self-querying.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2c001c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Necessary import \n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import datetime as dt\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain.document_loaders.csv_loader import CSVLoader\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,  ChatPromptTemplate\n",
    "from langchain.retrievers import ContextualCompressionRetriever\n",
    "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "from langchain.vectorstores import FAISS, Chroma\n",
    "import csv\n",
    "from typing import Dict, List, Optional\n",
    "from langchain.document_loaders.base import BaseLoader\n",
    "from langchain.docstore.document import Document"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c406869",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9587980d",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Define 4-th order runge-kutta \n",
    "def rk_4(y_0,t,f,args=()):\n",
    "  n = len(t)\n",
    "  y = np.zeros((n, len(y0)))\n",
    "  y[0] = y0\n",
    "  for i in range(n - 1):\n",
    "      h = t[i+1] - t[i]\n",
    "      k1 = f(y[i], t[i], *args)\n",
    "      k2 = f(y[i] + k1 * h / 2., t[i] + h / 2., *args)\n",
    "      k3 = f(y[i] + k2 * h / 2., t[i] + h / 2., *args)\n",
    "      k4 = f(y[i] + k3 * h, t[i] + h, *args)\n",
    "      y[i+1] = y[i] + (h / 6.) * (k1 + 2*k2 + 2*k3 + k4)\n",
    "  return y\n",
    "\n",
    "# Define parameters of SIR model  \n",
    "beta=0.7 # Infection rate \n",
    "gamma=0.1  # Removal reate \n",
    "T_max= 90 # total days in consideration \n",
    "dt=1\n",
    "T_num=int(T_max/dt)\n",
    "t= np.linspace(start=0,stop=T_max,num=int(T_num))\n",
    "\n",
    "\n",
    "# Define f_SIR\n",
    "def f_SIR(y,t,N,beta, gamma):\n",
    "  f_SIR=np.zeros(3)\n",
    "  f_SIR[0]= - beta/N*y[0]*y[1]\n",
    "  f_SIR[1]= beta/N*y[0]*y[1]-gamma*y[1]\n",
    "  f_SIR[2]= gamma*y[1]\n",
    "  return f_SIR \n",
    "\n",
    "np.random.seed(0)\n",
    "y=[]\n",
    "y0_i=np.random.randint(low=50, high=2000, size=10, dtype=int)\n",
    "N=np.random.randint(low=5000, high=2e4, size=10, dtype=int)\n",
    "for i in range(10):\n",
    "    y0=np.array([N[i]-y0_i[i],N[i]-y0_i[i], 0]) \n",
    "    # Compute the value of SIR \n",
    "    y.append(rk_4(y0, t, f_SIR,args=(N[i], beta, gamma)).astype(int))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0518960",
   "metadata": {},
   "outputs": [],
   "source": [
    "sir_table_list=[]\n",
    "for i in range(10): \n",
    "    sir_table=pd.DataFrame(\n",
    "        { \n",
    "    \"time\":pd.date_range(start='1/1/2018', periods=90),\"susceptible\":y[i][:,0],\"infectious\":y[i][:,1] ,\"removed\":y[i][:,2],\n",
    "\"city\": f\"city{i}\"        })\n",
    "    sir_table_list.append(sir_table)\n",
    "sir_table=pd.concat(sir_table_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a3a6873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>time</th>\n",
       "      <th>susceptible</th>\n",
       "      <th>infectious</th>\n",
       "      <th>removed</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>8639</td>\n",
       "      <td>8639</td>\n",
       "      <td>0</td>\n",
       "      <td>city0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>3857</td>\n",
       "      <td>12338</td>\n",
       "      <td>1081</td>\n",
       "      <td>city0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>1458</td>\n",
       "      <td>13414</td>\n",
       "      <td>2405</td>\n",
       "      <td>city0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>545</td>\n",
       "      <td>12983</td>\n",
       "      <td>3749</td>\n",
       "      <td>city0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>214</td>\n",
       "      <td>12046</td>\n",
       "      <td>5017</td>\n",
       "      <td>city0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  susceptible  infectious  removed   city\n",
       "0 2018-01-01         8639        8639        0  city0\n",
       "1 2018-01-02         3857       12338     1081  city0\n",
       "2 2018-01-03         1458       13414     2405  city0\n",
       "3 2018-01-04          545       12983     3749  city0\n",
       "4 2018-01-05          214       12046     5017  city0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sir_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cc9407f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,5,figsize=(20, 10))\n",
    "for i in range(10): \n",
    "    for j, group in enumerate([\"S\",\"I\",\"R\"]): \n",
    "        if i>4:\n",
    "            k =1\n",
    "            l= i-5\n",
    "        else: \n",
    "            k=0\n",
    "            l= i \n",
    "        axs[k,l].plot(t, y[i][:,j],label=group)\n",
    "        axs[k,l].set_title(f\"city{i}\")\n",
    "        axs[k,l].legend()\n",
    "#plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c6280c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sir_table.to_csv(\"sir.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962c993c",
   "metadata": {},
   "source": [
    "# Embedding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42c9951c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup openai api key \n",
    "os.environ['OPENAI_API_KEY'] = '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'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1db8ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and set embeddings \n",
    "loader = CSVLoader(file_path='sir.csv')\n",
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0db2f98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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2018-02-11\\nsusceptible: 0\\ninfectious: 458\\nremoved: 26601\\ncity: city9', metadata={'source': 'sir.csv', 'row': 851}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 414\\nremoved: 26645\\ncity: city9', metadata={'source': 'sir.csv', 'row': 852}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 374\\nremoved: 26685\\ncity: city9', metadata={'source': 'sir.csv', 'row': 853}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 338\\nremoved: 26721\\ncity: city9', metadata={'source': 'sir.csv', 'row': 854}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 305\\nremoved: 26754\\ncity: city9', metadata={'source': 'sir.csv', 'row': 855}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 276\\nremoved: 26783\\ncity: city9', metadata={'source': 'sir.csv', 'row': 856}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 249\\nremoved: 26810\\ncity: city9', metadata={'source': 'sir.csv', 'row': 857}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 225\\nremoved: 26834\\ncity: city9', metadata={'source': 'sir.csv', 'row': 858}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 204\\nremoved: 26855\\ncity: city9', metadata={'source': 'sir.csv', 'row': 859}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 184\\nremoved: 26875\\ncity: city9', metadata={'source': 'sir.csv', 'row': 860}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 166\\nremoved: 26893\\ncity: city9', metadata={'source': 'sir.csv', 'row': 861}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 150\\nremoved: 26909\\ncity: city9', metadata={'source': 'sir.csv', 'row': 862}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 136\\nremoved: 26923\\ncity: city9', metadata={'source': 'sir.csv', 'row': 863}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 123\\nremoved: 26936\\ncity: city9', metadata={'source': 'sir.csv', 'row': 864}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 111\\nremoved: 26948\\ncity: city9', metadata={'source': 'sir.csv', 'row': 865}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 100\\nremoved: 26959\\ncity: city9', metadata={'source': 'sir.csv', 'row': 866}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 90\\nremoved: 26969\\ncity: city9', metadata={'source': 'sir.csv', 'row': 867}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 82\\nremoved: 26977\\ncity: city9', metadata={'source': 'sir.csv', 'row': 868}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 74\\nremoved: 26985\\ncity: city9', metadata={'source': 'sir.csv', 'row': 869}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 67\\nremoved: 26992\\ncity: city9', metadata={'source': 'sir.csv', 'row': 870}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 60\\nremoved: 26999\\ncity: city9', metadata={'source': 'sir.csv', 'row': 871}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 54\\nremoved: 27005\\ncity: city9', metadata={'source': 'sir.csv', 'row': 872}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 49\\nremoved: 27010\\ncity: city9', metadata={'source': 'sir.csv', 'row': 873}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 44\\nremoved: 27015\\ncity: city9', metadata={'source': 'sir.csv', 'row': 874}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 40\\nremoved: 27019\\ncity: city9', metadata={'source': 'sir.csv', 'row': 875}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 36\\nremoved: 27023\\ncity: city9', metadata={'source': 'sir.csv', 'row': 876}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 33\\nremoved: 27026\\ncity: city9', metadata={'source': 'sir.csv', 'row': 877}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 29\\nremoved: 27030\\ncity: city9', metadata={'source': 'sir.csv', 'row': 878}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 27\\nremoved: 27032\\ncity: city9', metadata={'source': 'sir.csv', 'row': 879}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 24\\nremoved: 27035\\ncity: city9', metadata={'source': 'sir.csv', 'row': 880}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 22\\nremoved: 27037\\ncity: city9', metadata={'source': 'sir.csv', 'row': 881}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 19\\nremoved: 27040\\ncity: city9', metadata={'source': 'sir.csv', 'row': 882}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 18\\nremoved: 27041\\ncity: city9', metadata={'source': 'sir.csv', 'row': 883}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 16\\nremoved: 27043\\ncity: city9', metadata={'source': 'sir.csv', 'row': 884}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 14\\nremoved: 27045\\ncity: city9', metadata={'source': 'sir.csv', 'row': 885}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 13\\nremoved: 27046\\ncity: city9', metadata={'source': 'sir.csv', 'row': 886}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 12\\nremoved: 27047\\ncity: city9', metadata={'source': 'sir.csv', 'row': 887}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 10\\nremoved: 27049\\ncity: city9', metadata={'source': 'sir.csv', 'row': 888}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 9\\nremoved: 27050\\ncity: city9', metadata={'source': 'sir.csv', 'row': 889}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 8\\nremoved: 27051\\ncity: city9', metadata={'source': 'sir.csv', 'row': 890}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 8\\nremoved: 27051\\ncity: city9', metadata={'source': 'sir.csv', 'row': 891}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 7\\nremoved: 27052\\ncity: city9', metadata={'source': 'sir.csv', 'row': 892}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 6\\nremoved: 27053\\ncity: city9', metadata={'source': 'sir.csv', 'row': 893}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 5\\nremoved: 27054\\ncity: city9', metadata={'source': 'sir.csv', 'row': 894}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 5\\nremoved: 27054\\ncity: city9', metadata={'source': 'sir.csv', 'row': 895}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 4\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 896}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 4\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 897}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 3\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 898}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 3\\nremoved: 27056\\ncity: city9', metadata={'source': 'sir.csv', 'row': 899})]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a427748c",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()\n",
    "vectorstore = Chroma.from_documents(data, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14a3115a",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever=vectorstore.as_retriever(search_kwargs={\"k\": 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45e30c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm=ChatOpenAI(model_name=\"gpt-4\",temperature=0)\n",
    "\n",
    "# Define the system message template\n",
    "system_template = \"\"\"The provided {context} is a tabular dataset containing Suspectible, infectious and removed population during 90 days in 10 cities.\n",
    "The dataset includes the following columns:\n",
    "'time': time the population was meseaured,\n",
    "'city': city in which the popoluation was measured,\n",
    "\"susceptible\": the susceptible population of the disease, \n",
    "\"infectious\": the infectious population of the disease, \n",
    "\"removed\": the removed popolation of the disease. \n",
    "----------------\n",
    "{context}\"\"\"\n",
    "\n",
    "# Create the chat prompt templates\n",
    "messages = [\n",
    "    SystemMessagePromptTemplate.from_template(system_template),\n",
    "    HumanMessagePromptTemplate.from_template(\"{question}\")\n",
    "]\n",
    "qa_prompt = ChatPromptTemplate.from_messages(messages)\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "qa = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), return_source_documents=False,combine_docs_chain_kwargs={\"prompt\": qa_prompt},memory=memory,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64ecb565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new StuffDocumentsChain chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: The provided : 1\n",
      "time: 2018-01-02\n",
      "susceptible: 3109\n",
      "infectious: 9118\n",
      "removed: 804\n",
      "city: city8\n",
      "\n",
      ": 1\n",
      "time: 2018-01-02\n",
      "susceptible: 3481\n",
      "infectious: 10873\n",
      "removed: 954\n",
      "city: city5\n",
      "\n",
      ": 23\n",
      "time: 2018-01-24\n",
      "susceptible: 0\n",
      "infectious: 2828\n",
      "removed: 24231\n",
      "city: city9\n",
      "\n",
      ": 1\n",
      "time: 2018-01-02\n",
      "susceptible: 8081\n",
      "infectious: 25424\n",
      "removed: 2231\n",
      "city: city6 is a tabular dataset containing Suspectible, infectious and removed population during 90 days in 10 cities.\n",
      "The dataset includes the following columns:\n",
      "'time': time the population was meseaured,\n",
      "'city': city in which the popoluation was measured,\n",
      "\"susceptible\": the susceptible population of the disease, \n",
      "\"infectious\": the infectious population of the disease, \n",
      "\"removed\": the removed popolation of the disease. \n",
      "----------------\n",
      ": 1\n",
      "time: 2018-01-02\n",
      "susceptible: 3109\n",
      "infectious: 9118\n",
      "removed: 804\n",
      "city: city8\n",
      "\n",
      ": 1\n",
      "time: 2018-01-02\n",
      "susceptible: 3481\n",
