redis-langchain-room-custom_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yk3jkeRp3Dd-",
    "outputId": "2b352d3a-1598-4727-b25e-714fc705d0f1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import redis\n",
    "import time\n",
    "\n",
    "from redis.commands.search.field import VectorField\n",
    "from redis.commands.search.field import TextField\n",
    "from redis.commands.search.field import TagField\n",
    "from redis.commands.search.query import Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "redis_conn = redis.Redis(\n",
    "  host='localhost',\n",
    "  port=6379,\n",
    "  password='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "00_n4VWH7FoB",
    "outputId": "f26daa8c-4af9-4def-d5ab-3197777fe2f9",
    "tags": []
   },
   "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>item_id</th>\n",
       "      <th>marketplace</th>\n",
       "      <th>country</th>\n",
       "      <th>main_image_id</th>\n",
       "      <th>domain_name</th>\n",
       "      <th>bullet_point</th>\n",
       "      <th>item_keywords</th>\n",
       "      <th>material</th>\n",
       "      <th>brand</th>\n",
       "      <th>color</th>\n",
       "      <th>item_name</th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_number</th>\n",
       "      <th>product_type</th>\n",
       "      <th>primary_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B07T6RZ2CM</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>IN</td>\n",
       "      <td>71dZhpsferL</td>\n",
       "      <td>amazon.in</td>\n",
       "      <td>3D Printed Hard Back Case Mobile Cover for Len...</td>\n",
       "      <td>mobile cover back cover mobile case phone case...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amazon Brand - Solimo</td>\n",
       "      <td>Others</td>\n",
       "      <td>Amazon Brand - Solimo Designer Couples Sitting...</td>\n",
       "      <td>Lenovo K4 Note</td>\n",
       "      <td>gz8115-SL40423</td>\n",
       "      <td>CELLULAR_PHONE_CASE</td>\n",
       "      <td>B07T6RZ2CM-amazon.in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B07T2JY31Y</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>IN</td>\n",
       "      <td>71vX7qIEAIL</td>\n",
       "      <td>amazon.in</td>\n",
       "      <td>3D Printed Hard Back Case Mobile Cover for Son...</td>\n",
       "      <td>mobile cover back cover mobile case phone case...</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Amazon Brand - Solimo</td>\n",
       "      <td>others</td>\n",
       "      <td>Amazon Brand - Solimo Designer Leaf on Wood 3D...</td>\n",
       "      <td>Sony Xperia Z1 L39H</td>\n",
       "      <td>gz8056-SL40528</td>\n",
       "      <td>CELLULAR_PHONE_CASE</td>\n",
       "      <td>B07T2JY31Y-amazon.in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0849YGSCZ</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>AE</td>\n",
       "      <td>A1EZF-2mB5L</td>\n",
       "      <td>amazon.ae</td>\n",
       "      <td></td>\n",
       "      <td>small de fur rooms navidad woven girls shag pa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stone &amp; Beam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stone &amp; Beam Contemporary Doily Wool Farmhouse...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I59I8044IVYGRYC00-Parent</td>\n",
       "      <td>HOME_FURNITURE_AND_DECOR</td>\n",
       "      <td>B0849YGSCZ-amazon.ae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B081K6TCML</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>IN</td>\n",
       "      <td>81o9EyZ-fAL</td>\n",
       "      <td>amazon.in</td>\n",
       "      <td>Solimo Plastic Multipurpose Modular Drawer; sm...</td>\n",
       "      <td>drawer modular drawer 3 rack modular drawer ki...</td>\n",
       "      <td>Plastic</td>\n",
       "      <td>Amazon Brand - Solimo</td>\n",
       "      <td>Multicolor</td>\n",
       "      <td>Amazon Brand - Solimo Plastic Multipurpose Mod...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sol_cujo_13</td>\n",
       "      <td>HOME</td>\n",
       "      <td>B081K6TCML-amazon.in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0854774X5</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>IN</td>\n",
       "      <td>81xaJCVnl3L</td>\n",
       "      <td>amazon.in</td>\n",
       "      <td>Snug fit for Nokia 8.1, with perfect cut-outs ...</td>\n",
