dataapps/openaiqa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://platform.openai.com/docs/guides/embeddings/what-are-embeddings?lang=python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "response = client.embeddings.create(\n",
    "    input=\"Help me search LLM technologies\",\n",
    "    model=\"text-embedding-3-small\"\n",
    ")\n",
    "\n",
    "print(response.data[0].embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding=response.data[0].embedding\n",
    "len(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the length of the embedding vector will be 1536 for text-embedding-3-small or 3072 for text-embedding-3-large. You can reduce the dimensions of the embedding by passing in the dimensions parameter without the embedding losing its concept-representing properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings_from_dataset.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kaikailiu/Documents/MyRepo/DeepDataMiningLearning/dataapps\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ohana_Resources.pdf      diamonds.csv             sample.txt\n",
      "Sunflower.jpg            elections.csv            shakespeare.txt\n",
      "baby.csv                 fake-hate.csv            sjsuimag1.jpg\n",
      "bus.jpg                  fine_food_reviews_1k.csv sjsupeople.jpg\n"
     ]
    }
   ],
   "source": [
    "!ls ../sampledata/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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     "execution_count": 6,
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   ],
   "source": [
    "import pandas as pd\n",
    "import tiktoken #pip install tiktoken\n",
    "\n",
    "# load & inspect dataset\n",
    "input_datapath = \"../sampledata/fine_food_reviews_1k.csv\"  # to save space, we provide a pre-filtered dataset\n",
    "df = pd.read_csv(input_datapath, index_col=0)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003JK537S</td>\n",
       "      <td>A3JBPC3WFUT5ZP</td>\n",
       "      <td>1</td>\n",
       "      <td>Arrived in pieces</td>\n",
       "      <td>Not pleased at all. When I opened the box, mos...</td>\n",
       "      <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B000JMBE7M</td>\n",
       "      <td>AQX1N6A51QOKG</td>\n",
       "      <td>4</td>\n",
       "      <td>It isn't blanc mange, but isn't bad . . .</td>\n",
       "      <td>I'm not sure that custard is really custard wi...</td>\n",
       "      <td>Title: It isn't blanc mange, but isn't bad . ....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B004AHGBX4</td>\n",
       "      <td>A2UY46X0OSNVUQ</td>\n",
       "      <td>3</td>\n",
       "      <td>These also have SALT and it's not sea salt.</td>\n",
       "      <td>I like the fact that you can see what you're g...</td>\n",
       "      <td>Title: These also have SALT and it's not sea s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B001BORBHO</td>\n",
       "      <td>A1AFOYZ9HSM2CZ</td>\n",
       "      <td>5</td>\n",
       "      <td>Happy with the product</td>\n",
       "      <td>My dog was suffering with itchy skin.  He had ...</td>\n",
       "      <td>Title: Happy with the product; Content: My dog...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B008PSM0BQ</td>\n",
       "      <td>A3OUFIMGL2K6RS</td>\n",
       "      <td>4</td>\n",
       "      <td>Good Sauce</td>\n",
       "      <td>This is a good all purpose sauce.  Has good fl...</td>\n",
       "      <td>Title: Good Sauce; Content: This is a good all...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B008YA1LQK</td>\n",
       "      <td>A9YEAAQVHFUTX</td>\n",
       "      <td>5</td>\n",
       "      <td>Blackcat</td>\n",
       "      <td>Great coffee!  Love all Green Mountain coffee ...</td>\n",
       "      <td>Title: Blackcat; Content: Great coffee!  Love ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B001KP6B98</td>\n",
       "      <td>ABWCUS3HBDZRS</td>\n",
       "      <td>5</td>\n",
       "      <td>Excellent product</td>\n",
       "      <td>After scouring every store in town for orange ...</td>\n",
       "      <td>Title: Excellent product; Content: After scour...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B008YA1LQK</td>\n",
       "      <td>A2RSB6FVQ9K9OD</td>\n",
       "      <td>5</td>\n",
       "      <td>Bulk k-Cups</td>\n",
       "      <td>This is the best way to buy coffee for my offi...</td>\n",
       "      <td>Title: Bulk k-Cups; Content: This is the best ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B001E5E2QI</td>\n",
       "      <td>A23WYVBCNE75X1</td>\n",
       "      <td>3</td>\n",
       "      <td>It's Okay</td>\n",
       "      <td>Next time, I will buy Gevalia Irish Cream deca...</td>\n",
       "      <td>Title: It's Okay; Content: Next time, I will b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time   ProductId          UserId  Score  \\\n",
