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   "cell_type": "markdown",
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   "source": [
    "## 1. Load the dataset\n",
    "\n",
    "The dataset used in this example is [fine-food reviews](https://www.kaggle.com/snap/amazon-fine-food-reviews) from Amazon. The dataset contains a total of 568,454 food reviews Amazon users left up to October 2012. We will use a subset of this dataset, consisting of 1,000 most recent reviews for illustration purposes. The reviews are in English and tend to be positive or negative. Each review has a ProductId, UserId, Score, review title (Summary) and review body (Text).\n",
    "\n",
    "We will combine the review summary and review text into a single combined text. The model will encode this combined text and it will output a single vector embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>Time</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>1351123200</td>\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time   ProductId          UserId  Score  \\\n",
       "0  1351123200  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "1  1351123200  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  where does one  start...and stop... with a tre...   \n",
       "1                                  Arrived in pieces   \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",
       "\n",
       "                                            combined  \n",
       "0  Title: where does one  start...and stop... wit...  \n",
       "1  Title: Arrived in pieces; Content: Not pleased...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "input_datapath = 'data/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 = df[['Time', 'ProductId', 'UserId', 'Score', 'Summary', 'Text']]\n",
    "df = df.dropna()\n",
    "df['combined'] = \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subsample to 1k most recent reviews and remove samples that are too long\n",
    "df = df.sort_values('Time').tail(1_100)\n",
    "df.drop('Time', axis=1, inplace=True)\n",
    "\n",
    "from transformers import GPT2TokenizerFast\n",
    "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n",
    "\n",
    "# remove reviews that are too long\n",
    "df['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\n",
    "df = df[df.n_tokens<2000].tail(1_000)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Get embeddings and save them for future reuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai.embeddings_utils import get_embedding\n",
    "\n",
    "# This will take just under 10 minutes\n",
    "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-similarity-babbage-001'))\n",
    "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-search-babbage-doc-001'))\n",
    "df.to_csv('data/fine_food_reviews_with_embeddings_1k.csv')"
   ]
  }
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