       "      <td>Back Cover Designer Case Designer Take It Easy...</td>\n",
       "      <td>Silicon</td>\n",
       "      <td>Amazon Brand - Solimo</td>\n",
       "      <td>Multicolor</td>\n",
       "      <td>Amazon Brand - Solimo Designer Take It Easy UV...</td>\n",
       "      <td>Nokia 8.1</td>\n",
       "      <td>UV10714-SL40617</td>\n",
       "      <td>CELLULAR_PHONE_CASE</td>\n",
       "      <td>B0854774X5-amazon.in</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id marketplace country main_image_id domain_name  \\\n",
       "0  B07T6RZ2CM      Amazon      IN   71dZhpsferL   amazon.in   \n",
       "1  B07T2JY31Y      Amazon      IN   71vX7qIEAIL   amazon.in   \n",
       "2  B0849YGSCZ      Amazon      AE   A1EZF-2mB5L   amazon.ae   \n",
       "3  B081K6TCML      Amazon      IN   81o9EyZ-fAL   amazon.in   \n",
       "4  B0854774X5      Amazon      IN   81xaJCVnl3L   amazon.in   \n",
       "\n",
       "                                        bullet_point  \\\n",
       "0  3D Printed Hard Back Case Mobile Cover for Len...   \n",
       "1  3D Printed Hard Back Case Mobile Cover for Son...   \n",
       "2                                                      \n",
       "3  Solimo Plastic Multipurpose Modular Drawer; sm...   \n",
       "4  Snug fit for Nokia 8.1, with perfect cut-outs ...   \n",
       "\n",
       "                                       item_keywords material  \\\n",
       "0  mobile cover back cover mobile case phone case...      NaN   \n",
       "1  mobile cover back cover mobile case phone case...     Wood   \n",
       "2  small de fur rooms navidad woven girls shag pa...      NaN   \n",
       "3  drawer modular drawer 3 rack modular drawer ki...  Plastic   \n",
       "4  Back Cover Designer Case Designer Take It Easy...  Silicon   \n",
       "\n",
       "                   brand       color  \\\n",
       "0  Amazon Brand - Solimo      Others   \n",
       "1  Amazon Brand - Solimo      others   \n",
       "2           Stone & Beam         NaN   \n",
       "3  Amazon Brand - Solimo  Multicolor   \n",
       "4  Amazon Brand - Solimo  Multicolor   \n",
       "\n",
       "                                           item_name           model_name  \\\n",
       "0  Amazon Brand - Solimo Designer Couples Sitting...       Lenovo K4 Note   \n",
       "1  Amazon Brand - Solimo Designer Leaf on Wood 3D...  Sony Xperia Z1 L39H   \n",
       "2  Stone & Beam Contemporary Doily Wool Farmhouse...                  NaN   \n",
       "3  Amazon Brand - Solimo Plastic Multipurpose Mod...                  NaN   \n",
       "4  Amazon Brand - Solimo Designer Take It Easy UV...            Nokia 8.1   \n",
       "\n",
       "               model_number              product_type           primary_key  \n",
       "0            gz8115-SL40423       CELLULAR_PHONE_CASE  B07T6RZ2CM-amazon.in  \n",
       "1            gz8056-SL40528       CELLULAR_PHONE_CASE  B07T2JY31Y-amazon.in  \n",
       "2  I59I8044IVYGRYC00-Parent  HOME_FURNITURE_AND_DECOR  B0849YGSCZ-amazon.ae  \n",
       "3               sol_cujo_13                      HOME  B081K6TCML-amazon.in  \n",
       "4           UV10714-SL40617       CELLULAR_PHONE_CASE  B0854774X5-amazon.in  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAX_TEXT_LENGTH=512\n",
    "NUMBER_PRODUCTS=1000\n",
    "\n",
    "def auto_truncate(val):\n",
    "    return val[:MAX_TEXT_LENGTH]\n",
    "\n",
    "#Load Product data and truncate long text fields\n",
    "all_prods_df = pd.read_csv(\"product_data.csv\", converters={'bullet_point': auto_truncate,'item_keywords':auto_truncate,'item_name':auto_truncate})\n",
    "all_prods_df['primary_key'] = all_prods_df['item_id'] + '-' + all_prods_df['domain_name']\n",
    "all_prods_df['item_keywords'].replace('', np.nan, inplace=True)\n",
    "all_prods_df.dropna(subset=['item_keywords'], inplace=True)\n",
    "all_prods_df.reset_index(drop=True,inplace=True)\n",
    "\n",
    "#get the first 1000 products with non-empty item keywords\n",
    "product_metadata = all_prods_df.head(NUMBER_PRODUCTS).to_dict(orient='index')\n",
    "\n",
    "all_prods_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "from easynmt import EasyNMT\n",
    "\n",
    "translator = EasyNMT(\"opus-mt\")\n",
    "translator.translate(\"\", source_lang=\"es\", target_lang=\"en\")\n",
    "\n",