       "0  1351123200  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "1  1351123200  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
       "2  1351123200  B000JMBE7M   AQX1N6A51QOKG      4   \n",
       "3  1351123200  B004AHGBX4  A2UY46X0OSNVUQ      3   \n",
       "4  1351123200  B001BORBHO  A1AFOYZ9HSM2CZ      5   \n",
       "5  1351123200  B008PSM0BQ  A3OUFIMGL2K6RS      4   \n",
       "6  1351123200  B008YA1LQK   A9YEAAQVHFUTX      5   \n",
       "7  1351123200  B001KP6B98   ABWCUS3HBDZRS      5   \n",
       "8  1351123200  B008YA1LQK  A2RSB6FVQ9K9OD      5   \n",
       "9  1351123200  B001E5E2QI  A23WYVBCNE75X1      3   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  where does one  start...and stop... with a tre...   \n",
       "1                                  Arrived in pieces   \n",
       "2          It isn't blanc mange, but isn't bad . . .   \n",
       "3        These also have SALT and it's not sea salt.   \n",
       "4                             Happy with the product   \n",
       "5                                         Good Sauce   \n",
       "6                                           Blackcat   \n",
       "7                                  Excellent product   \n",
       "8                                        Bulk k-Cups   \n",
       "9                                          It's Okay   \n",
       "\n",
       "                                                Text  \\\n",
       "0  Wanted to save some to bring to my Chicago fam...   \n",
       "1  Not pleased at all. When I opened the box, mos...   \n",
       "2  I'm not sure that custard is really custard wi...   \n",
       "3  I like the fact that you can see what you're g...   \n",
       "4  My dog was suffering with itchy skin.  He had ...   \n",
       "5  This is a good all purpose sauce.  Has good fl...   \n",
       "6  Great coffee!  Love all Green Mountain coffee ...   \n",
       "7  After scouring every store in town for orange ...   \n",
       "8  This is the best way to buy coffee for my offi...   \n",
       "9  Next time, I will buy Gevalia Irish Cream deca...   \n",
       "\n",
       "                                            combined  \n",
       "0  Title: where does one  start...and stop... wit...  \n",
       "1  Title: Arrived in pieces; Content: Not pleased...  \n",
       "2  Title: It isn't blanc mange, but isn't bad . ....  \n",
       "3  Title: These also have SALT and it's not sea s...  \n",
       "4  Title: Happy with the product; Content: My dog...  \n",
       "5  Title: Good Sauce; Content: This is a good all...  \n",
       "6  Title: Blackcat; Content: Great coffee!  Love ...  \n",
       "7  Title: Excellent product; Content: After scour...  \n",
       "8  Title: Bulk k-Cups; Content: This is the best ...  \n",
       "9  Title: It's Okay; Content: Next time, I will b...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"Time\", \"ProductId\", \"UserId\", \"Score\", \"Summary\", \"Text\"]]\n",
    "df = df.dropna()\n",
    "df[\"combined\"] = (\n",
    "    \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
    ")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subsample to 1k most recent reviews and remove samples that are too long\n",
    "top_n = 1000\n",
    "df = df.sort_values(\"Time\").tail(top_n * 2)  # first cut to first 2k entries, assuming less than half will be filtered out\n",
    "df.drop(\"Time\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "\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>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>B008JKTTUA</td>\n",
       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
       "      <td>Should advertise coconut as an ingredient more...</td>\n",
       "      <td>First, these should be called Mac - Coconut ba...</td>\n",
       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>B000LKTTTW</td>\n",
       "      <td>A14MQ40CCU8B13</td>\n",
       "      <td>5</td>\n",
       "      <td>Best tomato soup</td>\n",
       "      <td>I have a hard time finding packaged food of an...</td>\n",
       "      <td>Title: Best tomato soup; Content: I have a har...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>B001D09KAM</td>\n",
       "      <td>A34XBAIFT02B60</td>\n",
       "      <td>1</td>\n",
       "      <td>Should advertise coconut as an ingredient more...</td>\n",
       "      <td>First, these should be called Mac - Coconut ba...</td>\n",
       "      <td>Title: Should advertise coconut as an ingredie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>B001D09KAM</td>\n",
       "      <td>A1XV4W7JWX341C</td>\n",
       "      <td>5</td>\n",
       "      <td>Loved these gluten free healthy bars, saved $$...</td>\n",
       "      <td>These Kind Bars are so good and healthy &amp; glut...</td>\n",
       "      <td>Title: Loved these gluten free healthy bars, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>B002JA06Z8</td>\n",
