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   "source": [
    "## Semantic text search using embeddings\n",
    "\n",
    "We can search through all our reviews semantically in a very efficient manner and at very low cost, by simply embedding our search query, and then finding the most similar reviews. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv('output/embedded_1k_reviews.csv')\n",
    "df['babbage_search'] = df.babbage_search.apply(eval).apply(np.array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember to use the documents embedding engine for documents (in this case reviews), and query embedding engine for queries. Note that here we just compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jamaican Blue beans:  Excellent coffee bean for roasting. Our family just purchased another 5 pounds for more roasting. Plenty of flavor and mild on acidity when roasted to a dark brown bean and befor\n",
      "\n",
      "Good Buy:  I liked the beans. They were vacuum sealed, plump and moist. Would recommend them for any use. I personally split and stuck them in some vodka to make vanilla extract. Yum!\n",
      "\n",
      "Fantastic Instant Refried beans:  Fantastic Instant Refried Beans have been a staple for my family now for nearly 20 years.  All 7 of us love it and my grown kids are passing on the tradition.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from openai.embeddings_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",
    "    embedding = get_embedding(product_description, engine='text-search-babbage-query-001')\n",
    "    df['similarities'] = df.babbage_search.apply(lambda x: cosine_similarity(x, embedding))\n",
    "\n",
    "    res = df.sort_values('similarities', ascending=False).head(n).combined.str.replace('Title: ','').str.replace('; Content:', ': ')\n",
    "    if pprint:\n",
    "        for r in res:\n",
    "            print(r[:200])\n",
    "            print()\n",
    "    return res\n",
    "res = search_reviews(df, 'delicious beans', n=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rustichella ROCKS!:  Anything this company makes is worthwhile eating! My favorite is their Trenne.<br />Their whole wheat pasta is the best I have ever had.\n",
      "\n",
      "sooo good:  tastes so good. Worth the money. My boyfriend hates wheat pasta and LOVES this. cooks fast tastes great.I love this brand and started buying more of their pastas. Bulk is best.\n",
      "\n",
      "Wonderful:  Came quickly. Was plentiful and delicious and cheaper than in the store. You will enjoy it if you like thick pasta.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df, 'whole wheat pasta', n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can search through these reviews easily. To speed up computation, we can use a special algorithm, aimed at faster search through embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "great product, poor delivery:  The coffee is excellent and I am a repeat buyer.  Problem this time was with the UPS delivery.  They left the box in front of my garage door in the middle of the drivewa\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df, 'bad delivery', n=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, this can immediately deliver a lot of value. In this example we show being able to quickly find the examples of delivery failures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extremely dissapointed:  Hi,<br />I am very disappointed with the past shipment I received of the ONE coconut water. 3 of the boxes were leaking and the coconut water was spoiled.<br /><br />Thanks.<b\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df, 'spoilt', n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good food:  The only dry food my queen cat will eat. Helps prevent hair balls. Good packaging. Arrives promptly. Recommended by a friend who sells pet food.\n",
      "\n",
      "A great deal on Greenies:  Paid only $22 with free shipping for 96 teenies compared to about $35 at the pet store. How can you go wrong with a deal like that?  The dog begs for his daily Greenie. Got \n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df, 'pet food', n=2)"
   ]
  }
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