    "def get_description(link: str) -> str:\n",
    "    text = requests.get(link).text\n",
    "    parsed = BeautifulSoup(text)\n",
    "    return '\\n'.join(item.text for item in parsed.find_all('p')[:6])\n",
    "\n",
    "\n",
    "def translate_to_eng(text: str) -> str:\n",
    "    \n",
    "    return translator.translate(text, source_lang=\"es\", target_lang=\"en\")\n",
    "\n",
    "\n",
    "def get_and_translate(link:str) -> str:\n",
    "    return translate_to_eng(get_description(link))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "room_data = pd.read_excel('DATABASE OF ROOMS.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "room_data = room_data[\n",
    "    ['Code of Room', 'City', 'Tipología', 'ID', 'Area', 'Location',\n",
    "       'Nº HABIT', 'Link', 'Link Idealista', 'ESTADO', 'Días libres', 'Dias Completo', 'Nombre', 'Apellidos',\n",
    "       'Fecha de Nacimiento', 'PASAPORTE/DNI/NIE', 'Número de Identificación',\n",
    "       'Sexo', 'Ocupación', 'Nivel', 'Centro', 'Teléfono',\n",
    "       'País', 'Enlace Whatsapp',\n",
    "       'hab. Ocupadas presentes y futuras', 'NªHab. Ocupadas Reales',\n",
    "       'Renta Mensual', 'PVP OBJETIVO2', 'OBRA', 'Enlace desde Septiembre', 'Link de la carpeta con los contratos del Cliente',\n",
    "       'Columna3', 'TIPO3', 'Propietario',\n",
    "       'Nombre Legal Propietario', 'Cuenta Bancaria Propietario',\n",
    "       'Dirección Facturación', 'CIF/DNI/NIF/NIE', 'MAIL', 'TELEFONO',\n",
    "       'Whatsapp', 'Carpeta con Facturas de la vivienda', 'Tamaño Cama',\n",
    "       'Metros Cuadrados Habitación', 'jun.-22', 'jul.-22', 'ago.-22',\n",
    "       'sep.-22', 'oct.-22', 'nov.-22', 'dic.-22', 'ene.-23',\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "room_data = room_data.drop_duplicates(subset=['Area'], keep='first', ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Code of Room</th>\n",
       "      <th>City</th>\n",
       "      <th>Tipología</th>\n",
       "      <th>ID</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Nº HABIT</th>\n",
       "      <th>Link</th>\n",
       "      <th>Link Idealista</th>\n",
       "      <th>ESTADO</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamaño Cama</th>\n",
       "      <th>Metros Cuadrados Habitación</th>\n",
       "      <th>jun.-22</th>\n",
       "      <th>jul.-22</th>\n",
       "      <th>ago.-22</th>\n",
       "      <th>sep.-22</th>\n",
       "      <th>oct.-22</th>\n",
       "      <th>nov.-22</th>\n",
       "      <th>dic.-22</th>\n",
       "      <th>ene.-23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1HC</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>ç</td>\n",
       "      <td>Principe de Vergara 58, 2ºExt Izq</td>\n",
       "      <td>Piso Completo</td>\n",
       "      <td>https://tripath.es/principe-de-vergara-58/</td>\n",
       "      <td>https://www.idealista.com/inmueble/94802151/</td>\n",
       "      <td>LIBRE</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>9.24 m2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1H1</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>SALAMANCA</td>\n",
       "      <td>Principe de Vergara 58, 2ºExt Izq</td>\n",
       "      <td>1</td>\n",
       "      <td>https://tripath.es/principe-de-vergara-58/</td>\n",
       "      <td>https://www.idealista.com/inmueble/93962299/</td>\n",
       "      <td>LIBRE</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>9.24 m2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2HC</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>CHAMBERÍ</td>\n",
       "      <td>Vallehermoso 69, 3C</td>\n",
       "      <td>Piso Completo</td>\n",
       "      <td>https://tripath.es/vallehermoso-69/</td>\n",
       "      <td>https://www.idealista.com/inmueble/94802151/</td>\n",
       "      <td>OCUPADA</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>9 m2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3HC</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>CARABANCHEL</td>\n",
       "      <td>Manuel Galindo 2, 1ºA</td>\n",
       "      <td>Piso Completo</td>\n",
       "      <td>https://tripath.es/manuel-galindo-2/</td>\n",
       "      <td>https://www.idealista.com/inmueble/89909346/</td>\n",
       "      <td>OCUPADA</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>9 m2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4HC</td>\n",