      "infectious: 10873\n",
      "removed: 954\n",
      "city: city5\n",
      "\n",
      ": 23\n",
      "time: 2018-01-24\n",
      "susceptible: 0\n",
      "infectious: 2828\n",
      "removed: 24231\n",
      "city: city9\n",
      "\n",
      ": 1\n",
      "time: 2018-01-02\n",
      "susceptible: 8081\n",
      "infectious: 25424\n",
      "removed: 2231\n",
      "city: city6\n",
      "Human: Which city has the most infetious people on 2018-02-03?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The dataset provided does not include data for the date 2018-02-03.'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa.run(\n",
    "    {\n",
    "        \"question\": \"Which city has the most infectious people on 2018-02-03?\"\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39366702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1729\\nremoved: 35608\\ncity: city3', metadata={'source': 'sir.csv', 'row': 301}),\n",
       " Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 3109\\ninfectious: 9118\\nremoved: 804\\ncity: city8', metadata={'source': 'sir.csv', 'row': 721}),\n",
       " Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 1\\ninfectious: 2035\\nremoved: 6507\\ncity: city7', metadata={'source': 'sir.csv', 'row': 645}),\n",
       " Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 3481\\ninfectious: 10873\\nremoved: 954\\ncity: city5', metadata={'source': 'sir.csv', 'row': 451}),\n",
       " Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 2828\\nremoved: 24231\\ncity: city9', metadata={'source': 'sir.csv', 'row': 833}),\n",
       " Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 8081\\ninfectious: 25424\\nremoved: 2231\\ncity: city6', metadata={'source': 'sir.csv', 'row': 541}),\n",
       " Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 511\\ninfectious: 9733\\nremoved: 2787\\ncity: city8', metadata={'source': 'sir.csv', 'row': 723}),\n",
       " Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 3510\\nremoved: 33826\\ncity: city3', metadata={'source': 'sir.csv', 'row': 294}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1413\\nremoved: 35924\\ncity: city3', metadata={'source': 'sir.csv', 'row': 303}),\n",
       " Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 3173\\nremoved: 34164\\ncity: city3', metadata={'source': 'sir.csv', 'row': 295}),\n",
       " Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 3857\\ninfectious: 12338\\nremoved: 1081\\ncity: city0', metadata={'source': 'sir.csv', 'row': 1}),\n",
       " Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 1365\\nremoved: 11666\\ncity: city8', metadata={'source': 'sir.csv', 'row': 743}),\n",
       " Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 2770\\nremoved: 10260\\ncity: city8', metadata={'source': 'sir.csv', 'row': 736}),\n",
       " Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 487\\ninfectious: 6280\\nremoved: 1775\\ncity: city7', metadata={'source': 'sir.csv', 'row': 633}),\n",
       " Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 0\\ninfectious: 3391\\nremoved: 9639\\ncity: city8', metadata={'source': 'sir.csv', 'row': 734}),\n",
       " Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 1849\\nremoved: 11182\\ncity: city8', metadata={'source': 'sir.csv', 'row': 740}),\n",
       " Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 1705\\nremoved: 25353\\ncity: city9', metadata={'source': 'sir.csv', 'row': 838}),\n",
       " Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 3884\\nremoved: 33453\\ncity: city3', metadata={'source': 'sir.csv', 'row': 293}),\n",
       " Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 1\\ninfectious: 1839\\nremoved: 6703\\ncity: city7', metadata={'source': 'sir.csv', 'row': 646}),\n",
       " Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 1\\ninfectious: 6350\\nremoved: 20708\\ncity: city9', metadata={'source': 'sir.csv', 'row': 825})]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"Which city has the most infectious people on 2018-02-03?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad5202f",
   "metadata": {},
   "source": [
    "## Customized CSV loader with metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47c7bddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetaDataCSVLoader(BaseLoader):\n",
    "    \"\"\"Loads a CSV file into a list of documents.\n",
    "\n",
    "    Each document represents one row of the CSV file. Every row is converted into a\n",
    "    key/value pair and outputted to a new line in the document's page_content.\n",
    "\n",
    "    The source for each document loaded from csv is set to the value of the\n",
    "    `file_path` argument for all doucments by default.\n",
    "    You can override this by setting the `source_column` argument to the\n",
    "    name of a column in the CSV file.\n",
    "    The source of each document will then be set to the value of the column\n",
    "    with the name specified in `source_column`.\n",
    "\n",
    "    Output Example:\n",
    "        .. code-block:: txt\n",
    "\n",
    "            column1: value1\n",
    "            column2: value2\n",
    "            column3: value3\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        file_path: str,\n",
    "        source_column: Optional[str] = None,\n",
    "        metadata_columns: Optional[List[str]] = None,   \n",
    "        content_columns: Optional[List[str]] =None ,  \n",
    "        csv_args: Optional[Dict] = None,\n",
    "        encoding: Optional[str] = None,\n",
    "    ):\n",
    "        self.file_path = file_path\n",
    "        self.source_column = source_column\n",
    "        self.encoding = encoding\n",
    "        self.csv_args = csv_args or {}\n",
    "        self.content_columns= content_columns\n",
    "        self.metadata_columns = metadata_columns        # < ADDED\n",
    "\n",
    "    def load(self) -> List[Document]:\n",
    "        \"\"\"Load data into document objects.\"\"\"\n",
    "\n",
    "        docs = []\n",
    "        with open(self.file_path, newline=\"\", encoding=self.encoding) as csvfile:\n",
    "            csv_reader = csv.DictReader(csvfile, **self.csv_args)  # type: ignore\n",
    "            for i, row in enumerate(csv_reader):\n",
    "                if self.content_columns: \n",
    "                    content = \"\\n\".join(f\"{k.strip()}: {v.strip()}\" for k, v in row.items() if k in self.content_columns)\n",
    "                else: \n",
    "                    content = \"\\n\".join(f\"{k.strip()}: {v.strip()}\" for k, v in row.items())\n",
    "                try:\n",
    "                    source = (\n",
    "                        row[self.source_column]\n",
    "                        if self.source_column is not None\n",
    "                        else self.file_path\n",
    "                    )\n",
    "                except KeyError:\n",
    "                    raise ValueError(\n",
    "                        f\"Source column '{self.source_column}' not found in CSV file.\"\n",
    "                    )\n",
    "                metadata = {\"source\": source, \"row\": i}\n",
    "                # ADDED TO SAVE METADATA\n",
    "                if self.metadata_columns:\n",
    "                    for k, v in row.items():\n",
    "                        if k in self.metadata_columns:\n",
    "                            metadata[k] = v\n",
    "                # END OF ADDED CODE\n",
    "                doc = Document(page_content=content, metadata=metadata)\n",
    "                docs.append(doc)\n",
    "\n",
    "        return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8a5bea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and set embeddings \n",
    "loader = MetaDataCSVLoader(file_path=\"sir.csv\",metadata_columns=['time','city'])\n",
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e68378a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 8639\\ninfectious: 8639\\nremoved: 0\\ncity: city0', metadata={'source': 'sir.csv', 'row': 0, 'time': '2018-01-01', 'city': 'city0'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 3857\\ninfectious: 12338\\nremoved: 1081\\ncity: city0', metadata={'source': 'sir.csv', 'row': 1, 'time': '2018-01-02', 'city': 'city0'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 1458\\ninfectious: 13414\\nremoved: 2405\\ncity: city0', metadata={'source': 'sir.csv', 'row': 2, 'time': '2018-01-03', 'city': 'city0'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 545\\ninfectious: 12983\\nremoved: 3749\\ncity: city0', metadata={'source': 'sir.csv', 'row': 3, 'time': '2018-01-04', 'city': 'city0'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 214\\ninfectious: 12046\\nremoved: 5017\\ncity: city0', metadata={'source': 'sir.csv', 'row': 4, 'time': '2018-01-05', 'city': 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'city': 'city2'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 7\\ninfectious: 13509\\nremoved: 21309\\ncity: city2', metadata={'source': 'sir.csv', 'row': 190, 'time': '2018-01-11', 'city': 'city2'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 4\\ninfectious: 12212\\nremoved: 22608\\ncity: city2', metadata={'source': 'sir.csv', 'row': 191, 'time': '2018-01-12', 'city': 'city2'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 3\\ninfectious: 11039\\nremoved: 23783\\ncity: city2', metadata={'source': 'sir.csv', 'row': 192, 'time': '2018-01-13', 'city': 'city2'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 2\\ninfectious: 9978\\nremoved: 24845\\ncity: city2', metadata={'source': 'sir.csv', 'row': 193, 'time': '2018-01-14', 'city': 'city2'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 1\\ninfectious: 9019\\nremoved: 25804\\ncity: city2', metadata={'source': 'sir.csv', 'row': 194, 'time': '2018-01-15', 'city': 'city2'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 1\\ninfectious: 8152\\nremoved: 26672\\ncity: city2', metadata={'source': 'sir.csv', 'row': 195, 'time': '2018-01-16', 'city': 'city2'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 7368\\nremoved: 27456\\ncity: city2', metadata={'source': 'sir.csv', 'row': 196, 'time': '2018-01-17', 'city': 'city2'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 6660\\nremoved: 28165\\ncity: city2', metadata={'source': 'sir.csv', 'row': 197, 'time': '2018-01-18', 'city': 'city2'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 6019\\nremoved: 28805\\ncity: city2', metadata={'source': 'sir.csv', 'row': 198, 'time': '2018-01-19', 'city': 'city2'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 5440\\nremoved: 29384\\ncity: city2', metadata={'source': 'sir.csv', 'row': 199, 'time': '2018-01-20', 'city': 'city2'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 4917\\nremoved: 29908\\ncity: city2', metadata={'source': 'sir.csv', 'row': 200, 'time': '2018-01-21', 'city': 'city2'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 4444\\nremoved: 30381\\ncity: city2', metadata={'source': 'sir.csv', 'row': 201, 'time': '2018-01-22', 'city': 'city2'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 4017\\nremoved: 30808\\ncity: city2', metadata={'source': 'sir.csv', 'row': 202, 'time': '2018-01-23', 'city': 'city2'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 3630\\nremoved: 31194\\ncity: city2', metadata={'source': 'sir.csv', 'row': 203, 'time': '2018-01-24', 'city': 'city2'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 3281\\nremoved: 31544\\ncity: city2', metadata={'source': 'sir.csv', 'row': 204, 'time': '2018-01-25', 'city': 'city2'}), Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 2966\\nremoved: 31859\\ncity: city2', metadata={'source': 'sir.csv', 'row': 205, 'time': '2018-01-26', 'city': 'city2'}), Document(page_content=': 26\\ntime: 2018-01-27\\nsusceptible: 0\\ninfectious: 2680\\nremoved: 32145\\ncity: city2', metadata={'source': 'sir.csv', 'row': 206, 'time': '2018-01-27', 'city': 'city2'}), Document(page_content=': 27\\ntime: 2018-01-28\\nsusceptible: 0\\ninfectious: 2422\\nremoved: 32402\\ncity: city2', metadata={'source': 'sir.csv', 'row': 207, 'time': '2018-01-28', 'city': 'city2'}), Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 2189\\nremoved: 32635\\ncity: city2', metadata={'source': 'sir.csv', 'row': 208, 'time': '2018-01-29', 'city': 'city2'}), Document(page_content=': 29\\ntime: 2018-01-30\\nsusceptible: 0\\ninfectious: 1979\\nremoved: 32846\\ncity: city2', metadata={'source': 'sir.csv', 'row': 209, 'time': '2018-01-30', 'city': 'city2'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 1788\\nremoved: 33036\\ncity: city2', metadata={'source': 'sir.csv', 'row': 210, 'time': '2018-01-31', 'city': 'city2'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1616\\nremoved: 33208\\ncity: city2', metadata={'source': 'sir.csv', 'row': 211, 'time': '2018-02-01', 'city': 'city2'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 1461\\nremoved: 33364\\ncity: city2', metadata={'source': 'sir.csv', 'row': 212, 'time': '2018-02-02', 'city': 'city2'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1320\\nremoved: 33505\\ncity: city2', metadata={'source': 'sir.csv', 'row': 213, 'time': '2018-02-03', 'city': 'city2'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 1193\\nremoved: 33632\\ncity: city2', metadata={'source': 'sir.csv', 'row': 214, 'time': '2018-02-04', 'city': 'city2'}), Document(page_content=': 35\\ntime: 2018-02-05\\nsusceptible: 0\\ninfectious: 1079\\nremoved: 33746\\ncity: city2', metadata={'source': 'sir.csv', 'row': 215, 'time': '2018-02-05', 'city': 'city2'}), Document(page_content=': 36\\ntime: 2018-02-06\\nsusceptible: 0\\ninfectious: 975\\nremoved: 33850\\ncity: city2', metadata={'source': 'sir.csv', 'row': 216, 'time': '2018-02-06', 'city': 'city2'}), Document(page_content=': 37\\ntime: 2018-02-07\\nsusceptible: 0\\ninfectious: 881\\nremoved: 33944\\ncity: city2', metadata={'source': 'sir.csv', 'row': 217, 'time': '2018-02-07', 'city': 'city2'}), Document(page_content=': 38\\ntime: 2018-02-08\\nsusceptible: 0\\ninfectious: 796\\nremoved: 34029\\ncity: city2', metadata={'source': 'sir.csv', 'row': 218, 'time': '2018-02-08', 'city': 'city2'}), Document(page_content=': 39\\ntime: 2018-02-09\\nsusceptible: 0\\ninfectious: 720\\nremoved: 34105\\ncity: city2', metadata={'source': 'sir.csv', 'row': 219, 'time': '2018-02-09', 'city': 'city2'}), Document(page_content=': 40\\ntime: 2018-02-10\\nsusceptible: 0\\ninfectious: 650\\nremoved: 34175\\ncity: city2', metadata={'source': 'sir.csv', 'row': 220, 'time': '2018-02-10', 'city': 'city2'}), Document(page_content=': 41\\ntime: 2018-02-11\\nsusceptible: 0\\ninfectious: 588\\nremoved: 34237\\ncity: city2', metadata={'source': 'sir.csv', 'row': 221, 'time': '2018-02-11', 'city': 'city2'}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 531\\nremoved: 34294\\ncity: city2', metadata={'source': 'sir.csv', 'row': 222, 'time': '2018-02-12', 'city': 'city2'}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 480\\nremoved: 34345\\ncity: city2', metadata={'source': 'sir.csv', 'row': 223, 'time': '2018-02-13', 'city': 'city2'}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 434\\nremoved: 34391\\ncity: city2', metadata={'source': 'sir.csv', 'row': 224, 'time': '2018-02-14', 'city': 