       "      <td>A3ESIUM1JTR7KK</td>\n",
       "      <td>5</td>\n",
       "      <td>These fresh berries are truly MIRACULOUS!!!</td>\n",
       "      <td>I have ordered from Ethans on three separate o...</td>\n",
       "      <td>Title: These fresh berries are truly MIRACULOU...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>B002HQNCBO</td>\n",
       "      <td>A1UW65ZMZ3UWD3</td>\n",
       "      <td>5</td>\n",
       "      <td>Baconnaise</td>\n",
       "      <td>If you are a fan of bacon you're going to like...</td>\n",
       "      <td>Title: Baconnaise; Content: If you are a fan o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>B008JKTTUA</td>\n",
       "      <td>A1XV4W7JWX341C</td>\n",
       "      <td>5</td>\n",
       "      <td>Loved these gluten free healthy bars, saved $$...</td>\n",
       "      <td>These Kind Bars are so good and healthy &amp; glut...</td>\n",
       "      <td>Title: Loved these gluten free healthy bars, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>B0048GRNZM</td>\n",
       "      <td>AXG287OY16WWL</td>\n",
       "      <td>1</td>\n",
       "      <td>Cute</td>\n",
       "      <td>For some reason I thought that you got three \"...</td>\n",
       "      <td>Title: Cute; Content: For some reason I though...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId          UserId  Score  \\\n",
       "0    B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "296  B008JKTTUA  A34XBAIFT02B60      1   \n",
       "295  B000LKTTTW  A14MQ40CCU8B13      5   \n",
       "294  B001D09KAM  A34XBAIFT02B60      1   \n",
       "293  B001D09KAM  A1XV4W7JWX341C      5   \n",
       "292  B002JA06Z8  A3ESIUM1JTR7KK      5   \n",
       "291  B002HQNCBO  A1UW65ZMZ3UWD3      5   \n",
       "290  B008JKTTUA  A1XV4W7JWX341C      5   \n",
       "289  B0048GRNZM   AXG287OY16WWL      1   \n",
       "\n",
       "                                               Summary  \\\n",
       "0    where does one  start...and stop... with a tre...   \n",
       "297                   Good, but not Wolfgang Puck good   \n",
       "296  Should advertise coconut as an ingredient more...   \n",
       "295                                   Best tomato soup   \n",
       "294  Should advertise coconut as an ingredient more...   \n",
       "293  Loved these gluten free healthy bars, saved $$...   \n",
       "292        These fresh berries are truly MIRACULOUS!!!   \n",
       "291                                         Baconnaise   \n",
       "290  Loved these gluten free healthy bars, saved $$...   \n",
       "289                                               Cute   \n",
       "\n",
       "                                                  Text  \\\n",
       "0    Wanted to save some to bring to my Chicago fam...   \n",
       "297  Honestly, I have to admit that I expected a li...   \n",
       "296  First, these should be called Mac - Coconut ba...   \n",
       "295  I have a hard time finding packaged food of an...   \n",
       "294  First, these should be called Mac - Coconut ba...   \n",
       "293  These Kind Bars are so good and healthy & glut...   \n",
       "292  I have ordered from Ethans on three separate o...   \n",
       "291  If you are a fan of bacon you're going to like...   \n",
       "290  These Kind Bars are so good and healthy & glut...   \n",
       "289  For some reason I thought that you got three \"...   \n",
       "\n",
       "                                              combined  \n",
       "0    Title: where does one  start...and stop... wit...  \n",
       "297  Title: Good, but not Wolfgang Puck good; Conte...  \n",
       "296  Title: Should advertise coconut as an ingredie...  \n",
       "295  Title: Best tomato soup; Content: I have a har...  \n",
       "294  Title: Should advertise coconut as an ingredie...  \n",
       "293  Title: Loved these gluten free healthy bars, s...  \n",
       "292  Title: These fresh berries are truly MIRACULOU...  \n",
       "291  Title: Baconnaise; Content: If you are a fan o...  \n",
       "290  Title: Loved these gluten free healthy bars, s...  \n",
       "289  Title: Cute; Content: For some reason I though...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Encoding 'cl100k_base'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_model = \"text-embedding-3-small\"\n",
    "embedding_encoding = \"cl100k_base\"\n",
    "max_tokens = 8000  # the maximum for text-embedding-3-small is 8191\n",
    "\n",
    "encoding = tiktoken.get_encoding(embedding_encoding)\n",
    "encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get embeddings and save them for future reuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# omit reviews that are too long to embed\n",
    "df[\"n_tokens\"] = df.combined.apply(lambda x: len(encoding.encode(x)))\n",