       "      <td>Alicante</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>Avenida Aguilera 18, 3ºB</td>\n",
       "      <td>Piso Completo</td>\n",
       "      <td>https://tripath.es/avenida-aguilera-18-alicante/</td>\n",
       "      <td>https://www.idealista.com/inmueble/89909346/</td>\n",
       "      <td>OCUPADA</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5HC</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>GUINDALERA</td>\n",
       "      <td>Cartagena 10, 1ºA</td>\n",
       "      <td>Piso Completo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OCUPADA</td>\n",
       "      <td>...</td>\n",
       "      <td>135 cm</td>\n",
       "      <td>9 m2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code of Room      City Tipología  ID         Area  \\\n",
       "0          1HC    Madrid         A   1            ç   \n",
       "1          1H1    Madrid         A   1    SALAMANCA   \n",
       "2          2HC    Madrid         A   2     CHAMBERÍ   \n",
       "3          3HC    Madrid         C   3  CARABANCHEL   \n",
       "4          4HC  Alicante         A   4       CENTRO   \n",
       "5          5HC    Madrid         A   5   GUINDALERA   \n",
       "\n",
       "                            Location       Nº HABIT  \\\n",
       "0  Principe de Vergara 58, 2ºExt Izq  Piso Completo   \n",
       "1  Principe de Vergara 58, 2ºExt Izq              1   \n",
       "2                Vallehermoso 69, 3C  Piso Completo   \n",
       "3              Manuel Galindo 2, 1ºA  Piso Completo   \n",
       "4           Avenida Aguilera 18, 3ºB  Piso Completo   \n",
       "5                  Cartagena 10, 1ºA  Piso Completo   \n",
       "\n",
       "                                               Link  \\\n",
       "0        https://tripath.es/principe-de-vergara-58/   \n",
       "1        https://tripath.es/principe-de-vergara-58/   \n",
       "2               https://tripath.es/vallehermoso-69/   \n",
       "3              https://tripath.es/manuel-galindo-2/   \n",
       "4  https://tripath.es/avenida-aguilera-18-alicante/   \n",
       "5                                               NaN   \n",
       "\n",
       "                                 Link Idealista   ESTADO  ...  Tamaño Cama  \\\n",
       "0  https://www.idealista.com/inmueble/94802151/    LIBRE  ...       135 cm   \n",
       "1  https://www.idealista.com/inmueble/93962299/    LIBRE  ...       135 cm   \n",
       "2  https://www.idealista.com/inmueble/94802151/  OCUPADA  ...       135 cm   \n",
       "3  https://www.idealista.com/inmueble/89909346/  OCUPADA  ...       135 cm   \n",
       "4  https://www.idealista.com/inmueble/89909346/  OCUPADA  ...       135 cm   \n",
       "5                                           NaN  OCUPADA  ...       135 cm   \n",
       "\n",
       "   Metros Cuadrados Habitación jun.-22 jul.-22 ago.-22 sep.-22 oct.-22  \\\n",
       "0                      9.24 m2     NaN     NaN     NaN     NaN     NaN   \n",
       "1                      9.24 m2     NaN     NaN     NaN     NaN     NaN   \n",
       "2                         9 m2     NaN     NaN     NaN     NaN     NaN   \n",
       "3                         9 m2     NaN     NaN     NaN     NaN     NaN   \n",
       "4                          NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5                         9 m2     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "  nov.-22 dic.-22 ene.-23  \n",
       "0     NaN     NaN     NaN  \n",
       "1     NaN     NaN     NaN  \n",
       "2     NaN     NaN     NaN  \n",
       "3     NaN     NaN     NaN  \n",
       "4     NaN     NaN     NaN  \n",
       "5     NaN     NaN     NaN  \n",
       "\n",
       "[6 rows x 52 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "room_data = room_data[room_data.Link.notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_28504/2791034652.py:11: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 11 of the file /home/erik/hackatons/chatgpt/chatbot_POC/redis-langchain-ecommerce-chatbot_working_example-Copy1.ipynb. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
      "\n",
      "  parsed = BeautifulSoup(text)\n",