'city2'}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 392\\nremoved: 34433\\ncity: city2', metadata={'source': 'sir.csv', 'row': 225, 'time': '2018-02-15', 'city': 'city2'}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 354\\nremoved: 34471\\ncity: city2', metadata={'source': 'sir.csv', 'row': 226, 'time': '2018-02-16', 'city': 'city2'}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 320\\nremoved: 34505\\ncity: city2', metadata={'source': 'sir.csv', 'row': 227, 'time': '2018-02-17', 'city': 'city2'}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 289\\nremoved: 34536\\ncity: city2', metadata={'source': 'sir.csv', 'row': 228, 'time': '2018-02-18', 'city': 'city2'}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 261\\nremoved: 34564\\ncity: city2', metadata={'source': 'sir.csv', 'row': 229, 'time': '2018-02-19', 'city': 'city2'}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 236\\nremoved: 34589\\ncity: city2', metadata={'source': 'sir.csv', 'row': 230, 'time': '2018-02-20', 'city': 'city2'}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 213\\nremoved: 34611\\ncity: city2', metadata={'source': 'sir.csv', 'row': 231, 'time': '2018-02-21', 'city': 'city2'}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 193\\nremoved: 34632\\ncity: city2', metadata={'source': 'sir.csv', 'row': 232, 'time': '2018-02-22', 'city': 'city2'}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 174\\nremoved: 34651\\ncity: city2', metadata={'source': 'sir.csv', 'row': 233, 'time': '2018-02-23', 'city': 'city2'}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 157\\nremoved: 34667\\ncity: city2', metadata={'source': 'sir.csv', 'row': 234, 'time': '2018-02-24', 'city': 'city2'}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 142\\nremoved: 34683\\ncity: city2', metadata={'source': 'sir.csv', 'row': 235, 'time': '2018-02-25', 'city': 'city2'}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 129\\nremoved: 34696\\ncity: city2', metadata={'source': 'sir.csv', 'row': 236, 'time': '2018-02-26', 'city': 'city2'}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 116\\nremoved: 34709\\ncity: city2', metadata={'source': 'sir.csv', 'row': 237, 'time': '2018-02-27', 'city': 'city2'}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 105\\nremoved: 34720\\ncity: city2', metadata={'source': 'sir.csv', 'row': 238, 'time': '2018-02-28', 'city': 'city2'}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 95\\nremoved: 34730\\ncity: city2', metadata={'source': 'sir.csv', 'row': 239, 'time': '2018-03-01', 'city': 'city2'}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 86\\nremoved: 34739\\ncity: city2', metadata={'source': 'sir.csv', 'row': 240, 'time': '2018-03-02', 'city': 'city2'}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 77\\nremoved: 34748\\ncity: city2', metadata={'source': 'sir.csv', 'row': 241, 'time': '2018-03-03', 'city': 'city2'}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 70\\nremoved: 34755\\ncity: city2', metadata={'source': 'sir.csv', 'row': 242, 'time': '2018-03-04', 'city': 'city2'}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 63\\nremoved: 34762\\ncity: city2', metadata={'source': 'sir.csv', 'row': 243, 'time': '2018-03-05', 'city': 'city2'}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 57\\nremoved: 34768\\ncity: city2', metadata={'source': 'sir.csv', 'row': 244, 'time': '2018-03-06', 'city': 'city2'}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 51\\nremoved: 34774\\ncity: city2', metadata={'source': 'sir.csv', 'row': 245, 'time': '2018-03-07', 'city': 'city2'}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 46\\nremoved: 34778\\ncity: city2', metadata={'source': 'sir.csv', 'row': 246, 'time': '2018-03-08', 'city': 'city2'}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 42\\nremoved: 34783\\ncity: city2', metadata={'source': 'sir.csv', 'row': 247, 'time': '2018-03-09', 'city': 'city2'}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 38\\nremoved: 34787\\ncity: city2', metadata={'source': 'sir.csv', 'row': 248, 'time': '2018-03-10', 'city': 'city2'}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 34\\nremoved: 34791\\ncity: city2', metadata={'source': 'sir.csv', 'row': 249, 'time': '2018-03-11', 'city': 'city2'}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 31\\nremoved: 34794\\ncity: city2', metadata={'source': 'sir.csv', 'row': 250, 'time': '2018-03-12', 'city': 'city2'}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 28\\nremoved: 34797\\ncity: city2', metadata={'source': 'sir.csv', 'row': 251, 'time': '2018-03-13', 'city': 'city2'}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 25\\nremoved: 34800\\ncity: city2', metadata={'source': 'sir.csv', 'row': 252, 'time': '2018-03-14', 'city': 'city2'}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 23\\nremoved: 34802\\ncity: city2', metadata={'source': 'sir.csv', 'row': 253, 'time': '2018-03-15', 'city': 'city2'}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 20\\nremoved: 34805\\ncity: city2', metadata={'source': 'sir.csv', 'row': 254, 'time': '2018-03-16', 'city': 'city2'}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 18\\nremoved: 34807\\ncity: city2', metadata={'source': 'sir.csv', 'row': 255, 'time': '2018-03-17', 'city': 'city2'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 17\\nremoved: 34808\\ncity: city2', metadata={'source': 'sir.csv', 'row': 256, 'time': '2018-03-18', 'city': 'city2'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 15\\nremoved: 34810\\ncity: city2', metadata={'source': 'sir.csv', 'row': 257, 'time': '2018-03-19', 'city': 'city2'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 13\\nremoved: 34811\\ncity: city2', metadata={'source': 'sir.csv', 'row': 258, 'time': '2018-03-20', 'city': 'city2'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 12\\nremoved: 34813\\ncity: city2', metadata={'source': 'sir.csv', 'row': 259, 'time': '2018-03-21', 'city': 'city2'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 11\\nremoved: 34814\\ncity: city2', metadata={'source': 'sir.csv', 'row': 260, 'time': '2018-03-22', 'city': 'city2'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 10\\nremoved: 34815\\ncity: city2', metadata={'source': 'sir.csv', 'row': 261, 'time': '2018-03-23', 'city': 'city2'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 9\\nremoved: 34816\\ncity: city2', metadata={'source': 'sir.csv', 'row': 262, 'time': '2018-03-24', 'city': 'city2'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 8\\nremoved: 34817\\ncity: city2', metadata={'source': 'sir.csv', 'row': 263, 'time': '2018-03-25', 'city': 'city2'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 7\\nremoved: 34818\\ncity: city2', metadata={'source': 'sir.csv', 'row': 264, 'time': '2018-03-26', 'city': 'city2'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 6\\nremoved: 34819\\ncity: city2', metadata={'source': 'sir.csv', 'row': 265, 'time': '2018-03-27', 'city': 'city2'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 6\\nremoved: 34819\\ncity: city2', metadata={'source': 'sir.csv', 'row': 266, 'time': '2018-03-28', 'city': 'city2'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 5\\nremoved: 34820\\ncity: city2', metadata={'source': 'sir.csv', 'row': 267, 'time': '2018-03-29', 'city': 'city2'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 5\\nremoved: 34820\\ncity: city2', metadata={'source': 'sir.csv', 'row': 268, 'time': '2018-03-30', 'city': 'city2'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 4\\nremoved: 34821\\ncity: city2', metadata={'source': 'sir.csv', 'row': 269, 'time': '2018-03-31', 'city': 'city2'}), Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 18669\\ninfectious: 18669\\nremoved: 0\\ncity: city3', metadata={'source': 'sir.csv', 'row': 270, 'time': '2018-01-01', 'city': 'city3'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 8207\\ninfectious: 26784\\nremoved: 2345\\ncity: city3', metadata={'source': 'sir.csv', 'row': 271, 'time': '2018-01-02', 'city': 'city3'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 3048\\ninfectious: 29072\\nremoved: 5217\\ncity: city3', metadata={'source': 'sir.csv', 'row': 272, 'time': '2018-01-03', 'city': 'city3'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 1121\\ninfectious: 28089\\nremoved: 8127\\ncity: city3', metadata={'source': 'sir.csv', 'row': 273, 'time': '2018-01-04', 'city': 'city3'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 434\\ninfectious: 26034\\nremoved: 10869\\ncity: city3', metadata={'source': 'sir.csv', 'row': 274, 'time': '2018-01-05', 'city': 'city3'}), Document(page_content=': 5\\ntime: 2018-01-06\\nsusceptible: 181\\ninfectious: 23769\\nremoved: 13387\\ncity: city3', metadata={'source': 'sir.csv', 'row': 275, 'time': '2018-01-06', 'city': 'city3'}), Document(page_content=': 6\\ntime: 2018-01-07\\nsusceptible: 81\\ninfectious: 21577\\nremoved: 15679\\ncity: city3', metadata={'source': 'sir.csv', 'row': 276, 'time': '2018-01-07', 'city': 'city3'}), Document(page_content=': 7\\ntime: 2018-01-08\\nsusceptible: 39\\ninfectious: 19541\\nremoved: 17757\\ncity: city3', metadata={'source': 'sir.csv', 'row': 277, 'time': '2018-01-08', 'city': 'city3'}), Document(page_content=': 8\\ntime: 2018-01-09\\nsusceptible: 20\\ninfectious: 17679\\nremoved: 19637\\ncity: city3', metadata={'source': 'sir.csv', 'row': 278, 'time': '2018-01-09', 'city': 'city3'}), Document(page_content=': 9\\ntime: 2018-01-10\\nsusceptible: 11\\ninfectious: 15988\\nremoved: 21338\\ncity: city3', metadata={'source': 'sir.csv', 'row': 279, 'time': '2018-01-10', 'city': 'city3'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 6\\ninfectious: 14454\\nremoved: 22876\\ncity: city3', metadata={'source': 'sir.csv', 'row': 280, 'time': '2018-01-11', 'city': 'city3'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 4\\ninfectious: 13067\\nremoved: 24266\\ncity: city3', metadata={'source': 'sir.csv', 'row': 281, 'time': '2018-01-12', 'city': 'city3'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 2\\ninfectious: 11811\\nremoved: 25523\\ncity: city3', metadata={'source': 'sir.csv', 'row': 282, 'time': '2018-01-13', 'city': 'city3'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 1\\ninfectious: 10676\\nremoved: 26659\\ncity: city3', metadata={'source': 'sir.csv', 'row': 283, 'time': '2018-01-14', 'city': 'city3'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 1\\ninfectious: 9650\\nremoved: 27686\\ncity: city3', metadata={'source': 'sir.csv', 'row': 284, 'time': '2018-01-15', 'city': 'city3'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 0\\ninfectious: 8722\\nremoved: 28614\\ncity: city3', metadata={'source': 'sir.csv', 'row': 285, 'time': '2018-01-16', 'city': 'city3'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 7883\\nremoved: 29453\\ncity: city3', metadata={'source': 'sir.csv', 'row': 286, 'time': '2018-01-17', 'city': 'city3'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 7125\\nremoved: 30212\\ncity: city3', metadata={'source': 'sir.csv', 'row': 287, 'time': '2018-01-18', 'city': 'city3'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 6440\\nremoved: 30897\\ncity: city3', metadata={'source': 'sir.csv', 'row': 288, 'time': '2018-01-19', 'city': 'city3'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 5820\\nremoved: 31516\\ncity: city3', metadata={'source': 'sir.csv', 'row': 289, 'time': '2018-01-20', 'city': 'city3'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 5261\\nremoved: 32076\\ncity: city3', metadata={'source': 'sir.csv', 'row': 290, 'time': '2018-01-21', 'city': 'city3'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 4755\\nremoved: 32582\\ncity: city3', metadata={'source': 'sir.csv', 'row': 291, 'time': '2018-01-22', 'city': 'city3'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 4297\\nremoved: 33039\\ncity: city3', metadata={'source': 'sir.csv', 'row': 292, 'time': '2018-01-23', 'city': 'city3'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 3884\\nremoved: 33453\\ncity: city3', metadata={'source': 'sir.csv', 'row': 293, 'time': '2018-01-24', 'city': 'city3'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 3510\\nremoved: 33826\\ncity: city3', metadata={'source': 'sir.csv', 'row': 294, 'time': '2018-01-25', 'city': 'city3'}), Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 3173\\nremoved: 34164\\ncity: city3', metadata={'source': 'sir.csv', 'row': 295, 'time': '2018-01-26', 'city': 'city3'}), Document(page_content=': 26\\ntime: 2018-01-27\\nsusceptible: 0\\ninfectious: 2868\\nremoved: 34469\\ncity: city3', metadata={'source': 'sir.csv', 'row': 296, 'time': '2018-01-27', 'city': 'city3'}), Document(page_content=': 27\\ntime: 2018-01-28\\nsusceptible: 0\\ninfectious: 2592\\nremoved: 34745\\ncity: city3', metadata={'source': 'sir.csv', 'row': 297, 'time': '2018-01-28', 'city': 'city3'}), Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 2342\\nremoved: 34994\\ncity: city3', metadata={'source': 'sir.csv', 'row': 298, 'time': '2018-01-29', 'city': 'city3'}), Document(page_content=': 29\\ntime: 2018-01-30\\nsusceptible: 0\\ninfectious: 2117\\nremoved: 35220\\ncity: city3', metadata={'source': 'sir.csv', 'row': 299, 'time': '2018-01-30', 'city': 'city3'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 1913\\nremoved: 35423\\ncity: city3', metadata={'source': 'sir.csv', 'row': 300, 'time': '2018-01-31', 'city': 'city3'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1729\\nremoved: 35608\\ncity: city3', metadata={'source': 'sir.csv', 'row': 301, 'time': '2018-02-01', 'city': 'city3'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 1563\\nremoved: 35774\\ncity: city3', metadata={'source': 'sir.csv', 'row': 302, 'time': '2018-02-02', 'city': 'city3'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1413\\nremoved: 35924\\ncity: city3', metadata={'source': 'sir.csv', 'row': 303, 'time': '2018-02-03', 'city': 'city3'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 1277\\nremoved: 36060\\ncity: city3', metadata={'source': 'sir.csv', 'row': 304, 'time': '2018-02-04', 'city': 'city3'}), Document(page_content=': 35\\ntime: 2018-02-05\\nsusceptible: 0\\ninfectious: 1154\\nremoved: 36183\\ncity: city3', metadata={'source': 'sir.csv', 'row': 305, 'time': '2018-02-05', 'city': 'city3'}), Document(page_content=': 36\\ntime: 2018-02-06\\nsusceptible: 0\\ninfectious: 1043\\nremoved: 36294\\ncity: city3', metadata={'source': 'sir.csv', 'row': 306, 'time': '2018-02-06', 'city': 'city3'}), Document(page_content=': 37\\ntime: 2018-02-07\\nsusceptible: 0\\ninfectious: 943\\nremoved: 36394\\ncity: city3', metadata={'source': 'sir.csv', 'row': 307, 'time': '2018-02-07', 'city': 'city3'}), Document(page_content=': 38\\ntime: 2018-02-08\\nsusceptible: 0\\ninfectious: 852\\nremoved: 36485\\ncity: city3', metadata={'source': 'sir.csv', 'row': 308, 'time': '2018-02-08', 'city': 'city3'}), Document(page_content=': 39\\ntime: 2018-02-09\\nsusceptible: 0\\ninfectious: 770\\nremoved: 36567\\ncity: city3', metadata={'source': 'sir.csv', 'row': 309, 'time': '2018-02-09', 'city': 'city3'}), Document(page_content=': 40\\ntime: 2018-02-10\\nsusceptible: 0\\ninfectious: 696\\nremoved: 36641\\ncity: city3', metadata={'source': 'sir.csv', 'row': 310, 'time': '2018-02-10', 'city': 'city3'}), Document(page_content=': 41\\ntime: 2018-02-11\\nsusceptible: 