Langchain_Semnatic_Serach_Pinecone.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM+LlDQsGXPEHE1XTQC7Kf0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PradipNichite/Youtube-Tutorials/blob/main/Langchain_Semnatic_Serach_Pinecone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Youtube Video: https://youtu.be/cVA1RPsGQcw\n",
        "\n",
        "Blog: https://blog.futuresmart.ai/building-a-document-based-question-answering-system-with-langchain-pinecone-and-llms-like-gpt-4-and-chatgpt\n",
        "\n",
        "AI Demos: https://www.aidemos.com/\n",
        "\n",
        "AIDemos.com is your go-to directory for video demos of the latest AI tools. AI Demos goal is to educate and inform about the possibilities of AI."
      ],
      "metadata": {
        "id": "PDlxSdp790BO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image               # to load images\n",
        "from IPython.display import display # to display images\n",
        "\n",
        "pil_im = Image.open('/content/langchain and pinecone.png')\n",
        "display(pil_im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "C_-uyi19vNjG",
        "outputId": "6f1fea63-559c-4763-b120-cbbd81a8291a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x800>"
            ],
            "image/png": 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rwMBA5cyZUy4uLibXwsPDdeXKFTk5OSlnzpxP3GfQnSBdv3FdudxyKXuO7E98z7Xr1+Ts7KzcuXM/tn1ERISuXL4ia2tr5cufT9bWKbPkbnBQsAKvBT5xHXE/PxcXF+XMmVO7d+/WnDlzVLRY0QQBVmRkpK5cvqLwh+Fyc3NTjhw5UqRmSxEWGKjDgz6Vfe7cKt6zh9xr1ZIdIwCTddfXVxeWryC4AiwMARYAADCrXbt2qX///o9t17t371QPsNKTwMDAZI+RvpR8uaSKeRbT4sWL9XH/j/Xzzz8rKipKktSoUSPNnjNbWbNm1fhx4zV9+nSFhIRIkooWLapp06epevXqSfb9119/adAng7Rv3z7FxMRIkqpVq6avx3ytChUqJHrPhvUbNG78OB0/dtw4V7hwYX32+Wdq3bp1gvahoaEaNnSYli9frnv37kmScuXKpX79+qlN2zaJPuOff/5Rndp11KVLF43/ZnyC8/0/7q9OnTrpw74favv27UbtJUqU0KTJkxJ9zxcvXNSgQYO0detWRUdHS5JKly6tkJAQVapUSV26dDHaRkZGatTIUZo3f56Cg4KN8+XKldOoUaPS7Ugtz+7ddHn9BoVdu2ZyPiwwUMdGjpIkudeqpfxNm8ilXDnCrH/d9fXVpfUbdG33boUl8/epZ/duCty1WyFnz6ZhdQAkAiwAAIB0qUGDBnr33Xf1559/qkKFCmrSpIm5S8JzePDggYKCgtS5U2eFhYVp7LixsrGx0dq1a7V582Z9NewrOTk5afHixfqg7wfKny+/jh49qrlz5+qd1u/o+InjiY4cOnr0qPr3668qVapo1KhRsrax1t69e7Vh/QZ5NfbSpk2bVK58OZN7Zs6YqSFDhqhQoUIaPny4ChQsIH8/f/3www/q2aOnQkJC1K1bN6P9w4cP1cSriY4ePaoyZcuoefPmcnJy0okTJzR8+HD99fdfib7n6OhohYeHKyIiItHzt27eUtMmTeXh4aGx48bK0dFR3ke8NW/ePLVt21be3t4mC8PfunVLjRs31q1bt/Re7/dUpkwZXQu8ptmzZys4OFi/bflNDg4ORvthw4Zp+rTpaty4sVq0bCEHBwedPHlS38/6Xm3atNGJkycSjH5LD1wrVFDxHj10Zs4c+f6Y+BTNa3v26NqePZIkl/Ll5VG3jpw8PeVSvnxalmpWYQEBuuXjo1tHvHXL2zvZ0EqSXMqVU6mP+8vJ01O3jninUZUA4iPAAgAAFmX8+PGyt7dPcN7Ozi7Na0lqAe60ktz0Kzs7O82dmzbrB+HZxI2gepSVlVWiowmPHzuu5m81108//2T8f9+lSxdVqlhJc+fOlaurq3bu2ql8+fJJktp3aK9MmTNp2tRp2rJli9q0STjSaeqUqRr/zXj16tXLOPf+++9r4YKF+vDDDzVo0CBt+X2Lce306dMaOnSoKlSooA0bN5j8WezcpbNqvFpDI4aPUNu2bY0waPr06Tp69Kjefvtt/TD7B5Pft93e7abGjRs/zY/NMHv2bL333nsaN36cca59+/ZycXHR+PHjtXbtWpMgbcGCBbp69aqmT5+uDh07GOdbtmqpqlWqauiXQ/X9D98b51euWKkiRYpoydIlxp91Ly8vtWvXTmfOnEmX4VV8xXv0UH4vL52ePUdXNm1Kst1tHx/d9vExjl3Kl5eTp6dyVqoop2LFZO/hkRblprrbPj666+urm4eP6JaPjyJDQ5/oPnt3dxXv2UP5vLxSuUIAj0OABQAALErPnj2VPXv2ZNvMmjVLJ0+elI2NjSZMmKDQ0FBNmzZNO3fuVEhIiPLnz6+WLVuqXbt2kqTz589r+vTpOnLkiMLDw1WsWDG9++67ql+/frLPsbe319GjRzVt2jT99ddfcnBwUMOGDdW3b99EQzYpdu2duXPnaseOHQoMDJS9vb1KlSql9u3bJ7lG1a1btzRp0iTt3btXDx8+VOnSpdW3b99kd6rz9/fX+PH/Tbv6+OOPVaRIkQTXGjdurMaNG2vv3r2aP3++/vnnH8XExKhUqVJ67733VLFixQR9R0dHa9WqVVq/fr3Onz+vyMhI5cqVS/nz5zcJJ1566SV98MEHyf4MMzL3XO6JnrexsdGt27cSvTZ06FCTn7GNjY0qV66sCxcuqEfPHkZ4Fad6teqaNnWaLl++nGh/lSpVMgmv4nTu0lnz58/XH3/8IX9/f2Ott8WLFysiIkKfDv40we/xHDlyqF27dvruu+/0x6E/VLdeXUnSiuUrZGtrq7HjxiYIXcuVL6c+ffro22+/TbS+5Dhnd9ZXw79KcL7hGw01fvx4nf7ntMn5E8dPSJIavWm6oUGBAgVUtmxZbf5ts8l5KysrRUZFKiYmxiSsLlCggFnXvktJ9h4eKjf0S5Xq308XVqzQheUrFPnvFM+kxAVaF1eulCTZZssmJ09POXh4yCFPHjkV95R97txy8vRMi7fwVMICAhQWGKi7vr6KCAnVzSNHFBka+ky7B7rXqqXCbdvINYlptkk5ceKE9u7