      "/home/erik/work/venvs/ml_experiment_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:1273: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 512 (`generation_config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_28504/2442681834.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  room_data['description'] = room_data['Link'].apply(get_and_translate)\n"
     ]
    }
   ],
   "source": [
    "room_data['description'] = room_data['Link'].apply(get_and_translate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#get the first 1000 products with non-empty item keywords\n",
    "rooms_metadata = room_data.head(NUMBER_PRODUCTS).to_dict(orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "gjouwTqF7ftf",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('sentence-transformers/all-distilroberta-v1')\n",
    "\n",
    "\n",
    "item_keywords =  [rooms_metadata[i]['description']  for i in rooms_metadata.keys()]\n",
    "item_keywords_vectors = [model.encode(sentence) for sentence in item_keywords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "id": "Iw7rlppY8f3a",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Code of Room': '2HC',\n",
       " 'City': 'Madrid',\n",
       " 'Tipología': 'A',\n",
       " 'ID': 2,\n",
       " 'Area': 'CHAMBERÍ',\n",
       " 'Location': 'Vallehermoso 69, 3C',\n",
       " 'Nº HABIT': 'Piso Completo',\n",
       " 'Link': 'https://tripath.es/vallehermoso-69/',\n",
       " 'Link Idealista': 'https://www.idealista.com/inmueble/94802151/',\n",
       " 'ESTADO': 'OCUPADA',\n",
       " 'Días libres': nan,\n",
       " 'Dias Completo': nan,\n",
       " 'Nombre': 'Nicolin Herault',\n",
       " 'Apellidos': nan,\n",
       " 'Fecha de Nacimiento': 22,\n",
       " 'PASAPORTE/DNI/NIE': nan,\n",
       " 'Número de Identificación': nan,\n",
       " 'Sexo': 'HOMBRE',\n",
       " 'Ocupación': 'ESTUDIO',\n",
       " 'Nivel': 'ERASMUS',\n",
       " 'Centro': 'UNIVERSIDAD POLITÉCNICA',\n",
       " 'Teléfono': 33789530981,\n",
       " 'País': 'FRANCIA',\n",
       " 'Enlace Whatsapp': 'https://api.whatsapp.com/send?phone=33789530981',\n",
       " 'hab. Ocupadas presentes y futuras': 1,\n",
       " 'NªHab. Ocupadas Reales': 1,\n",
       " 'Renta Mensual': 1,\n",
       " 'PVP OBJETIVO2': 525.0,\n",
       " 'OBRA': nan,\n",
       " 'Enlace desde Septiembre': 0,\n",
       " 'Link de la carpeta con los contratos del Cliente': nan,\n",
       " 'Columna3': 'OCUPADA',\n",
       " 'TIPO3': 'Madrid',\n",
       " 'Propietario': nan,\n",
       " 'Nombre Legal Propietario': 'EXCEM. CAPITAL PARTNERS SOC. INVERSION R',\n",
       " 'Cuenta Bancaria Propietario': nan,\n",
       " 'Dirección Facturación': 'Paseo de la Castellana 93, 9ºE',\n",
       " 'CIF/DNI/NIF/NIE': 'A-87417846',\n",
       " 'MAIL': 'rrm@excemre.com',\n",
       " 'TELEFONO': 34689898359,\n",
       " 'Whatsapp': 'https://wa.me/34689898359?text=',\n",
       " 'Carpeta con Facturas de la vivienda': nan,\n",
       " 'Tamaño Cama': '135 cm',\n",
       " 'Metros Cuadrados Habitación': '9 m2',\n",
       " 'jun.-22': nan,\n",
       " 'jul.-22': nan,\n",
       " 'ago.-22': nan,\n",
       " 'sep.-22': nan,\n",
       " 'oct.-22': nan,\n",
       " 'nov.-22': nan,\n",
       " 'dic.-22': nan,\n",
       " 'ene.-23': nan,\n",
       " 'description': 'Vallehermoso 69\\nWe can give tired looking apartments a new look. We also built and installed partitioning or plastering with a maximum work of 2 days. Many international and national people are familiar with them by the ‘open’ office spaces that have been divided. We take detailed pictures of each room ‘before’ and ‘after’. In this way, we can return the apartment to its original condition at the end of the rental agreement. All changes are paid by Tripath, which often increases the value of your apartment as owner in Madrid. \\nBelow are several photographs and a video of the current state of the house, and then continue with the previous state once we acquire it:\\n\\nTripath was founded in Madrid in 2020. The idea was born by experiencing how complex and frustrating it can be to find a home, Bureaucracy, a very limited offer, high settlement costs and difficult roommates. With personalized visits, a community of like-minded people and a comprehensive service, Tripath aims to create a top-notch housing experience. We start with a single flat in Madrid and now have about 20 rooms throughout the city, always in central places, at affordable prices and continuously attended by our dedicated property managers.\\nWe plan to offer a great deal of accommodation experience to an increasing number of people.',\n",