0\\ninfectious: 629\\nremoved: 36708\\ncity: city3', metadata={'source': 'sir.csv', 'row': 311, 'time': '2018-02-11', 'city': 'city3'}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 568\\nremoved: 36769\\ncity: city3', metadata={'source': 'sir.csv', 'row': 312, 'time': '2018-02-12', 'city': 'city3'}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 514\\nremoved: 36823\\ncity: city3', metadata={'source': 'sir.csv', 'row': 313, 'time': '2018-02-13', 'city': 'city3'}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 464\\nremoved: 36873\\ncity: city3', metadata={'source': 'sir.csv', 'row': 314, 'time': '2018-02-14', 'city': 'city3'}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 419\\nremoved: 36918\\ncity: city3', metadata={'source': 'sir.csv', 'row': 315, 'time': '2018-02-15', 'city': 'city3'}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 379\\nremoved: 36958\\ncity: city3', metadata={'source': 'sir.csv', 'row': 316, 'time': '2018-02-16', 'city': 'city3'}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 343\\nremoved: 36994\\ncity: city3', metadata={'source': 'sir.csv', 'row': 317, 'time': '2018-02-17', 'city': 'city3'}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 310\\nremoved: 37027\\ncity: city3', metadata={'source': 'sir.csv', 'row': 318, 'time': '2018-02-18', 'city': 'city3'}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 280\\nremoved: 37057\\ncity: city3', metadata={'source': 'sir.csv', 'row': 319, 'time': '2018-02-19', 'city': 'city3'}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 253\\nremoved: 37084\\ncity: city3', metadata={'source': 'sir.csv', 'row': 320, 'time': '2018-02-20', 'city': 'city3'}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 228\\nremoved: 37109\\ncity: city3', metadata={'source': 'sir.csv', 'row': 321, 'time': '2018-02-21', 'city': 'city3'}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 206\\nremoved: 37131\\ncity: city3', metadata={'source': 'sir.csv', 'row': 322, 'time': '2018-02-22', 'city': 'city3'}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 187\\nremoved: 37150\\ncity: city3', metadata={'source': 'sir.csv', 'row': 323, 'time': '2018-02-23', 'city': 'city3'}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 169\\nremoved: 37168\\ncity: city3', metadata={'source': 'sir.csv', 'row': 324, 'time': '2018-02-24', 'city': 'city3'}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 152\\nremoved: 37185\\ncity: city3', metadata={'source': 'sir.csv', 'row': 325, 'time': '2018-02-25', 'city': 'city3'}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 138\\nremoved: 37199\\ncity: city3', metadata={'source': 'sir.csv', 'row': 326, 'time': '2018-02-26', 'city': 'city3'}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 124\\nremoved: 37213\\ncity: city3', metadata={'source': 'sir.csv', 'row': 327, 'time': '2018-02-27', 'city': 'city3'}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 112\\nremoved: 37225\\ncity: city3', metadata={'source': 'sir.csv', 'row': 328, 'time': '2018-02-28', 'city': 'city3'}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 101\\nremoved: 37236\\ncity: city3', metadata={'source': 'sir.csv', 'row': 329, 'time': '2018-03-01', 'city': 'city3'}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 92\\nremoved: 37245\\ncity: city3', metadata={'source': 'sir.csv', 'row': 330, 'time': '2018-03-02', 'city': 'city3'}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 83\\nremoved: 37254\\ncity: city3', metadata={'source': 'sir.csv', 'row': 331, 'time': '2018-03-03', 'city': 'city3'}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 75\\nremoved: 37262\\ncity: city3', metadata={'source': 'sir.csv', 'row': 332, 'time': '2018-03-04', 'city': 'city3'}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 68\\nremoved: 37269\\ncity: city3', metadata={'source': 'sir.csv', 'row': 333, 'time': '2018-03-05', 'city': 'city3'}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 61\\nremoved: 37276\\ncity: city3', metadata={'source': 'sir.csv', 'row': 334, 'time': '2018-03-06', 'city': 'city3'}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 55\\nremoved: 37282\\ncity: city3', metadata={'source': 'sir.csv', 'row': 335, 'time': '2018-03-07', 'city': 'city3'}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 50\\nremoved: 37287\\ncity: city3', metadata={'source': 'sir.csv', 'row': 336, 'time': '2018-03-08', 'city': 'city3'}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 45\\nremoved: 37292\\ncity: city3', metadata={'source': 'sir.csv', 'row': 337, 'time': '2018-03-09', 'city': 'city3'}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 41\\nremoved: 37296\\ncity: city3', metadata={'source': 'sir.csv', 'row': 338, 'time': '2018-03-10', 'city': 'city3'}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 37\\nremoved: 37300\\ncity: city3', metadata={'source': 'sir.csv', 'row': 339, 'time': '2018-03-11', 'city': 'city3'}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 33\\nremoved: 37304\\ncity: city3', metadata={'source': 'sir.csv', 'row': 340, 'time': '2018-03-12', 'city': 'city3'}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 30\\nremoved: 37307\\ncity: city3', metadata={'source': 'sir.csv', 'row': 341, 'time': '2018-03-13', 'city': 'city3'}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 27\\nremoved: 37310\\ncity: city3', metadata={'source': 'sir.csv', 'row': 342, 'time': '2018-03-14', 'city': 'city3'}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 24\\nremoved: 37313\\ncity: city3', metadata={'source': 'sir.csv', 'row': 343, 'time': '2018-03-15', 'city': 'city3'}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 22\\nremoved: 37315\\ncity: city3', metadata={'source': 'sir.csv', 'row': 344, 'time': '2018-03-16', 'city': 'city3'}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 20\\nremoved: 37317\\ncity: city3', metadata={'source': 'sir.csv', 'row': 345, 'time': '2018-03-17', 'city': 'city3'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 18\\nremoved: 37319\\ncity: city3', metadata={'source': 'sir.csv', 'row': 346, 'time': '2018-03-18', 'city': 'city3'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 16\\nremoved: 37321\\ncity: city3', metadata={'source': 'sir.csv', 'row': 347, 'time': '2018-03-19', 'city': 'city3'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 14\\nremoved: 37323\\ncity: city3', metadata={'source': 'sir.csv', 'row': 348, 'time': '2018-03-20', 'city': 'city3'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 13\\nremoved: 37324\\ncity: city3', metadata={'source': 'sir.csv', 'row': 349, 'time': '2018-03-21', 'city': 'city3'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 12\\nremoved: 37325\\ncity: city3', metadata={'source': 'sir.csv', 'row': 350, 'time': '2018-03-22', 'city': 'city3'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 11\\nremoved: 37326\\ncity: city3', metadata={'source': 'sir.csv', 'row': 351, 'time': '2018-03-23', 'city': 'city3'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 9\\nremoved: 37327\\ncity: city3', metadata={'source': 'sir.csv', 'row': 352, 'time': '2018-03-24', 'city': 'city3'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 9\\nremoved: 37328\\ncity: city3', metadata={'source': 'sir.csv', 'row': 353, 'time': '2018-03-25', 'city': 'city3'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 8\\nremoved: 37329\\ncity: city3', metadata={'source': 'sir.csv', 'row': 354, 'time': '2018-03-26', 'city': 'city3'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 7\\nremoved: 37330\\ncity: city3', metadata={'source': 'sir.csv', 'row': 355, 'time': '2018-03-27', 'city': 'city3'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 6\\nremoved: 37331\\ncity: city3', metadata={'source': 'sir.csv', 'row': 356, 'time': '2018-03-28', 'city': 'city3'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 6\\nremoved: 37331\\ncity: city3', metadata={'source': 'sir.csv', 'row': 357, 'time': '2018-03-29', 'city': 'city3'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 5\\nremoved: 37332\\ncity: city3', metadata={'source': 'sir.csv', 'row': 358, 'time': '2018-03-30', 'city': 'city3'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 4\\nremoved: 37333\\ncity: city3', metadata={'source': 'sir.csv', 'row': 359, 'time': '2018-03-31', 'city': 'city3'}), Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 10859\\ninfectious: 10859\\nremoved: 0\\ncity: city4', metadata={'source': 'sir.csv', 'row': 360, 'time': '2018-01-01', 'city': 'city4'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 4833\\ninfectious: 15523\\nremoved: 1360\\ncity: city4', metadata={'source': 'sir.csv', 'row': 361, 'time': '2018-01-02', 'city': 'city4'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 1820\\ninfectious: 16871\\nremoved: 3025\\ncity: city4', metadata={'source': 'sir.csv', 'row': 362, 'time': '2018-01-03', 'city': 'city4'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 678\\ninfectious: 16323\\nremoved: 4715\\ncity: city4', metadata={'source': 'sir.csv', 'row': 363, 'time': '2018-01-04', 'city': 'city4'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 266\\ninfectious: 15142\\nremoved: 6309\\ncity: city4', metadata={'source': 'sir.csv', 'row': 364, 'time': '2018-01-05', 'city': 'city4'}), Document(page_content=': 5\\ntime: 2018-01-06\\nsusceptible: 112\\ninfectious: 13830\\nremoved: 7774\\ncity: city4', metadata={'source': 'sir.csv', 'row': 365, 'time': '2018-01-06', 'city': 'city4'}), Document(page_content=': 6\\ntime: 2018-01-07\\nsusceptible: 50\\ninfectious: 12558\\nremoved: 9108\\ncity: city4', metadata={'source': 'sir.csv', 'row': 366, 'time': '2018-01-07', 'city': 'city4'}), Document(page_content=': 7\\ntime: 2018-01-08\\nsusceptible: 24\\ninfectious: 11375\\nremoved: 10318\\ncity: city4', metadata={'source': 'sir.csv', 'row': 367, 'time': '2018-01-08', 'city': 'city4'}), Document(page_content=': 8\\ntime: 2018-01-09\\nsusceptible: 12\\ninfectious: 10292\\nremoved: 11412\\ncity: city4', metadata={'source': 'sir.csv', 'row': 368, 'time': '2018-01-09', 'city': 'city4'}), Document(page_content=': 9\\ntime: 2018-01-10\\nsusceptible: 7\\ninfectious: 9307\\nremoved: 12402\\ncity: city4', metadata={'source': 'sir.csv', 'row': 369, 'time': '2018-01-10', 'city': 'city4'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 4\\ninfectious: 8415\\nremoved: 13298\\ncity: city4', metadata={'source': 'sir.csv', 'row': 370, 'time': '2018-01-11', 'city': 'city4'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 2\\ninfectious: 7607\\nremoved: 14107\\ncity: city4', metadata={'source': 'sir.csv', 'row': 371, 'time': '2018-01-12', 'city': 'city4'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 1\\ninfectious: 6876\\nremoved: 14839\\ncity: city4', metadata={'source': 'sir.csv', 'row': 372, 'time': '2018-01-13', 'city': 'city4'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 1\\ninfectious: 6215\\nremoved: 15500\\ncity: city4', metadata={'source': 'sir.csv', 'row': 373, 'time': '2018-01-14', 'city': 'city4'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 0\\ninfectious: 5618\\nremoved: 16098\\ncity: city4', metadata={'source': 'sir.csv', 'row': 374, 'time': '2018-01-15', 'city': 'city4'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 0\\ninfectious: 5078\\nremoved: 16639\\ncity: city4', metadata={'source': 'sir.csv', 'row': 375, 'time': '2018-01-16', 'city': 'city4'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 4589\\nremoved: 17127\\ncity: city4', metadata={'source': 'sir.csv', 'row': 376, 'time': '2018-01-17', 'city': 'city4'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 4148\\nremoved: 17569\\ncity: city4', metadata={'source': 'sir.csv', 'row': 377, 'time': '2018-01-18', 'city': 'city4'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 3749\\nremoved: 17968\\ncity: city4', metadata={'source': 'sir.csv', 'row': 378, 'time': '2018-01-19', 'city': 'city4'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 3389\\nremoved: 18328\\ncity: city4', metadata={'source': 'sir.csv', 'row': 379, 'time': '2018-01-20', 'city': 'city4'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 3063\\nremoved: 18654\\ncity: city4', metadata={'source': 'sir.csv', 'row': 380, 'time': '2018-01-21', 'city': 'city4'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 2768\\nremoved: 18949\\ncity: city4', metadata={'source': 'sir.csv', 'row': 381, 'time': '2018-01-22', 'city': 'city4'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 2502\\nremoved: 19215\\ncity: city4', metadata={'source': 'sir.csv', 'row': 382, 'time': '2018-01-23', 'city': 'city4'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 2261\\nremoved: 19456\\ncity: city4', metadata={'source': 'sir.csv', 'row': 383, 'time': '2018-01-24', 'city': 'city4'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 2044\\nremoved: 19673\\ncity: city4', metadata={'source': 'sir.csv', 'row': 384, 'time': '2018-01-25', 'city': 'city4'}), Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 1847\\nremoved: 19870\\ncity: city4', metadata={'source': 'sir.csv', 'row': 385, 'time': '2018-01-26', 'city': 'city4'}), Document(page_content=': 26\\ntime: 2018-01-27\\nsusceptible: 0\\ninfectious: 1669\\nremoved: 20048\\ncity: city4', metadata={'source': 'sir.csv', 'row': 386, 'time': '2018-01-27', 'city': 'city4'}), Document(page_content=': 27\\ntime: 2018-01-28\\nsusceptible: 0\\ninfectious: 1509\\nremoved: 20208\\ncity: city4', metadata={'source': 'sir.csv', 'row': 387, 'time': '2018-01-28', 'city': 'city4'}), Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 1364\\nremoved: 20353\\ncity: city4', metadata={'source': 'sir.csv', 'row': 388, 'time': '2018-01-29', 'city': 'city4'}), Document(page_content=': 29\\ntime: 2018-01-30\\nsusceptible: 0\\ninfectious: 1232\\nremoved: 20485\\ncity: city4', metadata={'source': 'sir.csv', 'row': 389, 'time': '2018-01-30', 'city': 'city4'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 1114\\nremoved: 20603\\ncity: city4', metadata={'source': 'sir.csv', 'row': 390, 'time': '2018-01-31', 'city': 'city4'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1007\\nremoved: 20710\\ncity: city4', metadata={'source': 'sir.csv', 'row': 391, 'time': '2018-02-01', 'city': 'city4'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 910\\nremoved: 20807\\ncity: city4', metadata={'source': 'sir.csv', 'row': 392, 'time': '2018-02-02', 'city': 'city4'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 822\\nremoved: 20895\\ncity: city4', metadata={'source': 'sir.csv', 'row': 393, 'time': '2018-02-03', 'city': 'city4'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 743\\nremoved: 20974\\ncity: city4', metadata={'source': 'sir.csv', 'row': 394, 'time': '2018-02-04', 'city': 'city4'}), Document(page_content=': 35\\ntime: 2018-02-05\\nsusceptible: 0\\ninfectious: 672\\nremoved: 21045\\ncity: city4', metadata={'source': 'sir.csv', 'row': 395, 'time': '2018-02-05', 'city': 'city4'}), Document(page_content=': 36\\ntime: 2018-02-06\\nsusceptible: 0\\ninfectious: 607\\nremoved: 21110\\ncity: city4', metadata={'source': 'sir.csv', 'row': 396, 'time': '2018-02-06', 'city': 'city4'}), Document(page_content=': 37\\ntime: 2018-02-07\\nsusceptible: 0\\ninfectious: 549\\nremoved: 21168\\ncity: city4', metadata={'source': 'sir.csv', 'row': 397, 'time': '2018-02-07', 