openai-cookbook/examples/Obtain_dataset.ipynb

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    "## 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."
   ]
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       "<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",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1303862400</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>5</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>Title: Good Quality Dog Food; Content: I have ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1346976000</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>1</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>Title: Not as Advertised; Content: Product arr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          Time   ProductId          UserId  Score                Summary  \\\n",
       "Id                                                                         \n",
       "1   1303862400  B001E4KFG0  A3SGXH7AUHU8GW      5  Good Quality Dog Food   \n",
       "2   1346976000  B00813GRG4  A1D87F6ZCVE5NK      1      Not as Advertised   \n",
       "\n",
       "                                                 Text  \\\n",
       "Id                                                      \n",
       "1   I have bought several of the Vitality canned d...   \n",
       "2   Product arrived labeled as Jumbo Salted Peanut...   \n",
       "\n",
       "                                             combined  \n",
       "Id                                                     \n",
       "1   Title: Good Quality Dog Food; Content: I have ...  \n",
       "2   Title: Not as Advertised; Content: Product arr...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('input/Reviews.csv', 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,
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    {
     "data": {
      "text/plain": [
       "1000"
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     "execution_count": 2,
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   "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('output/embedded_1k_reviews.csv')"
   ]
  }
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