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      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(item_keywords_vectors)\n",
    "len(rooms_metadata)\n",
    "# Check one of the products\n",
    "rooms_metadata[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "id": "ayGF0v-w8mxy",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def load_vectors(client, product_metadata, vector_dict, vector_field_name):\n",
    "    p = client.pipeline(transaction=False)\n",
    "    for index in product_metadata.keys():    \n",
    "        #hash key\n",
    "        key='product:'+ str(index)+ ':' + product_metadata[index]['Location']\n",
    "        \n",
    "        #hash values\n",
    "        item_metadata = product_metadata[index]\n",
    "        item_keywords_vector = vector_dict[index].astype(np.float32).tobytes()\n",
    "        item_metadata[vector_field_name]=item_keywords_vector\n",
    "        \n",
    "        # HSET\n",
    "        p.hset(key,mapping=item_metadata)\n",
    "            \n",
    "    p.execute()\n",
    "\n",
    "def create_flat_index (redis_conn,vector_field_name,number_of_vectors, vector_dimensions=512, distance_metric='L2'):\n",
    "    redis_conn.ft().create_index([\n",
    "        VectorField(vector_field_name, \"FLAT\", {\"TYPE\": \"FLOAT32\", \"DIM\": vector_dimensions, \"DISTANCE_METRIC\": distance_metric, \"INITIAL_CAP\": number_of_vectors, \"BLOCK_SIZE\":number_of_vectors }),\n",
    "        TagField(\"Area\"),\n",
    "        TextField(\"item_Tipología\"),\n",
    "        TextField(\"item_description\")\n",
    "    ]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "id": "lcjo2rXb8pT9",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and Indexing + 1000 products\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ITEM_KEYWORD_EMBEDDING_FIELD='item_keyword_vector'\n",
    "TEXT_EMBEDDING_DIMENSION=768\n",
    "NUMBER_PRODUCTS=1000\n",
    "\n",
    "print ('Loading and Indexing + ' +  str(NUMBER_PRODUCTS) + ' products')\n",
    "\n",
    "#flush all data\n",
    "redis_conn.flushall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "#create flat index & load vectors\n",
    "create_flat_index(\n",
    "    redis_conn, ITEM_KEYWORD_EMBEDDING_FIELD,NUMBER_PRODUCTS,TEXT_EMBEDDING_DIMENSION,'COSINE'\n",
    ")\n",
    "load_vectors(redis_conn,rooms_metadata,item_keywords_vectors,ITEM_KEYWORD_EMBEDDING_FIELD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erik/work/venvs/ml_experiment_3.10/lib/python3.10/site-packages/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
      "  warnings.warn(\n",
      "/home/erik/work/venvs/ml_experiment_3.10/lib/python3.10/site-packages/langchain/llms/openai.py:608: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "llm = OpenAI(model_name=\"gpt-3.5-turbo\", temperature=0.3, openai_api_key=\"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\")\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"product_description\"],\n",
    "    template=\"Create comma seperated product keywords to perform a query on a amazon dataset for this user input: {product_description}\",\n",
    ")\n",
    "\n",
    "from langchain.chains import LLMChain\n",
    "chain = LLMChain(llm=llm, prompt=prompt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m userinput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHey im a Roo(m)bot, how can i help you today? \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser:\u001b[39m\u001b[38;5;124m\"\u001b[39m, userinput)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Run the chain only specifying the input variable.\u001b[39;00m\n",
      "File \u001b[0;32m~/work/venvs/ml_experiment_3.10/lib/python3.10/site-packages/ipykernel/kernelbase.py:1182\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1180\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1181\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1183\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1184\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1185\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1186\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1187\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/venvs/ml_experiment_3.10/lib/python3.10/site-packages/ipykernel/kernelbase.py:1225\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1222\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1223\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1224\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1225\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "userinput = input(\"Hey im a Roo(m)bot, how can i help you today? \")\n",