'city': 'city4'}), Document(page_content=': 38\\ntime: 2018-02-08\\nsusceptible: 0\\ninfectious: 496\\nremoved: 21221\\ncity: city4', metadata={'source': 'sir.csv', 'row': 398, 'time': '2018-02-08', 'city': 'city4'}), Document(page_content=': 39\\ntime: 2018-02-09\\nsusceptible: 0\\ninfectious: 448\\nremoved: 21269\\ncity: city4', metadata={'source': 'sir.csv', 'row': 399, 'time': '2018-02-09', 'city': 'city4'}), Document(page_content=': 40\\ntime: 2018-02-10\\nsusceptible: 0\\ninfectious: 405\\nremoved: 21312\\ncity: city4', metadata={'source': 'sir.csv', 'row': 400, 'time': '2018-02-10', 'city': 'city4'}), Document(page_content=': 41\\ntime: 2018-02-11\\nsusceptible: 0\\ninfectious: 366\\nremoved: 21351\\ncity: city4', metadata={'source': 'sir.csv', 'row': 401, 'time': '2018-02-11', 'city': 'city4'}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 331\\nremoved: 21386\\ncity: city4', metadata={'source': 'sir.csv', 'row': 402, 'time': '2018-02-12', 'city': 'city4'}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 299\\nremoved: 21418\\ncity: city4', metadata={'source': 'sir.csv', 'row': 403, 'time': '2018-02-13', 'city': 'city4'}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 270\\nremoved: 21447\\ncity: city4', metadata={'source': 'sir.csv', 'row': 404, 'time': '2018-02-14', 'city': 'city4'}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 244\\nremoved: 21473\\ncity: city4', metadata={'source': 'sir.csv', 'row': 405, 'time': '2018-02-15', 'city': 'city4'}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 220\\nremoved: 21496\\ncity: city4', metadata={'source': 'sir.csv', 'row': 406, 'time': '2018-02-16', 'city': 'city4'}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 199\\nremoved: 21518\\ncity: city4', metadata={'source': 'sir.csv', 'row': 407, 'time': '2018-02-17', 'city': 'city4'}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 180\\nremoved: 21537\\ncity: city4', metadata={'source': 'sir.csv', 'row': 408, 'time': '2018-02-18', 'city': 'city4'}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 163\\nremoved: 21554\\ncity: city4', metadata={'source': 'sir.csv', 'row': 409, 'time': '2018-02-19', 'city': 'city4'}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 147\\nremoved: 21570\\ncity: city4', metadata={'source': 'sir.csv', 'row': 410, 'time': '2018-02-20', 'city': 'city4'}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 133\\nremoved: 21584\\ncity: city4', metadata={'source': 'sir.csv', 'row': 411, 'time': '2018-02-21', 'city': 'city4'}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 120\\nremoved: 21597\\ncity: city4', metadata={'source': 'sir.csv', 'row': 412, 'time': '2018-02-22', 'city': 'city4'}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 108\\nremoved: 21609\\ncity: city4', metadata={'source': 'sir.csv', 'row': 413, 'time': '2018-02-23', 'city': 'city4'}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 98\\nremoved: 21619\\ncity: city4', metadata={'source': 'sir.csv', 'row': 414, 'time': '2018-02-24', 'city': 'city4'}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 88\\nremoved: 21629\\ncity: city4', metadata={'source': 'sir.csv', 'row': 415, 'time': '2018-02-25', 'city': 'city4'}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 80\\nremoved: 21637\\ncity: city4', metadata={'source': 'sir.csv', 'row': 416, 'time': '2018-02-26', 'city': 'city4'}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 72\\nremoved: 21645\\ncity: city4', metadata={'source': 'sir.csv', 'row': 417, 'time': '2018-02-27', 'city': 'city4'}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 65\\nremoved: 21652\\ncity: city4', metadata={'source': 'sir.csv', 'row': 418, 'time': '2018-02-28', 'city': 'city4'}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 59\\nremoved: 21658\\ncity: city4', metadata={'source': 'sir.csv', 'row': 419, 'time': '2018-03-01', 'city': 'city4'}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 53\\nremoved: 21664\\ncity: city4', metadata={'source': 'sir.csv', 'row': 420, 'time': '2018-03-02', 'city': 'city4'}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 48\\nremoved: 21669\\ncity: city4', metadata={'source': 'sir.csv', 'row': 421, 'time': '2018-03-03', 'city': 'city4'}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 43\\nremoved: 21674\\ncity: city4', metadata={'source': 'sir.csv', 'row': 422, 'time': '2018-03-04', 'city': 'city4'}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 39\\nremoved: 21678\\ncity: city4', metadata={'source': 'sir.csv', 'row': 423, 'time': '2018-03-05', 'city': 'city4'}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 35\\nremoved: 21682\\ncity: city4', metadata={'source': 'sir.csv', 'row': 424, 'time': '2018-03-06', 'city': 'city4'}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 32\\nremoved: 21685\\ncity: city4', metadata={'source': 'sir.csv', 'row': 425, 'time': '2018-03-07', 'city': 'city4'}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 29\\nremoved: 21688\\ncity: city4', metadata={'source': 'sir.csv', 'row': 426, 'time': '2018-03-08', 'city': 'city4'}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 26\\nremoved: 21691\\ncity: city4', metadata={'source': 'sir.csv', 'row': 427, 'time': '2018-03-09', 'city': 'city4'}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 23\\nremoved: 21694\\ncity: city4', metadata={'source': 'sir.csv', 'row': 428, 'time': '2018-03-10', 'city': 'city4'}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 21\\nremoved: 21696\\ncity: city4', metadata={'source': 'sir.csv', 'row': 429, 'time': '2018-03-11', 'city': 'city4'}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 19\\nremoved: 21698\\ncity: city4', metadata={'source': 'sir.csv', 'row': 430, 'time': '2018-03-12', 'city': 'city4'}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 17\\nremoved: 21700\\ncity: city4', metadata={'source': 'sir.csv', 'row': 431, 'time': '2018-03-13', 'city': 'city4'}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 15\\nremoved: 21702\\ncity: city4', metadata={'source': 'sir.csv', 'row': 432, 'time': '2018-03-14', 'city': 'city4'}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 14\\nremoved: 21703\\ncity: city4', metadata={'source': 'sir.csv', 'row': 433, 'time': '2018-03-15', 'city': 'city4'}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 13\\nremoved: 21704\\ncity: city4', metadata={'source': 'sir.csv', 'row': 434, 'time': '2018-03-16', 'city': 'city4'}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 11\\nremoved: 21706\\ncity: city4', metadata={'source': 'sir.csv', 'row': 435, 'time': '2018-03-17', 'city': 'city4'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 10\\nremoved: 21707\\ncity: city4', metadata={'source': 'sir.csv', 'row': 436, 'time': '2018-03-18', 'city': 'city4'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 9\\nremoved: 21708\\ncity: city4', metadata={'source': 'sir.csv', 'row': 437, 'time': '2018-03-19', 'city': 'city4'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 8\\nremoved: 21709\\ncity: city4', metadata={'source': 'sir.csv', 'row': 438, 'time': '2018-03-20', 'city': 'city4'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 7\\nremoved: 21710\\ncity: city4', metadata={'source': 'sir.csv', 'row': 439, 'time': '2018-03-21', 'city': 'city4'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 7\\nremoved: 21710\\ncity: city4', metadata={'source': 'sir.csv', 'row': 440, 'time': '2018-03-22', 'city': 'city4'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 6\\nremoved: 21711\\ncity: city4', metadata={'source': 'sir.csv', 'row': 441, 'time': '2018-03-23', 'city': 'city4'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 5\\nremoved: 21712\\ncity: city4', metadata={'source': 'sir.csv', 'row': 442, 'time': '2018-03-24', 'city': 'city4'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 5\\nremoved: 21712\\ncity: city4', metadata={'source': 'sir.csv', 'row': 443, 'time': '2018-03-25', 'city': 'city4'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 4\\nremoved: 21713\\ncity: city4', metadata={'source': 'sir.csv', 'row': 444, 'time': '2018-03-26', 'city': 'city4'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 4\\nremoved: 21713\\ncity: city4', metadata={'source': 'sir.csv', 'row': 445, 'time': '2018-03-27', 'city': 'city4'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 3\\nremoved: 21714\\ncity: city4', metadata={'source': 'sir.csv', 'row': 446, 'time': '2018-03-28', 'city': 'city4'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 3\\nremoved: 21714\\ncity: city4', metadata={'source': 'sir.csv', 'row': 447, 'time': '2018-03-29', 'city': 'city4'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 3\\nremoved: 21714\\ncity: city4', metadata={'source': 'sir.csv', 'row': 448, 'time': '2018-03-30', 'city': 'city4'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 2\\nremoved: 21715\\ncity: city4', metadata={'source': 'sir.csv', 'row': 449, 'time': '2018-03-31', 'city': 'city4'}), Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 7655\\ninfectious: 7655\\nremoved: 0\\ncity: city5', metadata={'source': 'sir.csv', 'row': 450, 'time': '2018-01-01', 'city': 'city5'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 3481\\ninfectious: 10873\\nremoved: 954\\ncity: city5', metadata={'source': 'sir.csv', 'row': 451, 'time': '2018-01-02', 'city': 'city5'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 1344\\ninfectious: 11843\\nremoved: 2121\\ncity: city5', metadata={'source': 'sir.csv', 'row': 452, 'time': '2018-01-03', 'city': 'city5'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 512\\ninfectious: 11487\\nremoved: 3309\\ncity: city5', metadata={'source': 'sir.csv', 'row': 453, 'time': '2018-01-04', 'city': 'city5'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 205\\ninfectious: 10672\\nremoved: 4432\\ncity: city5', metadata={'source': 'sir.csv', 'row': 454, 'time': '2018-01-05', 'city': 'city5'}), Document(page_content=': 5\\ntime: 2018-01-06\\nsusceptible: 88\\ninfectious: 9756\\nremoved: 5465\\ncity: city5', metadata={'source': 'sir.csv', 'row': 455, 'time': '2018-01-06', 'city': 'city5'}), Document(page_content=': 6\\ntime: 2018-01-07\\nsusceptible: 40\\ninfectious: 8862\\nremoved: 6406\\ncity: city5', metadata={'source': 'sir.csv', 'row': 456, 'time': '2018-01-07', 'city': 'city5'}), Document(page_content=': 7\\ntime: 2018-01-08\\nsusceptible: 20\\ninfectious: 8029\\nremoved: 7260\\ncity: city5', metadata={'source': 'sir.csv', 'row': 457, 'time': '2018-01-08', 'city': 'city5'}), Document(page_content=': 8\\ntime: 2018-01-09\\nsusceptible: 10\\ninfectious: 7266\\nremoved: 8032\\ncity: city5', metadata={'source': 'sir.csv', 'row': 458, 'time': '2018-01-09', 'city': 'city5'}), Document(page_content=': 9\\ntime: 2018-01-10\\nsusceptible: 5\\ninfectious: 6571\\nremoved: 8732\\ncity: city5', metadata={'source': 'sir.csv', 'row': 459, 'time': '2018-01-10', 'city': 'city5'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 3\\ninfectious: 5942\\nremoved: 9364\\ncity: city5', metadata={'source': 'sir.csv', 'row': 460, 'time': '2018-01-11', 'city': 'city5'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 2\\ninfectious: 5371\\nremoved: 9935\\ncity: city5', metadata={'source': 'sir.csv', 'row': 461, 'time': '2018-01-12', 'city': 'city5'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 1\\ninfectious: 4855\\nremoved: 10452\\ncity: city5', metadata={'source': 'sir.csv', 'row': 462, 'time': '2018-01-13', 'city': 'city5'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 0\\ninfectious: 4389\\nremoved: 10919\\ncity: city5', metadata={'source': 'sir.csv', 'row': 463, 'time': '2018-01-14', 'city': 'city5'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 0\\ninfectious: 3967\\nremoved: 11341\\ncity: city5', metadata={'source': 'sir.csv', 'row': 464, 'time': '2018-01-15', 'city': 'city5'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 0\\ninfectious: 3586\\nremoved: 11723\\ncity: city5', metadata={'source': 'sir.csv', 'row': 465, 'time': '2018-01-16', 'city': 'city5'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 3241\\nremoved: 12068\\ncity: city5', metadata={'source': 'sir.csv', 'row': 466, 'time': '2018-01-17', 'city': 'city5'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 2929\\nremoved: 12380\\ncity: city5', metadata={'source': 'sir.csv', 'row': 467, 'time': '2018-01-18', 'city': 'city5'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 2647\\nremoved: 12661\\ncity: city5', metadata={'source': 'sir.csv', 'row': 468, 'time': '2018-01-19', 'city': 'city5'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 2393\\nremoved: 12916\\ncity: city5', metadata={'source': 'sir.csv', 'row': 469, 'time': '2018-01-20', 'city': 'city5'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 2163\\nremoved: 13146\\ncity: city5', metadata={'source': 'sir.csv', 'row': 470, 'time': '2018-01-21', 'city': 'city5'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 1955\\nremoved: 13354\\ncity: city5', metadata={'source': 'sir.csv', 'row': 471, 'time': '2018-01-22', 'city': 'city5'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 1767\\nremoved: 13542\\ncity: city5', metadata={'source': 'sir.csv', 'row': 472, 'time': '2018-01-23', 'city': 'city5'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 1597\\nremoved: 13712\\ncity: city5', metadata={'source': 'sir.csv', 'row': 473, 'time': '2018-01-24', 'city': 'city5'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 1443\\nremoved: 13866\\ncity: city5', metadata={'source': 'sir.csv', 'row': 474, 'time': '2018-01-25', 'city': 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'city5'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 786\\nremoved: 14523\\ncity: city5', metadata={'source': 'sir.csv', 'row': 480, 'time': '2018-01-31', 'city': 'city5'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 711\\nremoved: 14598\\ncity: city5', metadata={'source': 'sir.csv', 'row': 481, 'time': '2018-02-01', 'city': 'city5'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 642\\nremoved: 14667\\ncity: city5', metadata={'source': 'sir.csv', 'row': 482, 'time': '2018-02-02', 'city': 'city5'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 581\\nremoved: 14728\\ncity: city5', metadata={'source': 'sir.csv', 'row': 483, 'time': '2018-02-03', 'city': 'city5'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 525\\nremoved: 14784\\ncity: city5', metadata={'source': 'sir.csv', 'row': 484, 'time': '2018-02-04', 'city': 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Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 8\\nremoved: 15301\\ncity: city5', metadata={'source': 'sir.csv', 'row': 525, 'time': '2018-03-17', 'city': 'city5'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 7\\nremoved: 15302\\ncity: city5', metadata={'source': 'sir.csv', 'row': 526, 'time': '2018-03-18', 'city': 'city5'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 6\\nremoved: 15303\\ncity: city5', metadata={'source': 'sir.csv', 'row': 527, 'time': '2018-03-19', 'city': 'city5'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 6\\nremoved: 15303\\ncity: city5', metadata={'source': 'sir.csv', 'row': 528, 'time': '2018-03-20', 'city': 'city5'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 5\\nremoved: 15304\\ncity: city5', metadata={'source': 