redis-langchain-ecommerce-chatbot_working_example.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",
    "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\n",
    "import redis\n",
    "import time\n",
    "\n",
    "redis_conn = redis.Redis(\n",
    "  host='localhost',\n",
    "  port=6379,\n",
    "  password='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "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": 3,
   "metadata": {
    "id": "gjouwTqF7ftf",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-25 09:50:50.336372: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-25 09:50:50.416267: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-03-25 09:50:50.781100: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-03-25 09:50:50.781139: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-03-25 09:50:50.781143: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('sentence-transformers/all-distilroberta-v1')\n",
    "\n",
    "\n",
    "item_keywords =  [product_metadata[i]['item_keywords']  for i in product_metadata.keys()]\n",
    "item_keywords_vectors = [ model.encode(sentence) for sentence in item_keywords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Iw7rlppY8f3a",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'item_id': 'B07T6RZ2CM',\n",
       " 'marketplace': 'Amazon',\n",
       " 'country': 'IN',\n",
       " 'main_image_id': '71dZhpsferL',\n",
       " 'domain_name': 'amazon.in',\n",
       " 'bullet_point': '3D Printed Hard Back Case Mobile Cover for Lenovo K4 Note Easy to put & take off with perfect cutouts for volume buttons, audio & charging ports. Stylish design and appearance, express your unique personality. Extreme precision design allows easy access to all buttons and ports while featuring raised bezel to life screen and camera off flat surface. Slim Hard Back Cover No Warranty None',\n",
       " 'item_keywords': 'mobile cover back cover mobile case phone case mobile panel phone panel Lenovo mobile case Lenovo phone cover Lenovo back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Lenovo mobile case Lenovo phone cover Lenovo back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Lenovo mobile case Lenovo phone cover Lenovo back case hard case 3D printed mobile cover mobile cover back cover mobil',\n",
       " 'material': nan,\n",
       " 'brand': 'Amazon Brand - Solimo',\n",
       " 'color': 'Others',\n",
       " 'item_name': 'Amazon Brand - Solimo Designer Couples Sitting at Dark 3D Printed Hard Back Case Mobile Cover for Lenovo K4 Note',\n",
       " 'model_name': 'Lenovo K4 Note',\n",
       " 'model_number': 'gz8115-SL40423',\n",
       " 'product_type': 'CELLULAR_PHONE_CASE',\n",
       " 'primary_key': 'B07T6RZ2CM-amazon.in'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(item_keywords_vectors)\n",
    "len(product_metadata)\n",
    "# Check one of the products\n",
    "product_metadata[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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]['primary_key']\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(\"product_type\"),\n",
    "        TextField(\"item_name\"),\n",
    "        TextField(\"item_keywords\"),\n",
    "        TagField(\"country\")        \n",
    "    ]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "lcjo2rXb8pT9",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and Indexing + 1000 products\n"
     ]
    }
   ],
   "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()\n",
    "\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,product_metadata,item_keywords_vectors,ITEM_KEYWORD_EMBEDDING_FIELD)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hey im a E-commerce Chatbot, how can i help you today?  I need new phone with good camera\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: I need new phone with good camera\n"
     ]
    }
   ],
   "source": [
    "userinput = input(\"Hey im a E-commerce Chatbot, 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": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bot: Based on your query, here are some search results that might interest you:\n",
      "\n",
      "- Google Pixel 4a - This phone is known for its great camera quality and affordability.\n",
      "- iPhone 12 Pro Max - If you're willing to splurge, this phone has one of the best cameras on the market.\n",
      "- Samsung Galaxy S21 Ultra - This phone also has a fantastic camera and is a great option if you prefer Android.\n",
      "- OnePlus 9 Pro - This phone has a Hasselblad-branded camera system and is known for its photo and video capabilities.\n",
      "\n",
      "I hope this helps you find the perfect phone for your needs!\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Anything else you want to ask about this topic? Which is the cheapest?\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: Which is the cheapest?\n",
      "Bot: The Google Pixel 4a is the most budget-friendly option on the list without compromising on camera quality.\n"
     ]
    },
    {
     "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[17], line 31\u001b[0m\n\u001b[1;32m     28\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m0.5\u001b[39m)\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m---> 31\u001b[0m     follow_up \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;43mAnything else you want to ask about this topic?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\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, follow_up)\n\u001b[1;32m     33\u001b[0m     answer \u001b[38;5;241m=\u001b[39m llm_chain\u001b[38;5;241m.\u001b[39mpredict(\n\u001b[1;32m     34\u001b[0m         user_msg\u001b[38;5;241m=\u001b[39mfollow_up\n\u001b[1;32m     35\u001b[0m     )\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": [
    "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": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document {'id': 'product:309:B0856GNCRL-amazon.in', 'payload': None, 'vector_score': '0.649512529373', 'item_name': 'Amazon Brand - Solimo Designer Multicolor Love Butterfly Blue Pattern Design Printed Soft Back Case Mobile Cover for Motorola Moto E6s / Motorola Moto E6 Plus', 'item_id': 'B0856GNCRL', 'item_keywords': 'cellphonecover backcase mobileguard mobilecover cellphonecase phonecover case protectivecase cover backcover phoneguard mobilecase protectivecover phonecase moto e6 plus back cover moto e6 plus cover moto e6 plus designer case moto e6 plus mobile cover moto e6 plus printed covers moto e6 plus silicon back case moto e6 plus tpu back cover moto e6s 360 cover moto e6s back cover moto e6s cover moto e6s designer case moto e6s designer covers moto e6s flexible back cover moto e6s mobile accessories moto e6s mobi'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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