    "df = df[df.n_tokens <= max_tokens].tail(top_n)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kaikailiu/miniconda3/envs/mypy310/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from openai_embedding_utils import get_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 0.024209363386034966,\n",
       " 0.03811987116932869,\n",
       " 0.0225446168333292,\n",
       " -0.00040670778253115714,\n",
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       " -0.0049272263422608376,\n",
       " 0.04246514290571213,\n",
       " -0.01929977349936962,\n",
       " -0.022262457758188248,\n",
       " -0.01618189923465252,\n",
       " 0.013727104291319847,\n",
       " 0.017127137631177902,\n",
       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = get_embedding(\"Help me search LLM technologies\", model=embedding_model)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai_embedding_utils import distances_from_embeddings, cosine_similarity\n",
    "distance = distances_from_embeddings(query_embedding=a, embeddings=embedding)\n",
    "similarity = cosine_similarity(a, embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This may take a few minutes\n",
    "df[\"embedding\"] = df.combined.apply(lambda x: get_embedding(x, model=embedding_model))\n",
    "df.to_csv(\"data/fine_food_reviews_with_embeddings_1k.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://cookbook.openai.com/examples/semantic_text_search_using_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ast import literal_eval\n",
    "\n",
    "datafile_path = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df = pd.read_csv(datafile_path)\n",
    "df[\"embedding\"] = df.embedding.apply(literal_eval).apply(np.array)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai_embedding_utils import get_embedding, cosine_similarity\n",
    "\n",
    "# search through the reviews for a specific product\n",
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = get_embedding(\n",
    "        product_description,\n",
    "        model=\"text-embedding-3-small\"\n",
    "    )\n",
    "    df[\"similarity\"] = df.embedding.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:200])\n",
    "            print()\n",
    "    return results\n",
    "\n",
    "\n",
    "results = search_reviews(df, \"delicious beans\", n=3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI Q&A\n",
    "ref: https://platform.openai.com/docs/tutorials/web-qa-embeddings\n",
    "https://github.com/openai/openai-cookbook/blob/main/apps/web-crawl-q-and-a/web-qa.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://cookbook.openai.com/examples/question_answering_using_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# models\n",
    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
    "GPT_MODEL = \"gpt-3.5-turbo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "article = \"\"\"A US drone strike on a car in Baghdad has killed three members of the powerful Kataib Hezbollah militia, including a high-ranking commander, officials said after a string of blasts were heard in the Iraqi capital.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Three people were killed in the US drone strike on a car in Baghdad.\n"
     ]
    }
   ],
   "source": [
    "query = f\"\"\"Use the below article to answer the subsequent question. If the answer cannot be found, write \"I don't know.\"\n",
    "\n",
    "Article:\n",
    "\\\"\\\"\\\"\n",
    "{article}\n",
    "\\\"\\\"\\\"\n",
    "\n",
    "Question: How many people are killed?\"\"\"\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    messages=[\n",
    "        {'role': 'system', 'content': 'You answer questions about the provided article.'},\n",
    "        {'role': 'user', 'content': query},\n",
    "    ],\n",
    "    model=GPT_MODEL,\n",
    "    temperature=0,\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download pre-chunked text and pre-computed embeddings\n",
    "# this file is ~200 MB, so may take a minute depending on your connection speed\n",
    "embeddings_path = \"https://cdn.openai.com/API/examples/data/winter_olympics_2022.csv\"\n",
    "\n",
    "df = pd.read_csv(embeddings_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...</td>\n",
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       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==His...</td>\n",
       "      <td>[0.0033927420154213905, -0.007447326090186834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.00915789045393467, -0.008366798982024193, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[0.0030951891094446182, -0.006064314860850573,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.002936174161732197, -0.006185177247971296,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.0069602788425982, -0.003149641677737236, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n{{short description|Russ...</td>\n",