'sir.csv', 'row': 529, 'time': '2018-03-21', 'city': 'city5'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 5\\nremoved: 15304\\ncity: city5', metadata={'source': 'sir.csv', 'row': 530, 'time': '2018-03-22', 'city': 'city5'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 4\\nremoved: 15305\\ncity: city5', metadata={'source': 'sir.csv', 'row': 531, 'time': '2018-03-23', 'city': 'city5'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 4\\nremoved: 15305\\ncity: city5', metadata={'source': 'sir.csv', 'row': 532, 'time': '2018-03-24', 'city': 'city5'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 3\\nremoved: 15306\\ncity: city5', metadata={'source': 'sir.csv', 'row': 533, 'time': '2018-03-25', 'city': 'city5'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 3\\nremoved: 15306\\ncity: city5', metadata={'source': 'sir.csv', 'row': 534, 'time': '2018-03-26', 'city': 'city5'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 3\\nremoved: 15306\\ncity: city5', metadata={'source': 'sir.csv', 'row': 535, 'time': '2018-03-27', 'city': 'city5'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 2\\nremoved: 15307\\ncity: city5', metadata={'source': 'sir.csv', 'row': 536, 'time': '2018-03-28', 'city': 'city5'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 2\\nremoved: 15307\\ncity: city5', metadata={'source': 'sir.csv', 'row': 537, 'time': '2018-03-29', 'city': 'city5'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 2\\nremoved: 15307\\ncity: city5', metadata={'source': 'sir.csv', 'row': 538, 'time': '2018-03-30', 'city': 'city5'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 2\\nremoved: 15307\\ncity: city5', metadata={'source': 'sir.csv', 'row': 539, 'time': '2018-03-31', 'city': 'city5'}), Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 17869\\ninfectious: 17869\\nremoved: 0\\ncity: city6', metadata={'source': 'sir.csv', 'row': 540, 'time': '2018-01-01', 'city': 'city6'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 8081\\ninfectious: 25424\\nremoved: 2231\\ncity: city6', metadata={'source': 'sir.csv', 'row': 541, 'time': '2018-01-02', 'city': 'city6'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 3101\\ninfectious: 27676\\nremoved: 4959\\ncity: city6', metadata={'source': 'sir.csv', 'row': 542, 'time': '2018-01-03', 'city': 'city6'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 1175\\ninfectious: 26828\\nremoved: 7734\\ncity: city6', metadata={'source': 'sir.csv', 'row': 543, 'time': '2018-01-04', 'city': 'city6'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 467\\ninfectious: 24914\\nremoved: 10356\\ncity: city6', metadata={'source': 'sir.csv', 'row': 544, 'time': '2018-01-05', 'city': 'city6'}), Document(page_content=': 5\\ntime: 2018-01-06\\nsusceptible: 199\\ninfectious: 22770\\nremoved: 12767\\ncity: city6', metadata={'source': 'sir.csv', 'row': 545, 'time': '2018-01-06', 'city': 'city6'}), Document(page_content=': 6\\ntime: 2018-01-07\\nsusceptible: 91\\ninfectious: 20682\\nremoved: 14963\\ncity: city6', metadata={'source': 'sir.csv', 'row': 546, 'time': '2018-01-07', 'city': 'city6'}), Document(page_content=': 7\\ntime: 2018-01-08\\nsusceptible: 45\\ninfectious: 18737\\nremoved: 16955\\ncity: city6', metadata={'source': 'sir.csv', 'row': 547, 'time': '2018-01-08', 'city': 'city6'}), Document(page_content=': 8\\ntime: 2018-01-09\\nsusceptible: 23\\ninfectious: 16955\\nremoved: 18758\\ncity: city6', metadata={'source': 'sir.csv', 'row': 548, 'time': '2018-01-09', 'city': 'city6'}), Document(page_content=': 9\\ntime: 2018-01-10\\nsusceptible: 13\\ninfectious: 15334\\nremoved: 20390\\ncity: city6', metadata={'source': 'sir.csv', 'row': 549, 'time': '2018-01-10', 'city': 'city6'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 7\\ninfectious: 13864\\nremoved: 21865\\ncity: city6', metadata={'source': 'sir.csv', 'row': 550, 'time': '2018-01-11', 'city': 'city6'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 4\\ninfectious: 12534\\nremoved: 23199\\ncity: city6', metadata={'source': 'sir.csv', 'row': 551, 'time': '2018-01-12', 'city': 'city6'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 3\\ninfectious: 11330\\nremoved: 24404\\ncity: city6', metadata={'source': 'sir.csv', 'row': 552, 'time': '2018-01-13', 'city': 'city6'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 2\\ninfectious: 10241\\nremoved: 25494\\ncity: city6', metadata={'source': 'sir.csv', 'row': 553, 'time': '2018-01-14', 'city': 'city6'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 1\\ninfectious: 9257\\nremoved: 26479\\ncity: city6', metadata={'source': 'sir.csv', 'row': 554, 'time': '2018-01-15', 'city': 'city6'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 1\\ninfectious: 8367\\nremoved: 27369\\ncity: city6', metadata={'source': 'sir.csv', 'row': 555, 'time': '2018-01-16', 'city': 'city6'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 0\\ninfectious: 7562\\nremoved: 28174\\ncity: city6', metadata={'source': 'sir.csv', 'row': 556, 'time': '2018-01-17', 'city': 'city6'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 6835\\nremoved: 28901\\ncity: city6', metadata={'source': 'sir.csv', 'row': 557, 'time': '2018-01-18', 'city': 'city6'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 6178\\nremoved: 29559\\ncity: city6', metadata={'source': 'sir.csv', 'row': 558, 'time': '2018-01-19', 'city': 'city6'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 5584\\nremoved: 30153\\ncity: city6', metadata={'source': 'sir.csv', 'row': 559, 'time': '2018-01-20', 'city': 'city6'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 5047\\nremoved: 30690\\ncity: city6', metadata={'source': 'sir.csv', 'row': 560, 'time': '2018-01-21', 'city': 'city6'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 4561\\nremoved: 31176\\ncity: city6', metadata={'source': 'sir.csv', 'row': 561, 'time': '2018-01-22', 'city': 'city6'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 4122\\nremoved: 31614\\ncity: city6', metadata={'source': 'sir.csv', 'row': 562, 'time': '2018-01-23', 'city': 'city6'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 3726\\nremoved: 32011\\ncity: city6', metadata={'source': 'sir.csv', 'row': 563, 'time': '2018-01-24', 'city': 'city6'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 3368\\nremoved: 32369\\ncity: city6', metadata={'source': 'sir.csv', 'row': 564, 'time': '2018-01-25', 'city': 'city6'}), Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 3044\\nremoved: 32693\\ncity: city6', metadata={'source': 'sir.csv', 'row': 565, 'time': '2018-01-26', 'city': 'city6'}), Document(page_content=': 26\\ntime: 2018-01-27\\nsusceptible: 0\\ninfectious: 2751\\nremoved: 32986\\ncity: city6', metadata={'source': 'sir.csv', 'row': 566, 'time': '2018-01-27', 'city': 'city6'}), Document(page_content=': 27\\ntime: 2018-01-28\\nsusceptible: 0\\ninfectious: 2486\\nremoved: 33251\\ncity: city6', metadata={'source': 'sir.csv', 'row': 567, 'time': '2018-01-28', 'city': 'city6'}), Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 2247\\nremoved: 33490\\ncity: city6', metadata={'source': 'sir.csv', 'row': 568, 'time': '2018-01-29', 'city': 'city6'}), Document(page_content=': 29\\ntime: 2018-01-30\\nsusceptible: 0\\ninfectious: 2031\\nremoved: 33706\\ncity: city6', metadata={'source': 'sir.csv', 'row': 569, 'time': '2018-01-30', 'city': 'city6'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 1836\\nremoved: 33901\\ncity: city6', metadata={'source': 'sir.csv', 'row': 570, 'time': '2018-01-31', 'city': 'city6'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1659\\nremoved: 34078\\ncity: city6', metadata={'source': 'sir.csv', 'row': 571, 'time': '2018-02-01', 'city': 'city6'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 1499\\nremoved: 34238\\ncity: city6', metadata={'source': 'sir.csv', 'row': 572, 'time': '2018-02-02', 'city': 'city6'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1355\\nremoved: 34382\\ncity: city6', metadata={'source': 'sir.csv', 'row': 573, 'time': '2018-02-03', 'city': 'city6'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 1225\\nremoved: 34512\\ncity: city6', metadata={'source': 'sir.csv', 'row': 574, 'time': '2018-02-04', 'city': 'city6'}), Document(page_content=': 35\\ntime: 2018-02-05\\nsusceptible: 0\\ninfectious: 1107\\nremoved: 34630\\ncity: city6', metadata={'source': 'sir.csv', 'row': 575, 'time': '2018-02-05', 'city': 'city6'}), Document(page_content=': 36\\ntime: 2018-02-06\\nsusceptible: 0\\ninfectious: 1000\\nremoved: 34737\\ncity: city6', metadata={'source': 'sir.csv', 'row': 576, 'time': '2018-02-06', 'city': 'city6'}), Document(page_content=': 37\\ntime: 2018-02-07\\nsusceptible: 0\\ninfectious: 904\\nremoved: 34833\\ncity: city6', metadata={'source': 'sir.csv', 'row': 577, 'time': '2018-02-07', 'city': 'city6'}), Document(page_content=': 38\\ntime: 2018-02-08\\nsusceptible: 0\\ninfectious: 817\\nremoved: 34920\\ncity: city6', metadata={'source': 'sir.csv', 'row': 578, 'time': '2018-02-08', 'city': 'city6'}), Document(page_content=': 39\\ntime: 2018-02-09\\nsusceptible: 0\\ninfectious: 739\\nremoved: 34998\\ncity: city6', metadata={'source': 'sir.csv', 'row': 579, 'time': '2018-02-09', 'city': 'city6'}), Document(page_content=': 40\\ntime: 2018-02-10\\nsusceptible: 0\\ninfectious: 667\\nremoved: 35069\\ncity: city6', metadata={'source': 'sir.csv', 'row': 580, 'time': '2018-02-10', 'city': 'city6'}), Document(page_content=': 41\\ntime: 2018-02-11\\nsusceptible: 0\\ninfectious: 603\\nremoved: 35134\\ncity: city6', metadata={'source': 'sir.csv', 'row': 581, 'time': '2018-02-11', 'city': 'city6'}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 545\\nremoved: 35192\\ncity: city6', metadata={'source': 'sir.csv', 'row': 582, 'time': '2018-02-12', 'city': 'city6'}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 493\\nremoved: 35244\\ncity: city6', metadata={'source': 'sir.csv', 'row': 583, 'time': '2018-02-13', 'city': 'city6'}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 445\\nremoved: 35292\\ncity: city6', metadata={'source': 'sir.csv', 'row': 584, 'time': '2018-02-14', 'city': 'city6'}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 402\\nremoved: 35335\\ncity: city6', metadata={'source': 'sir.csv', 'row': 585, 'time': '2018-02-15', 'city': 'city6'}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 364\\nremoved: 35373\\ncity: city6', metadata={'source': 'sir.csv', 'row': 586, 'time': '2018-02-16', 'city': 'city6'}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 329\\nremoved: 35408\\ncity: city6', metadata={'source': 'sir.csv', 'row': 587, 'time': '2018-02-17', 'city': 'city6'}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 297\\nremoved: 35440\\ncity: city6', metadata={'source': 'sir.csv', 'row': 588, 'time': '2018-02-18', 'city': 'city6'}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 268\\nremoved: 35469\\ncity: city6', metadata={'source': 'sir.csv', 'row': 589, 'time': '2018-02-19', 'city': 'city6'}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 242\\nremoved: 35494\\ncity: city6', metadata={'source': 'sir.csv', 'row': 590, 'time': '2018-02-20', 'city': 'city6'}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 219\\nremoved: 35518\\ncity: city6', metadata={'source': 'sir.csv', 'row': 591, 'time': '2018-02-21', 'city': 'city6'}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 198\\nremoved: 35539\\ncity: city6', metadata={'source': 'sir.csv', 'row': 592, 'time': '2018-02-22', 'city': 'city6'}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 179\\nremoved: 35558\\ncity: city6', metadata={'source': 'sir.csv', 'row': 593, 'time': '2018-02-23', 'city': 'city6'}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 162\\nremoved: 35575\\ncity: city6', metadata={'source': 'sir.csv', 'row': 594, 'time': '2018-02-24', 'city': 'city6'}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 146\\nremoved: 35591\\ncity: city6', metadata={'source': 'sir.csv', 'row': 595, 'time': '2018-02-25', 'city': 'city6'}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 132\\nremoved: 35605\\ncity: city6', metadata={'source': 'sir.csv', 'row': 596, 'time': '2018-02-26', 'city': 'city6'}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 119\\nremoved: 35618\\ncity: city6', metadata={'source': 'sir.csv', 'row': 597, 'time': '2018-02-27', 'city': 'city6'}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 108\\nremoved: 35629\\ncity: city6', metadata={'source': 'sir.csv', 'row': 598, 'time': '2018-02-28', 'city': 'city6'}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 97\\nremoved: 35640\\ncity: city6', metadata={'source': 'sir.csv', 'row': 599, 'time': '2018-03-01', 'city': 'city6'}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 88\\nremoved: 35649\\ncity: city6', metadata={'source': 'sir.csv', 'row': 600, 'time': '2018-03-02', 'city': 'city6'}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 79\\nremoved: 35658\\ncity: city6', metadata={'source': 'sir.csv', 'row': 601, 'time': '2018-03-03', 'city': 'city6'}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 72\\nremoved: 35665\\ncity: city6', metadata={'source': 'sir.csv', 'row': 602, 'time': '2018-03-04', 'city': 'city6'}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 65\\nremoved: 35672\\ncity: city6', metadata={'source': 'sir.csv', 'row': 603, 'time': '2018-03-05', 'city': 'city6'}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 58\\nremoved: 35678\\ncity: city6', metadata={'source': 'sir.csv', 'row': 604, 'time': '2018-03-06', 'city': 'city6'}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 53\\nremoved: 35684\\ncity: city6', metadata={'source': 'sir.csv', 'row': 605, 'time': '2018-03-07', 'city': 'city6'}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 48\\nremoved: 35689\\ncity: city6', metadata={'source': 'sir.csv', 'row': 606, 'time': '2018-03-08', 'city': 'city6'}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 43\\nremoved: 35694\\ncity: city6', metadata={'source': 'sir.csv', 'row': 607, 'time': '2018-03-09', 'city': 'city6'}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 39\\nremoved: 35698\\ncity: city6', metadata={'source': 'sir.csv', 'row': 608, 'time': '2018-03-10', 'city': 'city6'}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 35\\nremoved: 35702\\ncity: city6', metadata={'source': 'sir.csv', 'row': 609, 'time': '2018-03-11', 'city': 'city6'}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 32\\nremoved: 35705\\ncity: city6', metadata={'source': 'sir.csv', 'row': 610, 'time': '2018-03-12', 'city': 'city6'}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 29\\nremoved: 35708\\ncity: city6', metadata={'source': 'sir.csv', 'row': 611, 'time': '2018-03-13', 'city': 'city6'}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 26\\nremoved: 35711\\ncity: city6', metadata={'source': 'sir.csv', 'row': 612, 'time': '2018-03-14', 'city': 'city6'}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 23\\nremoved: 35714\\ncity: city6', metadata={'source': 'sir.csv', 'row': 613, 'time': '2018-03-15', 'city': 'city6'}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 21\\nremoved: 35716\\ncity: city6', metadata={'source': 'sir.csv', 'row': 614, 'time': '2018-03-16', 'city': 'city6'}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 19\\nremoved: 35718\\ncity: city6', metadata={'source': 