    "print(\"User:\", userinput)\n",
    "\n",
    "# Run the chain only specifying the input variable.\n",
    "keywords = chain.run(userinput)\n",
    "\n",
    "topK=3\n",
    "#vectorize the query\n",
    "query_vector = model.encode(keywords).astype(np.float32).tobytes()\n",
    "\n",
    "#prepare the query\n",
    "q = Query(\n",
    "    f'*=>[KNN {topK} @{ITEM_KEYWORD_EMBEDDING_FIELD} $vec_param AS vector_score]'\n",
    ").sort_by('vector_score').paging(0,topK).return_fields(\n",
    "        'vector_score','item_name','item_id','item_keywords'\n",
    "    ).dialect(2)\n",
    "params_dict = {\"vec_param\": query_vector}\n",
    "\n",
    "#Execute the query\n",
    "results = redis_conn.ft().search(q, query_params = params_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Document' object has no attribute 'item_name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[112], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m full_result_string \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m product \u001b[38;5;129;01min\u001b[39;00m results\u001b[38;5;241m.\u001b[39mdocs:\n\u001b[0;32m----> 3\u001b[0m     full_result_string \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mproduct\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem_name\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m product\u001b[38;5;241m.\u001b[39mitem_keywords \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m product\u001b[38;5;241m.\u001b[39mitem_id \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmemory\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConversationBufferMemory\n\u001b[1;32m      7\u001b[0m template \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mYou are a chatbot. Be kind, detailed and nice. Present the given queried search result in a nice way as answer to the user input. dont ask questions back! just take the given context\u001b[39m\n\u001b[1;32m      8\u001b[0m \n\u001b[1;32m      9\u001b[0m \u001b[38;5;132;01m{chat_history}\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;124mHuman: \u001b[39m\u001b[38;5;132;01m{user_msg}\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124mChatbot:\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Document' object has no attribute 'item_name'"
     ]
    }
   ],
   "source": [
    "full_result_string = ''\n",
    "for product in results.docs:\n",
    "    full_result_string += product.item_name + ' ' + product.item_keywords + ' ' + product.item_id + \"\\n\\n\\n\"\n",
    "\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "template = \"\"\"You are a chatbot. Be kind, detailed and nice. Present the given queried search result in a nice way as answer to the user input. dont ask questions back! just take the given context\n",
    "\n",
    "{chat_history}\n",
    "Human: {user_msg}\n",
    "Chatbot:\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"chat_history\", \"user_msg\"], \n",
    "    template=template\n",
    ")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
    "llm_chain = LLMChain(\n",
    "    llm=OpenAI(model_name=\"gpt-3.5-turbo\", temperature=0.8, openai_api_key=\"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\"), \n",
    "    prompt=prompt, \n",
    "    verbose=False, \n",
    "    memory=memory,\n",
    ")\n",
    "\n",
    "\n",
    "answer = llm_chain.predict(user_msg= f\"{full_result_string} ---\\n\\n {userinput}\")\n",
    "print(\"Bot:\", answer)\n",
    "time.sleep(0.5)\n",
    "\n",
    "while True:\n",
    "    follow_up = input(\"Anything else you want to ask about this topic?\")\n",
    "    print(\"User:\", follow_up)\n",
    "    answer = llm_chain.predict(\n",
    "        user_msg=follow_up\n",
    "    )\n",
    "    print(\"Bot:\", answer)\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document {'id': 'product:1:Vallehermoso 69, 3C', 'payload': None, 'vector_score': '0.644711613655'}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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