       "      <td>[-0.013576720841228962, -0.012421397492289543,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...</td>\n",
       "      <td>[-0.007685251533985138, -0.024144049733877182,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==International play==\\n...</td>\n",
       "      <td>[-0.013910852372646332, -0.01668008230626583, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...</td>\n",
       "      <td>[-0.006781470030546188, -0.007359683513641357,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...   \n",
       "1  Lviv bid for the 2022 Winter Olympics\\n\\n==His...   \n",
       "2  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "3  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "4  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "5  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "6  Damir Sharipzyanov\\n\\n{{short description|Russ...   \n",
       "7  Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...   \n",
       "8  Damir Sharipzyanov\\n\\n==International play==\\n...   \n",
       "9  Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...   \n",
       "\n",
       "                                           embedding  \n",
       "0  [-0.005021067801862955, 0.00026050032465718687...  \n",
       "1  [0.0033927420154213905, -0.007447326090186834,...  \n",
       "2  [-0.00915789045393467, -0.008366798982024193, ...  \n",
       "3  [0.0030951891094446182, -0.006064314860850573,...  \n",
       "4  [-0.002936174161732197, -0.006185177247971296,...  \n",
       "5  [-0.0069602788425982, -0.003149641677737236, -...  \n",
       "6  [-0.013576720841228962, -0.012421397492289543,...  \n",
       "7  [-0.007685251533985138, -0.024144049733877182,...  \n",
       "8  [-0.013910852372646332, -0.01668008230626583, ...  \n",
       "9  [-0.006781470030546188, -0.007359683513641357,...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast  # for converting embeddings saved as strings back to arrays\n",
    "# convert embeddings from CSV str type back to list type\n",
    "df['embedding'] = df['embedding'].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>embedding</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...</td>\n",
       "      <td>[-0.005021067801862955, 0.00026050032465718687...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==His...</td>\n",
       "      <td>[0.0033927420154213905, -0.007447326090186834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.00915789045393467, -0.008366798982024193, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[0.0030951891094446182, -0.006064314860850573,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.002936174161732197, -0.006185177247971296,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.0069602788425982, -0.003149641677737236, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n{{short description|Russ...</td>\n",
       "      <td>[-0.013576720841228962, -0.012421397492289543,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...</td>\n",
       "      <td>[-0.007685251533985138, -0.024144049733877182,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==International play==\\n...</td>\n",
       "      <td>[-0.013910852372646332, -0.01668008230626583, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...</td>\n",
       "      <td>[-0.006781470030546188, -0.007359683513641357,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...   \n",
       "1  Lviv bid for the 2022 Winter Olympics\\n\\n==His...   \n",
       "2  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "3  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "4  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "5  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "6  Damir Sharipzyanov\\n\\n{{short description|Russ...   \n",
       "7  Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...   \n",
       "8  Damir Sharipzyanov\\n\\n==International play==\\n...   \n",
       "9  Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...   \n",
       "\n",
       "                                           embedding  \n",
       "0  [-0.005021067801862955, 0.00026050032465718687...  \n",
       "1  [0.0033927420154213905, -0.007447326090186834,...  \n",
       "2  [-0.00915789045393467, -0.008366798982024193, ...  \n",
       "3  [0.0030951891094446182, -0.006064314860850573,...  \n",
       "4  [-0.002936174161732197, -0.006185177247971296,...  \n",
       "5  [-0.0069602788425982, -0.003149641677737236, -...  \n",
       "6  [-0.013576720841228962, -0.012421397492289543,...  \n",
       "7  [-0.007685251533985138, -0.024144049733877182,...  \n",
       "8  [-0.013910852372646332, -0.01668008230626583, ...  \n",
       "9  [-0.006781470030546188, -0.007359683513641357,...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...\n",
       "1       Lviv bid for the 2022 Winter Olympics\\n\\n==His...\n",
       "2       Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...\n",