'sir.csv', 'row': 615, 'time': '2018-03-17', 'city': 'city6'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 17\\nremoved: 35720\\ncity: city6', metadata={'source': 'sir.csv', 'row': 616, 'time': '2018-03-18', 'city': 'city6'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 15\\nremoved: 35722\\ncity: city6', metadata={'source': 'sir.csv', 'row': 617, 'time': '2018-03-19', 'city': 'city6'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 14\\nremoved: 35723\\ncity: city6', metadata={'source': 'sir.csv', 'row': 618, 'time': '2018-03-20', 'city': 'city6'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 12\\nremoved: 35725\\ncity: city6', metadata={'source': 'sir.csv', 'row': 619, 'time': '2018-03-21', 'city': 'city6'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 11\\nremoved: 35726\\ncity: city6', metadata={'source': 'sir.csv', 'row': 620, 'time': '2018-03-22', 'city': 'city6'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 10\\nremoved: 35727\\ncity: city6', metadata={'source': 'sir.csv', 'row': 621, 'time': '2018-03-23', 'city': 'city6'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 9\\nremoved: 35728\\ncity: city6', metadata={'source': 'sir.csv', 'row': 622, 'time': '2018-03-24', 'city': 'city6'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 8\\nremoved: 35729\\ncity: city6', metadata={'source': 'sir.csv', 'row': 623, 'time': '2018-03-25', 'city': 'city6'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 7\\nremoved: 35730\\ncity: city6', metadata={'source': 'sir.csv', 'row': 624, 'time': '2018-03-26', 'city': 'city6'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 7\\nremoved: 35730\\ncity: city6', metadata={'source': 'sir.csv', 'row': 625, 'time': '2018-03-27', 'city': 'city6'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 6\\nremoved: 35731\\ncity: city6', metadata={'source': 'sir.csv', 'row': 626, 'time': '2018-03-28', 'city': 'city6'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 5\\nremoved: 35732\\ncity: city6', metadata={'source': 'sir.csv', 'row': 627, 'time': '2018-03-29', 'city': 'city6'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 5\\nremoved: 35732\\ncity: city6', metadata={'source': 'sir.csv', 'row': 628, 'time': '2018-03-30', 'city': 'city6'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 4\\nremoved: 35733\\ncity: city6', metadata={'source': 'sir.csv', 'row': 629, 'time': '2018-03-31', 'city': 'city6'}), Document(page_content=': 0\\ntime: 2018-01-01\\nsusceptible: 4272\\ninfectious: 4272\\nremoved: 0\\ncity: city7', metadata={'source': 'sir.csv', 'row': 630, 'time': '2018-01-01', 'city': 'city7'}), Document(page_content=': 1\\ntime: 2018-01-02\\nsusceptible: 2275\\ninfectious: 5755\\nremoved: 513\\ncity: city7', metadata={'source': 'sir.csv', 'row': 631, 'time': '2018-01-02', 'city': 'city7'}), Document(page_content=': 2\\ntime: 2018-01-03\\nsusceptible: 1067\\ninfectious: 6343\\nremoved: 1133\\ncity: city7', metadata={'source': 'sir.csv', 'row': 632, 'time': '2018-01-03', 'city': 'city7'}), Document(page_content=': 3\\ntime: 2018-01-04\\nsusceptible: 487\\ninfectious: 6280\\nremoved: 1775\\ncity: city7', metadata={'source': 'sir.csv', 'row': 633, 'time': '2018-01-04', 'city': 'city7'}), Document(page_content=': 4\\ntime: 2018-01-05\\nsusceptible: 229\\ninfectious: 5920\\nremoved: 2394\\ncity: city7', metadata={'source': 'sir.csv', 'row': 634, 'time': '2018-01-05', 'city': 'city7'}), Document(page_content=': 5\\ntime: 2018-01-06\\nsusceptible: 113\\ninfectious: 5460\\nremoved: 2969\\ncity: city7', metadata={'source': 'sir.csv', 'row': 635, 'time': '2018-01-06', 'city': 'city7'}), Document(page_content=': 6\\ntime: 2018-01-07\\nsusceptible: 59\\ninfectious: 4986\\nremoved: 3498\\ncity: city7', metadata={'source': 'sir.csv', 'row': 636, 'time': '2018-01-07', 'city': 'city7'}), Document(page_content=': 7\\ntime: 2018-01-08\\nsusceptible: 32\\ninfectious: 4531\\nremoved: 3979\\ncity: city7', metadata={'source': 'sir.csv', 'row': 637, 'time': '2018-01-08', 'city': 'city7'}), Document(page_content=': 8\\ntime: 2018-01-09\\nsusceptible: 19\\ninfectious: 4108\\nremoved: 4415\\ncity: city7', metadata={'source': 'sir.csv', 'row': 638, 'time': '2018-01-09', 'city': 'city7'}), Document(page_content=': 9\\ntime: 2018-01-10\\nsusceptible: 11\\ninfectious: 3720\\nremoved: 4811\\ncity: city7', metadata={'source': 'sir.csv', 'row': 639, 'time': '2018-01-10', 'city': 'city7'}), Document(page_content=': 10\\ntime: 2018-01-11\\nsusceptible: 7\\ninfectious: 3366\\nremoved: 5169\\ncity: city7', metadata={'source': 'sir.csv', 'row': 640, 'time': '2018-01-11', 'city': 'city7'}), Document(page_content=': 11\\ntime: 2018-01-12\\nsusceptible: 5\\ninfectious: 3045\\nremoved: 5493\\ncity: city7', metadata={'source': 'sir.csv', 'row': 641, 'time': '2018-01-12', 'city': 'city7'}), Document(page_content=': 12\\ntime: 2018-01-13\\nsusceptible: 3\\ninfectious: 2753\\nremoved: 5786\\ncity: city7', metadata={'source': 'sir.csv', 'row': 642, 'time': '2018-01-13', 'city': 'city7'}), Document(page_content=': 13\\ntime: 2018-01-14\\nsusceptible: 2\\ninfectious: 2490\\nremoved: 6051\\ncity: city7', metadata={'source': 'sir.csv', 'row': 643, 'time': '2018-01-14', 'city': 'city7'}), Document(page_content=': 14\\ntime: 2018-01-15\\nsusceptible: 1\\ninfectious: 2251\\nremoved: 6290\\ncity: city7', metadata={'source': 'sir.csv', 'row': 644, 'time': '2018-01-15', 'city': 'city7'}), Document(page_content=': 15\\ntime: 2018-01-16\\nsusceptible: 1\\ninfectious: 2035\\nremoved: 6507\\ncity: city7', metadata={'source': 'sir.csv', 'row': 645, 'time': '2018-01-16', 'city': 'city7'}), Document(page_content=': 16\\ntime: 2018-01-17\\nsusceptible: 1\\ninfectious: 1839\\nremoved: 6703\\ncity: city7', metadata={'source': 'sir.csv', 'row': 646, 'time': '2018-01-17', 'city': 'city7'}), Document(page_content=': 17\\ntime: 2018-01-18\\nsusceptible: 0\\ninfectious: 1662\\nremoved: 6880\\ncity: city7', metadata={'source': 'sir.csv', 'row': 647, 'time': '2018-01-18', 'city': 'city7'}), Document(page_content=': 18\\ntime: 2018-01-19\\nsusceptible: 0\\ninfectious: 1503\\nremoved: 7040\\ncity: city7', metadata={'source': 'sir.csv', 'row': 648, 'time': '2018-01-19', 'city': 'city7'}), Document(page_content=': 19\\ntime: 2018-01-20\\nsusceptible: 0\\ninfectious: 1358\\nremoved: 7184\\ncity: city7', metadata={'source': 'sir.csv', 'row': 649, 'time': '2018-01-20', 'city': 'city7'}), Document(page_content=': 20\\ntime: 2018-01-21\\nsusceptible: 0\\ninfectious: 1228\\nremoved: 7315\\ncity: city7', metadata={'source': 'sir.csv', 'row': 650, 'time': '2018-01-21', 'city': 'city7'}), Document(page_content=': 21\\ntime: 2018-01-22\\nsusceptible: 0\\ninfectious: 1110\\nremoved: 7433\\ncity: city7', metadata={'source': 'sir.csv', 'row': 651, 'time': '2018-01-22', 'city': 'city7'}), Document(page_content=': 22\\ntime: 2018-01-23\\nsusceptible: 0\\ninfectious: 1003\\nremoved: 7540\\ncity: city7', metadata={'source': 'sir.csv', 'row': 652, 'time': '2018-01-23', 'city': 'city7'}), Document(page_content=': 23\\ntime: 2018-01-24\\nsusceptible: 0\\ninfectious: 906\\nremoved: 7636\\ncity: city7', metadata={'source': 'sir.csv', 'row': 653, 'time': '2018-01-24', 'city': 'city7'}), Document(page_content=': 24\\ntime: 2018-01-25\\nsusceptible: 0\\ninfectious: 819\\nremoved: 7723\\ncity: city7', metadata={'source': 'sir.csv', 'row': 654, 'time': '2018-01-25', 'city': 'city7'}), Document(page_content=': 25\\ntime: 2018-01-26\\nsusceptible: 0\\ninfectious: 740\\nremoved: 7802\\ncity: city7', metadata={'source': 'sir.csv', 'row': 655, 'time': '2018-01-26', 'city': 'city7'}), Document(page_content=': 26\\ntime: 2018-01-27\\nsusceptible: 0\\ninfectious: 669\\nremoved: 7874\\ncity: city7', metadata={'source': 'sir.csv', 'row': 656, 'time': '2018-01-27', 'city': 'city7'}), Document(page_content=': 27\\ntime: 2018-01-28\\nsusceptible: 0\\ninfectious: 605\\nremoved: 7938\\ncity: city7', metadata={'source': 'sir.csv', 'row': 657, 'time': '2018-01-28', 'city': 'city7'}), Document(page_content=': 28\\ntime: 2018-01-29\\nsusceptible: 0\\ninfectious: 547\\nremoved: 7996\\ncity: city7', metadata={'source': 'sir.csv', 'row': 658, 'time': '2018-01-29', 'city': 'city7'}), Document(page_content=': 29\\ntime: 2018-01-30\\nsusceptible: 0\\ninfectious: 494\\nremoved: 8049\\ncity: city7', metadata={'source': 'sir.csv', 'row': 659, 'time': '2018-01-30', 'city': 'city7'}), 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'city': 'city9'}), Document(page_content=': 30\\ntime: 2018-01-31\\nsusceptible: 0\\ninfectious: 1393\\nremoved: 25666\\ncity: city9', metadata={'source': 'sir.csv', 'row': 840, 'time': '2018-01-31', 'city': 'city9'}), Document(page_content=': 31\\ntime: 2018-02-01\\nsusceptible: 0\\ninfectious: 1259\\nremoved: 25800\\ncity: city9', metadata={'source': 'sir.csv', 'row': 841, 'time': '2018-02-01', 'city': 'city9'}), Document(page_content=': 32\\ntime: 2018-02-02\\nsusceptible: 0\\ninfectious: 1138\\nremoved: 25921\\ncity: city9', metadata={'source': 'sir.csv', 'row': 842, 'time': '2018-02-02', 'city': 'city9'}), Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1028\\nremoved: 26030\\ncity: city9', metadata={'source': 'sir.csv', 'row': 843, 'time': '2018-02-03', 'city': 'city9'}), Document(page_content=': 34\\ntime: 2018-02-04\\nsusceptible: 0\\ninfectious: 929\\nremoved: 26129\\ncity: city9', metadata={'source': 'sir.csv', 'row': 844, 'time': '2018-02-04', 'city': 'city9'}), Document(page_content=': 35\\ntime: 2018-02-05\\nsusceptible: 0\\ninfectious: 840\\nremoved: 26219\\ncity: city9', metadata={'source': 'sir.csv', 'row': 845, 'time': '2018-02-05', 'city': 'city9'}), Document(page_content=': 36\\ntime: 2018-02-06\\nsusceptible: 0\\ninfectious: 759\\nremoved: 26300\\ncity: city9', metadata={'source': 'sir.csv', 'row': 846, 'time': '2018-02-06', 'city': 'city9'}), Document(page_content=': 37\\ntime: 2018-02-07\\nsusceptible: 0\\ninfectious: 686\\nremoved: 26373\\ncity: city9', metadata={'source': 'sir.csv', 'row': 847, 'time': '2018-02-07', 'city': 'city9'}), Document(page_content=': 38\\ntime: 2018-02-08\\nsusceptible: 0\\ninfectious: 620\\nremoved: 26439\\ncity: city9', metadata={'source': 'sir.csv', 'row': 848, 'time': '2018-02-08', 'city': 'city9'}), Document(page_content=': 39\\ntime: 2018-02-09\\nsusceptible: 0\\ninfectious: 560\\nremoved: 26499\\ncity: city9', metadata={'source': 'sir.csv', 'row': 849, 'time': '2018-02-09', 'city': 'city9'}), Document(page_content=': 40\\ntime: 2018-02-10\\nsusceptible: 0\\ninfectious: 506\\nremoved: 26552\\ncity: city9', metadata={'source': 'sir.csv', 'row': 850, 'time': '2018-02-10', 'city': 'city9'}), Document(page_content=': 41\\ntime: 2018-02-11\\nsusceptible: 0\\ninfectious: 458\\nremoved: 26601\\ncity: city9', metadata={'source': 'sir.csv', 'row': 851, 'time': '2018-02-11', 'city': 'city9'}), Document(page_content=': 42\\ntime: 2018-02-12\\nsusceptible: 0\\ninfectious: 414\\nremoved: 26645\\ncity: city9', metadata={'source': 'sir.csv', 'row': 852, 'time': '2018-02-12', 'city': 'city9'}), Document(page_content=': 43\\ntime: 2018-02-13\\nsusceptible: 0\\ninfectious: 374\\nremoved: 26685\\ncity: city9', metadata={'source': 'sir.csv', 'row': 853, 'time': '2018-02-13', 'city': 'city9'}), Document(page_content=': 44\\ntime: 2018-02-14\\nsusceptible: 0\\ninfectious: 338\\nremoved: 26721\\ncity: city9', metadata={'source': 'sir.csv', 'row': 854, 'time': '2018-02-14', 'city': 'city9'}), Document(page_content=': 45\\ntime: 2018-02-15\\nsusceptible: 0\\ninfectious: 305\\nremoved: 26754\\ncity: city9', metadata={'source': 'sir.csv', 'row': 855, 'time': '2018-02-15', 'city': 'city9'}), Document(page_content=': 46\\ntime: 2018-02-16\\nsusceptible: 0\\ninfectious: 276\\nremoved: 26783\\ncity: city9', metadata={'source': 'sir.csv', 'row': 856, 'time': '2018-02-16', 'city': 'city9'}), Document(page_content=': 47\\ntime: 2018-02-17\\nsusceptible: 0\\ninfectious: 249\\nremoved: 26810\\ncity: city9', metadata={'source': 'sir.csv', 'row': 857, 'time': '2018-02-17', 'city': 'city9'}), Document(page_content=': 48\\ntime: 2018-02-18\\nsusceptible: 0\\ninfectious: 225\\nremoved: 26834\\ncity: city9', metadata={'source': 'sir.csv', 'row': 858, 'time': '2018-02-18', 'city': 'city9'}), Document(page_content=': 49\\ntime: 2018-02-19\\nsusceptible: 0\\ninfectious: 204\\nremoved: 26855\\ncity: city9', metadata={'source': 'sir.csv', 'row': 859, 'time': '2018-02-19', 'city': 'city9'}), Document(page_content=': 50\\ntime: 2018-02-20\\nsusceptible: 0\\ninfectious: 184\\nremoved: 26875\\ncity: city9', metadata={'source': 'sir.csv', 'row': 860, 'time': '2018-02-20', 'city': 'city9'}), Document(page_content=': 51\\ntime: 2018-02-21\\nsusceptible: 0\\ninfectious: 166\\nremoved: 26893\\ncity: city9', metadata={'source': 'sir.csv', 'row': 861, 'time': '2018-02-21', 'city': 'city9'}), Document(page_content=': 52\\ntime: 2018-02-22\\nsusceptible: 0\\ninfectious: 150\\nremoved: 26909\\ncity: city9', metadata={'source': 'sir.csv', 'row': 862, 'time': '2018-02-22', 'city': 'city9'}), Document(page_content=': 53\\ntime: 2018-02-23\\nsusceptible: 0\\ninfectious: 136\\nremoved: 26923\\ncity: city9', metadata={'source': 'sir.csv', 'row': 863, 'time': '2018-02-23', 'city': 'city9'}), Document(page_content=': 54\\ntime: 2018-02-24\\nsusceptible: 0\\ninfectious: 123\\nremoved: 26936\\ncity: city9', metadata={'source': 'sir.csv', 'row': 864, 'time': '2018-02-24', 'city': 'city9'}), Document(page_content=': 55\\ntime: 2018-02-25\\nsusceptible: 0\\ninfectious: 111\\nremoved: 26948\\ncity: city9', metadata={'source': 'sir.csv', 'row': 865, 'time': '2018-02-25', 'city': 'city9'}), Document(page_content=': 56\\ntime: 2018-02-26\\nsusceptible: 0\\ninfectious: 100\\nremoved: 26959\\ncity: city9', metadata={'source': 'sir.csv', 'row': 866, 'time': '2018-02-26', 'city': 'city9'}), Document(page_content=': 57\\ntime: 2018-02-27\\nsusceptible: 0\\ninfectious: 90\\nremoved: 26969\\ncity: city9', metadata={'source': 'sir.csv', 'row': 867, 'time': '2018-02-27', 'city': 'city9'}), Document(page_content=': 58\\ntime: 2018-02-28\\nsusceptible: 0\\ninfectious: 82\\nremoved: 26977\\ncity: city9', metadata={'source': 'sir.csv', 'row': 868, 'time': '2018-02-28', 'city': 'city9'}), Document(page_content=': 59\\ntime: 2018-03-01\\nsusceptible: 0\\ninfectious: 74\\nremoved: 26985\\ncity: city9', metadata={'source': 'sir.csv', 'row': 869, 'time': '2018-03-01', 'city': 'city9'}), Document(page_content=': 60\\ntime: 2018-03-02\\nsusceptible: 0\\ninfectious: 67\\nremoved: 26992\\ncity: city9', metadata={'source': 'sir.csv', 'row': 870, 'time': '2018-03-02', 'city': 'city9'}), Document(page_content=': 61\\ntime: 2018-03-03\\nsusceptible: 0\\ninfectious: 60\\nremoved: 26999\\ncity: city9', metadata={'source': 'sir.csv', 'row': 871, 'time': '2018-03-03', 'city': 'city9'}), Document(page_content=': 62\\ntime: 2018-03-04\\nsusceptible: 0\\ninfectious: 54\\nremoved: 27005\\ncity: city9', metadata={'source': 'sir.csv', 'row': 872, 'time': '2018-03-04', 'city': 'city9'}), Document(page_content=': 63\\ntime: 2018-03-05\\nsusceptible: 0\\ninfectious: 49\\nremoved: 27010\\ncity: city9', metadata={'source': 'sir.csv', 'row': 873, 'time': '2018-03-05', 'city': 'city9'}), Document(page_content=': 64\\ntime: 2018-03-06\\nsusceptible: 0\\ninfectious: 44\\nremoved: 27015\\ncity: city9', metadata={'source': 'sir.csv', 