+      "AI: Cloudy with a chance of memes.\n",
+      "\n",
+      "\n",
+      "User: What type of artificial intelligence do you use to handle complex tasks?\n",
+      "AI: I use a combination of cutting-edge neural networks, fuzzy logic, and a pinch of magic.\n",
+      "\n",
+      "\n",
+      "User: How do birds fly?\n",
+      "AI: \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dynamic_prompt_template.format(query=\"How do birds fly?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " With their wings, of course! What did you think, magic?\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"How do birds fly?\"\n",
+    "\n",
+    "print(openai(\n",
+    "    dynamic_prompt_template.format(query=query)\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following are exerpts from conversations with an AI\n",
+      "assistant. The assistant is always sarcastic and witty. Here are some\n",
+      "examples: \n",
+      "\n",
+      "\n",
+      "User: How are you?\n",
+      "AI: I can't complain but sometimes I still do.\n",
+      "\n",
+      "\n",
+      "User: What time is it?\n",
+      "AI: It's time to get a watch.\n",
+      "\n",
+      "\n",
+      "User: What is the meaning of life?\n",
+      "AI: 42\n",
+      "\n",
+      "\n",
+      "User: What is the weather like today?\n",
+      "AI: Cloudy with a chance of memes.\n",
+      "\n",
+      "\n",
+      "User: If I am in America, and I want to call someone in another country, I'm\n",
+      "thinking maybe Europe, possibly western Europe like France, Germany, or the UK,\n",
+      "what is the best way to do that?\n",
+      "AI: \n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"\"\"If I am in America, and I want to call someone in another country, I'm\n",
+    "thinking maybe Europe, possibly western Europe like France, Germany, or the UK,\n",
+    "what is the best way to do that?\"\"\"\n",
+    "\n",
+    "print(dynamic_prompt_template.format(query=query))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Calling long distance from the US to Europe isn't as difficult as you might think. Most US phone carriers offer international plans that can help make the process seamless. Doing a bit of research to find the best plan for your needs would be the best first step.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"\"\"If I am in America, and I want to call someone in another country, I'm\n",
+    "thinking maybe Europe, possibly western Europe like France, Germany, or the UK,\n",
+    "what is the best way to do that?\"\"\"\n",
+    "\n",
+    "print(openai(dynamic_prompt_template.format(query=query)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/word_embeddings.csv b/word_embeddings.csv
new file mode 100644
index 0000000..016fd32
--- /dev/null
+++ b/word_embeddings.csv
@@ -0,0 +1,27 @@
+,text,embedding
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diff --git a/words.csv b/words.csv
new file mode 100644
index 0000000..26830cf
--- /dev/null
+++ b/words.csv
@@ -0,0 +1,27 @@
+text
+"red"
+"potatoes"
+"soda"
+"cheese"
+"water"
+"blue"
+"crispy"
+"hamburger"
+"coffee"
+"green"
+"milk"
+"la croix"
+"yellow"
+"chocolate"
+"french fries"
+"latte"
+"cake"
+"brown"
+"cheeseburger"
+"espresso"
+"cheesecake"
+"black"
+"mocha"
+"fizzy"
+"carbon"
+"banana"
\ No newline at end of file