       "3       Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...\n",
       "4       Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...\n",
       "                              ...                        \n",
       "6054    Anaïs Chevalier-Bouchet\\n\\n==Personal life==\\n...\n",
       "6055    Uliana Nigmatullina\\n\\n{{short description|Rus...\n",
       "6056    Uliana Nigmatullina\\n\\n==Biathlon results==\\n\\...\n",
       "6057    Uliana Nigmatullina\\n\\n==Biathlon results==\\n\\...\n",
       "6058    Uliana Nigmatullina\\n\\n==Biathlon results==\\n\\...\n",
       "Name: text, Length: 6059, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...</td>\n",
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       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==His...</td>\n",
       "      <td>[0.0033927420154213905, -0.007447326090186834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.00915789045393467, -0.008366798982024193, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[0.0030951891094446182, -0.006064314860850573,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.002936174161732197, -0.006185177247971296,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...</td>\n",
       "      <td>[-0.0069602788425982, -0.003149641677737236, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n{{short description|Russ...</td>\n",
       "      <td>[-0.013576720841228962, -0.012421397492289543,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...</td>\n",
       "      <td>[-0.007685251533985138, -0.024144049733877182,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==International play==\\n...</td>\n",
       "      <td>[-0.013910852372646332, -0.01668008230626583, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...</td>\n",
       "      <td>[-0.006781470030546188, -0.007359683513641357,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Lviv bid for the 2022 Winter Olympics\\n\\n{{Oly...   \n",
       "1  Lviv bid for the 2022 Winter Olympics\\n\\n==His...   \n",
       "2  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "3  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "4  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "5  Lviv bid for the 2022 Winter Olympics\\n\\n==Ven...   \n",
       "6  Damir Sharipzyanov\\n\\n{{short description|Russ...   \n",
       "7  Damir Sharipzyanov\\n\\n==Playing career==\\n\\nOn...   \n",
       "8  Damir Sharipzyanov\\n\\n==International play==\\n...   \n",
       "9  Damir Sharipzyanov\\n\\n==Career statistics==\\n\\...   \n",
       "\n",
       "                                           embedding  \n",
       "0  [-0.005021067801862955, 0.00026050032465718687...  \n",
       "1  [0.0033927420154213905, -0.007447326090186834,...  \n",
       "2  [-0.00915789045393467, -0.008366798982024193, ...  \n",
       "3  [0.0030951891094446182, -0.006064314860850573,...  \n",
       "4  [-0.002936174161732197, -0.006185177247971296,...  \n",
       "5  [-0.0069602788425982, -0.003149641677737236, -...  \n",
       "6  [-0.013576720841228962, -0.012421397492289543,...  \n",
       "7  [-0.007685251533985138, -0.024144049733877182,...  \n",
       "8  [-0.013910852372646332, -0.01668008230626583, ...  \n",
       "9  [-0.006781470030546188, -0.007359683513641357,...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdf=df[0:10]\n",
    "subdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial  # for calculating vector similarities for search\n",
    "\n",
    "# search function\n",
    "def strings_ranked_by_relatedness(\n",
    "    query: str,\n",
    "    df: pd.DataFrame,\n",
    "    relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),\n",
    "    top_n: int = 100\n",
    ") -> tuple[list[str], list[float]]:\n",
    "    \"\"\"Returns a list of strings and relatednesses, sorted from most related to least.\"\"\"\n",
    "    query_embedding_response = client.embeddings.create(\n",
    "        model=EMBEDDING_MODEL,\n",
    "        input=query,\n",
    "    )\n",
    "    query_embedding = query_embedding_response.data[0].embedding\n",
    "    strings_and_relatednesses = [\n",
    "        (row[\"text\"], relatedness_fn(query_embedding, row[\"embedding\"]))\n",
    "        for i, row in df.iterrows()\n",
    "    ]\n",
    "    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)\n",
    "    strings, relatednesses = zip(*strings_and_relatednesses)\n",
    "    return strings[:top_n], relatednesses[:top_n]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# examples\n",
    "strings, relatednesses = strings_ranked_by_relatedness(\"Winter Olympics\", subdf, top_n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.861883230502706,\n",
       " 0.861703469804855,\n",
       " 0.853608864466378,\n",
       " 0.8518601723569217,\n",