'row': 874, 'time': '2018-03-06', 'city': 'city9'}), Document(page_content=': 65\\ntime: 2018-03-07\\nsusceptible: 0\\ninfectious: 40\\nremoved: 27019\\ncity: city9', metadata={'source': 'sir.csv', 'row': 875, 'time': '2018-03-07', 'city': 'city9'}), Document(page_content=': 66\\ntime: 2018-03-08\\nsusceptible: 0\\ninfectious: 36\\nremoved: 27023\\ncity: city9', metadata={'source': 'sir.csv', 'row': 876, 'time': '2018-03-08', 'city': 'city9'}), Document(page_content=': 67\\ntime: 2018-03-09\\nsusceptible: 0\\ninfectious: 33\\nremoved: 27026\\ncity: city9', metadata={'source': 'sir.csv', 'row': 877, 'time': '2018-03-09', 'city': 'city9'}), Document(page_content=': 68\\ntime: 2018-03-10\\nsusceptible: 0\\ninfectious: 29\\nremoved: 27030\\ncity: city9', metadata={'source': 'sir.csv', 'row': 878, 'time': '2018-03-10', 'city': 'city9'}), Document(page_content=': 69\\ntime: 2018-03-11\\nsusceptible: 0\\ninfectious: 27\\nremoved: 27032\\ncity: city9', metadata={'source': 'sir.csv', 'row': 879, 'time': '2018-03-11', 'city': 'city9'}), Document(page_content=': 70\\ntime: 2018-03-12\\nsusceptible: 0\\ninfectious: 24\\nremoved: 27035\\ncity: city9', metadata={'source': 'sir.csv', 'row': 880, 'time': '2018-03-12', 'city': 'city9'}), Document(page_content=': 71\\ntime: 2018-03-13\\nsusceptible: 0\\ninfectious: 22\\nremoved: 27037\\ncity: city9', metadata={'source': 'sir.csv', 'row': 881, 'time': '2018-03-13', 'city': 'city9'}), Document(page_content=': 72\\ntime: 2018-03-14\\nsusceptible: 0\\ninfectious: 19\\nremoved: 27040\\ncity: city9', metadata={'source': 'sir.csv', 'row': 882, 'time': '2018-03-14', 'city': 'city9'}), Document(page_content=': 73\\ntime: 2018-03-15\\nsusceptible: 0\\ninfectious: 18\\nremoved: 27041\\ncity: city9', metadata={'source': 'sir.csv', 'row': 883, 'time': '2018-03-15', 'city': 'city9'}), Document(page_content=': 74\\ntime: 2018-03-16\\nsusceptible: 0\\ninfectious: 16\\nremoved: 27043\\ncity: city9', metadata={'source': 'sir.csv', 'row': 884, 'time': '2018-03-16', 'city': 'city9'}), Document(page_content=': 75\\ntime: 2018-03-17\\nsusceptible: 0\\ninfectious: 14\\nremoved: 27045\\ncity: city9', metadata={'source': 'sir.csv', 'row': 885, 'time': '2018-03-17', 'city': 'city9'}), Document(page_content=': 76\\ntime: 2018-03-18\\nsusceptible: 0\\ninfectious: 13\\nremoved: 27046\\ncity: city9', metadata={'source': 'sir.csv', 'row': 886, 'time': '2018-03-18', 'city': 'city9'}), Document(page_content=': 77\\ntime: 2018-03-19\\nsusceptible: 0\\ninfectious: 12\\nremoved: 27047\\ncity: city9', metadata={'source': 'sir.csv', 'row': 887, 'time': '2018-03-19', 'city': 'city9'}), Document(page_content=': 78\\ntime: 2018-03-20\\nsusceptible: 0\\ninfectious: 10\\nremoved: 27049\\ncity: city9', metadata={'source': 'sir.csv', 'row': 888, 'time': '2018-03-20', 'city': 'city9'}), Document(page_content=': 79\\ntime: 2018-03-21\\nsusceptible: 0\\ninfectious: 9\\nremoved: 27050\\ncity: city9', metadata={'source': 'sir.csv', 'row': 889, 'time': '2018-03-21', 'city': 'city9'}), Document(page_content=': 80\\ntime: 2018-03-22\\nsusceptible: 0\\ninfectious: 8\\nremoved: 27051\\ncity: city9', metadata={'source': 'sir.csv', 'row': 890, 'time': '2018-03-22', 'city': 'city9'}), Document(page_content=': 81\\ntime: 2018-03-23\\nsusceptible: 0\\ninfectious: 8\\nremoved: 27051\\ncity: city9', metadata={'source': 'sir.csv', 'row': 891, 'time': '2018-03-23', 'city': 'city9'}), Document(page_content=': 82\\ntime: 2018-03-24\\nsusceptible: 0\\ninfectious: 7\\nremoved: 27052\\ncity: city9', metadata={'source': 'sir.csv', 'row': 892, 'time': '2018-03-24', 'city': 'city9'}), Document(page_content=': 83\\ntime: 2018-03-25\\nsusceptible: 0\\ninfectious: 6\\nremoved: 27053\\ncity: city9', metadata={'source': 'sir.csv', 'row': 893, 'time': '2018-03-25', 'city': 'city9'}), Document(page_content=': 84\\ntime: 2018-03-26\\nsusceptible: 0\\ninfectious: 5\\nremoved: 27054\\ncity: city9', metadata={'source': 'sir.csv', 'row': 894, 'time': '2018-03-26', 'city': 'city9'}), Document(page_content=': 85\\ntime: 2018-03-27\\nsusceptible: 0\\ninfectious: 5\\nremoved: 27054\\ncity: city9', metadata={'source': 'sir.csv', 'row': 895, 'time': '2018-03-27', 'city': 'city9'}), Document(page_content=': 86\\ntime: 2018-03-28\\nsusceptible: 0\\ninfectious: 4\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 896, 'time': '2018-03-28', 'city': 'city9'}), Document(page_content=': 87\\ntime: 2018-03-29\\nsusceptible: 0\\ninfectious: 4\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 897, 'time': '2018-03-29', 'city': 'city9'}), Document(page_content=': 88\\ntime: 2018-03-30\\nsusceptible: 0\\ninfectious: 3\\nremoved: 27055\\ncity: city9', metadata={'source': 'sir.csv', 'row': 898, 'time': '2018-03-30', 'city': 'city9'}), Document(page_content=': 89\\ntime: 2018-03-31\\nsusceptible: 0\\ninfectious: 3\\nremoved: 27056\\ncity: city9', metadata={'source': 'sir.csv', 'row': 899, 'time': '2018-03-31', 'city': 'city9'})]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a2e53655",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()\n",
    "vectorstore = Chroma.from_documents(data, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2333d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm=ChatOpenAI(model_name=\"gpt-4\",temperature=0)\n",
    "metadata_field_info=[\n",
    "     AttributeInfo(\n",
    "        name=\"time\",\n",
    "        description=\"time the population was meseaured\", \n",
    "        type=\"datetime\", \n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"city\",\n",
    "        description=\"city in which the popoluation was measured\", \n",
    "        type=\"string\", \n",
    "    ),\n",
    "]\n",
    "document_content_description = \"Suspectible, infectious and removed population during 90 days in 10 cities \"\n",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm, vectorstore, document_content_description, metadata_field_info, search_kwargs={\"k\": 20},verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d784c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='max infectious people' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='time', value='2018-02-03')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1413\\nremoved: 35924\\ncity: city3', metadata={'source': 'sir.csv', 'row': 303, 'time': '2018-02-03', 'city': 'city3'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 822\\nremoved: 20895\\ncity: city4', metadata={'source': 'sir.csv', 'row': 393, 'time': '2018-02-03', 'city': 'city4'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1355\\nremoved: 34382\\ncity: city6', metadata={'source': 'sir.csv', 'row': 573, 'time': '2018-02-03', 'city': 'city6'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 581\\nremoved: 14728\\ncity: city5', metadata={'source': 'sir.csv', 'row': 483, 'time': '2018-02-03', 'city': 'city5'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 496\\nremoved: 12535\\ncity: city8', metadata={'source': 'sir.csv', 'row': 753, 'time': '2018-02-03', 'city': 'city8'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1028\\nremoved: 26030\\ncity: city9', metadata={'source': 'sir.csv', 'row': 843, 'time': '2018-02-03', 'city': 'city9'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 330\\nremoved: 8213\\ncity: city7', metadata={'source': 'sir.csv', 'row': 663, 'time': '2018-02-03', 'city': 'city7'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1320\\nremoved: 33505\\ncity: city2', metadata={'source': 'sir.csv', 'row': 213, 'time': '2018-02-03', 'city': 'city2'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 776\\nremoved: 19753\\ncity: city1', metadata={'source': 'sir.csv', 'row': 123, 'time': '2018-02-03', 'city': 'city1'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 654\\nremoved: 16623\\ncity: city0', metadata={'source': 'sir.csv', 'row': 33, 'time': '2018-02-03', 'city': 'city0'})]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"Which city has the max infetious people on 2018-02-03?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad5bf25",
   "metadata": {},
   "source": [
    "# Build the LLM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4e2f322",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm=ChatOpenAI(model_name=\"gpt-4\",temperature=0)\n",
    "\n",
    "# Define the system message template\n",
    "system_template = \"\"\"The provided {context} is a tabular dataset containing Suspectible, infectious and removed population during 90 days in 10 cities.\n",
    "The dataset includes the following columns:\n",
    "'time': time the population was meseaured,\n",
    "'city': city in which the popoluation was measured,\n",
    "\"susceptible\": the susceptible population of the disease, \n",
    "\"infectious\": the infectious population of the disease, \n",
    "\"removed\": the removed popolation of the disease. \n",
    "----------------\n",
    "{context}\"\"\"\n",
    "\n",
    "# Create the chat prompt templates\n",
    "messages = [\n",
    "    SystemMessagePromptTemplate.from_template(system_template),\n",
    "    HumanMessagePromptTemplate.from_template(\"{question}\")\n",
    "]\n",
    "qa_prompt = ChatPromptTemplate.from_messages(messages)\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "qa = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, return_source_documents=False,combine_docs_chain_kwargs={\"prompt\": qa_prompt},memory=memory,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "abe0967e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat_bot(question): \n",
    "    return qa.run(\n",
    "    {\n",
    "        \"question\": question\n",
    "    }) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8dc22d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='max infectious people' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='time', value='2018-02-03')\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new StuffDocumentsChain chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: The provided : 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1413\n",
      "removed: 35924\n",
      "city: city3\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 822\n",
      "removed: 20895\n",
      "city: city4\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1355\n",
      "removed: 34382\n",
      "city: city6\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 581\n",
      "removed: 14728\n",
      "city: city5\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 496\n",
      "removed: 12535\n",
      "city: city8\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1028\n",
      "removed: 26030\n",
      "city: city9\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 330\n",
      "removed: 8213\n",
      "city: city7\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1320\n",
      "removed: 33505\n",
      "city: city2\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 776\n",
      "removed: 19753\n",
      "city: city1\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 654\n",
      "removed: 16623\n",
      "city: city0 is a tabular dataset containing Suspectible, infectious and removed population during 90 days in 10 cities.\n",
      "The dataset includes the following columns:\n",
      "'time': time the population was meseaured,\n",
      "'city': city in which the popoluation was measured,\n",
      "\"susceptible\": the susceptible population of the disease, \n",
      "\"infectious\": the infectious population of the disease, \n",
      "\"removed\": the removed popolation of the disease. \n",
      "----------------\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1413\n",
      "removed: 35924\n",
      "city: city3\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 822\n",
      "removed: 20895\n",
      "city: city4\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1355\n",
      "removed: 34382\n",
      "city: city6\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 581\n",
      "removed: 14728\n",
      "city: city5\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 496\n",
      "removed: 12535\n",
      "city: city8\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1028\n",
      "removed: 26030\n",
      "city: city9\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 330\n",
      "removed: 8213\n",
      "city: city7\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 1320\n",
      "removed: 33505\n",
      "city: city2\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 776\n",
      "removed: 19753\n",
      "city: city1\n",
      "\n",
      ": 33\n",
      "time: 2018-02-03\n",
      "susceptible: 0\n",
      "infectious: 654\n",
      "removed: 16623\n",
      "city: city0\n",
      "Human: Which city has the max infetious people on 2018-02-03?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The city with the maximum infectious people on 2018-02-03 is city3 with 1413 infectious people.'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_bot(\n",
    "\"Which city has the max infetious people on 2018-02-03?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a6dfbf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='most infectious people' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='time', value='2018-02-03')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1413\\nremoved: 35924\\ncity: city3', metadata={'source': 'sir.csv', 'row': 303, 'time': '2018-02-03', 'city': 'city3'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 822\\nremoved: 20895\\ncity: city4', metadata={'source': 'sir.csv', 'row': 393, 'time': '2018-02-03', 'city': 'city4'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 581\\nremoved: 14728\\ncity: city5', metadata={'source': 'sir.csv', 'row': 483, 'time': '2018-02-03', 'city': 'city5'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1355\\nremoved: 34382\\ncity: city6', metadata={'source': 'sir.csv', 'row': 573, 'time': '2018-02-03', 'city': 'city6'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 496\\nremoved: 12535\\ncity: city8', metadata={'source': 'sir.csv', 'row': 753, 'time': '2018-02-03', 'city': 'city8'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1028\\nremoved: 26030\\ncity: city9', metadata={'source': 'sir.csv', 'row': 843, 'time': '2018-02-03', 'city': 'city9'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 330\\nremoved: 8213\\ncity: city7', metadata={'source': 'sir.csv', 'row': 663, 'time': '2018-02-03', 'city': 'city7'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 1320\\nremoved: 33505\\ncity: city2', metadata={'source': 'sir.csv', 'row': 213, 'time': '2018-02-03', 'city': 'city2'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 776\\nremoved: 19753\\ncity: city1', metadata={'source': 'sir.csv', 'row': 123, 'time': '2018-02-03', 'city': 'city1'}),\n",
       " Document(page_content=': 33\\ntime: 2018-02-03\\nsusceptible: 0\\ninfectious: 654\\nremoved: 16623\\ncity: city0', metadata={'source': 'sir.csv', 'row': 33, 'time': '2018-02-03', 'city': 'city0'})]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"Which city has the most infetious people on 2018-02-03?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d25d31ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd1b37e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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