       " 0.8467052278284372)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relatednesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relatedness=0.862\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Lviv bid for the 2022 Winter Olympics\\n\\n==Venues==\\n\\n===Mountain zone===\\n\\n====Venue cluster Tysovets-Panasivka====\\n\\nAn existing military ski training facility in [[Tysovets, Skole Raion|Tysovets]], 139&nbsp;km south of Lviv, along with two disused ski jumps, are proposed to be rebuilt to host all Nordic events.  Additionally, a ski hill would be developed to host all of the snowboard and freestyle skiing events.<ref name=concept_study />\\n*Tysovets Nordic Arena - biathlon, cross country, Nordic combined\\n*Tysovets Jumping Hills - ski jumping, Nordic combined\\n*Panasivka Snow Park - freestyle, snowboard'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relatedness=0.862\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Lviv bid for the 2022 Winter Olympics\\n\\n==Venues==\\n\\n{{Location map+\\n|Ukraine\\n|border =\\n|caption = Venue areas\\n|float = left\\n|width = 350\\n|places =\\n{{location map~ |Ukraine |lat=49.85 |long=24.01 |label='''[[Lviv]]''' |position=top}}\\n{{location map~ |Ukraine |lat=48.9828 |long=23.2774 |label='''[[Tysovets, Skole Raion|Tysovets]]''' |position=right}}\\n{{location map~ |Ukraine |lat=48.711111 |long=23.188333 |label='''[[Volovets|Borzhava]]''' |position=bottom}} }}\\nThe German companies Proprojekt and AS&P conducted a feasibility study for a Lviv bid in May 2012, which proposed to hold all events in two clusters.  The Lviv Ice Zone will host all ice sports, including bobsleigh and luge, as well as the opening and closing ceremonies.  The Tysovets Snow Zone and the Borzhava Alpine Area, both located in the Carpathian Mountains, would host the snow events.\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relatedness=0.854\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Lviv bid for the 2022 Winter Olympics\\n\\n==Venues==\\n\\n===Mountain zone===\\n\\n====Venue cluster Borzhava====\\n\\nA new alpine skiing centre, with a vertical drop of 900m, would be developed at Borzhava. It would include an alpine athlete's village and media accommodation.<ref name=concept_study />\\n*Borzhava Resort - Alpine skiing\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relatedness=0.852\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Lviv bid for the 2022 Winter Olympics\\n\\n==Venues==\\n\\n===City zone===\\n\\nThe main Olympic Park would be centered around the [[Arena Lviv]], hosting the opening and closing ceremonies.  The Olympic Park would have two ice rinks (ice hockey, short track speed skating and figure skating), a temporary speed skating oval and a temporary curling rink.  The Olympic Park would also host the Olympic Village and International Broadcast Centre.  A second ice rink for hockey competitions would be located just to the north-west of the Olympic Park.  A sliding track would also be built in the east of Lviv.<ref name=concept_study />\\n*[[Arena Lviv]] - opening and closing ceremonies\\n*Skating Arena - figure skating, short track\\n*Hockey Venue I - ice hockey\\n*Hockey Venue II - ice hockey\\n*Curling Sheet - curling\\n*Skating Oval - Speed skating\\n*Vynnycky Sliding Centre - Bobsleigh, luge, skeleton'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relatedness=0.847\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Lviv bid for the 2022 Winter Olympics\\n\\n==History==\\n\\n[[Image:Lwów - Rynek 01.JPG|thumb|right|200px|View of Rynok Square in Lviv]]\\n\\nOn 27 May 2010, [[President of Ukraine]] [[Viktor Yanukovych]] stated during a visit to [[Lviv]] that Ukraine \"will start working on the official nomination of our country as the holder of the Winter Olympic Games in [[Carpathian Mountains|Carpathians]]\".\\n\\nIn September 2012, [[government of Ukraine]] approved a document about the technical-economic substantiation of the national project \"Olympic Hope 2022\". This was announced by Vladyslav Kaskiv, the head of Ukraine´s Derzhinvestproekt (State investment project). The organizers announced on their website venue plans featuring Lviv as the host city and location for the \"ice sport\" venues, [[Volovets]] (around {{convert|185|km|mi|abbr=on}} from Lviv) as venue for the [[Alpine skiing]] competitions and [[Tysovets, Skole Raion|Tysovets]] (around {{convert|130|km|mi|abbr=on}} from Lviv) as venue for all other \"snow sport\" competitions. By March 2013 no other preparations than the feasibility study had been approved.\\n\\nOn 24 October 2013, session of the Lviv City Council adopted a resolution \"About submission to the International Olympic Committee for nomination of city to participate in the procedure for determining the host city of Olympic and Paralympic Winter Games in 2022\".\\n\\nOn 5 November 2013, it was confirmed that Lviv was bidding to host the [[2022 Winter Olympics]]. Lviv would host the ice sport events, while the skiing events would be held in the [[Carpathian]] mountains. This was the first bid Ukraine had ever submitted for an Olympic Games.\\n\\nOn 30 June 2014, the International Olympic Committee announced \"Lviv will turn its attention to an Olympic bid for 2026, and not continue with its application for 2022. The decision comes as a result of the present political and economic circumstances in Ukraine.\"\\n\\nUkraine\\'s Deputy Prime Minister Oleksandr Vilkul said that the Winter Games \"will be an impetus not just for promotion of sports and tourism in Ukraine, but a very important component in the economic development of Ukraine, the attraction of the investments, the creation of new jobs, opening Ukraine to the world, returning Ukrainians working abroad to their motherland.\"\\n\\nLviv was one of the host cities of [[UEFA Euro 2012]].'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for string, relatedness in zip(strings, relatednesses):\n",
    "    print(f\"{relatedness=:.3f}\")\n",
    "    display(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the search function above, we can now automatically retrieve relevant knowledge and insert it into messages to GPT.\n",
    "\n",
    "Below, we define a function ask that:\n",
    "\n",
    "Takes a user query\n",
    "Searches for text relevant to the query\n",
    "Stuffs that text into a message for GPT\n",
    "Sends the message to GPT\n",
    "Returns GPT's answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_tokens(text: str, model: str = GPT_MODEL) -> int:\n",
    "    \"\"\"Return the number of tokens in a string.\"\"\"\n",
    "    encoding = tiktoken.encoding_for_model(model)\n",
    "    return len(encoding.encode(text))\n",
    "\n",
    "\n",
    "def query_message(\n",
    "    query: str,\n",
    "    df: pd.DataFrame,\n",
    "    model: str,\n",
    "    token_budget: int\n",
    ") -> str:\n",
    "    \"\"\"Return a message for GPT, with relevant source texts pulled from a dataframe.\"\"\"\n",
    "    strings, relatednesses = strings_ranked_by_relatedness(query, df)\n",
    "    introduction = 'Use the below articles on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found in the articles, write \"I could not find an answer.\"'\n",
    "    question = f\"\\n\\nQuestion: {query}\"\n",
    "    message = introduction\n",
    "    for string in strings:\n",
    "        next_article = f'\\n\\nWikipedia article section:\\n\"\"\"\\n{string}\\n\"\"\"'\n",
    "        if (\n",
    "            num_tokens(message + next_article + question, model=model)\n",
    "            > token_budget\n",
    "        ):\n",
    "            break\n",
    "        else:\n",
    "            message += next_article\n",
    "    return message + question\n",
    "\n",
    "\n",
    "def ask(\n",
    "    query: str,\n",
    "    df: pd.DataFrame = df,\n",
    "    model: str = GPT_MODEL,\n",
    "    token_budget: int = 4096 - 500,\n",
    "    print_message: bool = False,\n",
    ") -> str:\n",
    "    \"\"\"Answers a query using GPT and a dataframe of relevant texts and embeddings.\"\"\"\n",
    "    message = query_message(query, df, model=model, token_budget=token_budget)\n",
    "    if print_message:\n",
    "        print(message)\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": \"You answer questions about the 2022 Winter Olympics.\"},\n",
    "        {\"role\": \"user\", \"content\": message},\n",
    "    ]\n",
    "    response = client.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=0\n",
    "    )\n",
    "    response_message = response.choices[0].message.content\n",
    "    return response_message\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>text</th>\n",
       "      <th>n_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>u cmpe faculty faculty emeriti ron mak.php.   ...</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>u cmpe faculty tenure line ahmet bindal.php.  ...</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>u cmpe research research areas.     Research A...</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>u cmpe academic cybersecurity program cybersec...</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  n_tokens\n",
       "0  u cmpe faculty faculty emeriti ron mak.php.   ...       160\n",
       "1  u cmpe faculty tenure line ahmet bindal.php.  ...       161\n",
       "2                                                  .         1\n",
       "3  u cmpe research research areas.     Research A...       154\n",
       "4  u cmpe academic cybersecurity program cybersec...       165"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "outputpath=\"./output/text/processed\"\n",
    "df = pd.read_csv(outputpath+'/split.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(outputpath+'/embeddings.csv')\n",
    "df.head()"
   ]
  }
 ],
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