Zaxx3fbdrkv9ehISEaPGixcZx3Xp19fLLLz91rUBGQYAFAADSnfXr12vDhg2SpDp16ujjjz+Wn5+fcf3QoUNavXq1tmzZou7du6tRo0bGujySdPDgQS1evFiTJ0/Whx9+mORzdu3apU8//dRkitO2bdu0bNky7dy5U87Ozibt9+3bpxYtWujGjRsJ+pkxY4bee+89zZw50+TD8rlz51S7dm1dvXrVOLd//37Nnz9fn3zySZK1Xb9+XdOnTzeO27ZtawRY8a/5+vrq1KlT+vTTT401hOKeMXfuXC1ZssRk5E5oaKi8vLy0e/fuJJ8d57XXXiPASsZH/T6SlRKO4ktqLTcXFxcVLVo0wfmsWbNKkqpVrZbktYiHEQmuSbFhT1Lq1q0rb29vHT582Ahs4j54lytXTqGJjFApWLCgJBmB2d27d/XPP/+oXLlySS4o3/CNhs8UYFWtUjXRD/5xUyVDQkNMzse1DQ8PT3BP2IMw2dmajuL08vLSvHnz1L5de/Xu3VvVX62uzJkzP3Wd6YGdo6OK9+ih4j166Nru3QrYuUu3vb0TrJOVmMjQ0ASjtOLY585tjNCKC7hin5ctQcAVF4Q9qYiQkATrTEWEhurumf/CqJtHjhivE6vvabmUK6fcdesod+3azzXybMiQIcbrAgULyCuJ0Vu//fabSdvDRw4/8zOf1R9//KGH4Q9V4qUScnNzS/PnA0+DAAsAAKRrb7/9thHM2NraKjIy0rg2f/58LVy4UNHR0bKyspK1tbXJtK7PPvtMXbt2lZOTU6J9Dxw4UNHR0Qn6PXr0qAYOHKjZs2cb5/z8/NSkSRMFBQVJklxdXVW7dm0FBATo4MGDkqTvv/9ehQsX1qeffmrc16lTJ5PwKk54eLhGjx79DD8RU7///ru2bImdImZjY6Po6Gjj5xUVFaU+ffqoadOmxnSwESNGGOFVjhw5NGrUKOXKlUvfffed8T6cnZ01cODAJD+UIdaXX35p7Br5JB63+1389Z6e1KMjtuLzyBP7Af3mzZvGuWv/BholipdItt/wh7EhUVxY65HMh/28efM+WbGPyOWeK9HzRgAcY3q+YcOGWrp0qWbOnKmRI0ca53fu2Knjx46rZcuWJu2HjxiuoKAg/fLLL9q8ebMyZ86sypUrq0XLFmrXrp3JelkvEvfateVeu7akfxcuX7ded319dfvo0afuKyww0Fg7KiUCpLRm7+4ulwoVlLNiBbnXrp0iC9qXLl1anp6e8v131NeG9RuS/Lty3bp1xusyZcuoWLFiz/38p+Hv5683Gr6hmJgY/Tj3R7Vq1SpNnw88LQIsAABgUXLnzp3gXKdOnUzCovhiYmLUsGFDzZ8/Xzlz5tSwYcM0ZswY43p0dLQ+/PBDjRgxQnZ2durcubPWrFkjSbp3754OHjyohg0TH6Xi4uKiVatWqXbt2vrrr7/UrFkzXbhwQVLsejtjx441QoVRo0YZ4VWePHnk7e0td/fYKWTjx483QquxY8eqX79+ypw5s/bv368DBw4Yz6tUqZIWL16sPHnyaOPGjerRo0eio2CeRkxMjOzs7DRjxgy1b99eUVFR6tSpk9auXStJun37tvbt26fXX39dkrRq1Srj3sGDB6tPnz6SpAoVKhijg0JCQtSpUydjNA5SRnJrnkmSje3T78KZ3Dpucdfij8yLiYlR5syZNXLUyKRukxQ7eiv+vU/ynKf16Iipx3mrxVvqvKOzpk6Zqj279+iV0q8oMCBQ27dvl5ubm4YNG2bS3snJSfPmz9Nwv+HauGmj9u3bpz279xjTbTdv3myMcHtROXl6qtTH/+0Ce9fXV3fPnNHNI9666+v7Qu20Z5s1q1wrVJBTcc/Y//X0TLUdGFu2bKlx42LXbtu0aZMiIyMThNkPHjzQtq3b/runhWnAmhaWLV9m8ucfsHQEWAAAwKIkNv3n0V3K4rO2ttbChQuNsOiLL77QN998Y4yYKly4sCZNmmR8iP7888+NAEuSApPZeap///7GB/VXXnlF48aN0zvvvGPUtGvXLrVs2VIxMTEmffbq1cuoR5IGDBigr776SmFhYQoKCtLhw4dVo0YN/f777ybPmzt3rkqUiB350qZNG/39998aPnx4kvU9qS5duqhHjx7G8fDhw40AS5IuXbpkvL5+/brxOq4WSSpSpIgxEi06Olr//PMPAVY6cOXKlSSvBVwNkCTlyvXfSCe3XG4KCAhQhw4dnii8ccsZO+UosVGET1JDSmvatKk2b9osf39/+fv7y9XVVT169NCAgQNM/kzGV6BgAfXu3Vu9e/dWeHi4Pv74Yy1ZvETz5s7TB30z1hRZJ09POXl6mixYHhYQoPsBAbrr66v7V67Gri8VGmqR4ZZt1qxy8vSUvYeHHPJ4yLl4cdlmy/bU61g9rxYtWxgB1p07d7R/337VrlPbpM327dtNvqBo0aJFgn4uXLigJYuX6MiRIwoODpazs7MqVqwY+wVCocT//o2Ojtb6deu1+bfN8rvop4iICLnndtfLL72sFi1bGGtsHTt6TAvmLzDuW7J4iQ4eiB1l6+LioiGfDTHp9969e1q2bJl2796tgKsBymKfRUWKFFHrt1urZq2aCeq4dOmSpkyeIklq0qSJ6tSto8DAwNj1KA8fkaOjo1asXPHYnyUQHwEWAACwKI8uFC4pybV1JKl48eImH0wdHByUK1cu4wN1jRo1TEaAFCpUyOT+hw8fJtl3+Ue2lG/QoIHJ8ZkzZyTFTh+8c+eOcX7NmjUmI6seFbd+UNz9Uuw0q9KlS5u0q13b9APPs3p0sfq4tbLixP8Z5MuXz6jLN97Cx35+fibTKFkrJX34fcvvGjhwYKLXdu3aJUmqXLmyca5qlao6fuy4du7cmei0p/v37ys0NNQIvZyzO6tIkSI6deqUbt26leg0x62/b02Jt/JYmzZtUru27TRx0kS9++67j20fGhqqbNmymZzLnDmzBgwYoCWLl+jUqVOpVWq6Yu/hIXsPjyRDoFve3pL+C7ok6f7VAIX9+zru2pOst5UYl3LlTI5dK/5XR1xNDv/WaCleeukllSxZUn/99Zek2HUbHw2w1q9bb7yuWLFigkBq9uzZ+mzIZwm+wNmxY4emTJmiSZMmqX2H9ibXAgIC1KF9B3n/+/9JfOt+Xac5c+bo9JnTmjx5sr4e/bWio6ON69u3b9f27dslxW5kED/AOnz4sDp26JjgC589u/dowfwFaty4sWbOmmmyLuTNmzeNkdN37tyRi6uLWrZoaUw7flE2SkDaIsACAAAW5fjx44/dhTC+xEZV2Nn9N/Xo0XWF4l97nEd3AcyRI4fs7OyMDxTBwcGSYqfhxXfixAmdOHEiyX7DwsIkxS6AHSexNYSeZc2jxDw6LTO5n0HHjh01dOhQSbHTHV1cXOTu7m6y42GJEiVU7pEPlbBMhw4d0oL5C9SlaxeT82vWrNHhw4dVs2ZNk3Wy2rZrqzlz5mjkiJF69dVXE/z5GfrlUK1cuVKH/jhk/J59++23NX78eH35xZeaMXOGSftTp06ZbDaQms6fOy9Juht8N8HOgo/y9/eXV2MvffLJJ+rcpbPJtaP/rgXlmjNl/vy96NJ6dFN60aJlCyPA2rBhg8aNH2f8noyMjNSmTZuMto+uz7ZhwwZ9MvC/TTwKFy6sMmXL6MTxEzp//rwePnyovn37qmixoqpataqk2FHB7dq2M37/xrGxsTHWfnznnXdkZ2enA/sPmIRXyfH399fbrd42pshLsSOf49+/ceNGvdfrPS1fsTzRPnbt2qWjR4+abHDi4uLyRM8H4iPAAgAA6drjFsl+3LpCyYkLmuIfx/82PG7Hskd3LmvatGmCUU7xlSxZUpJpkHT//v0E7eKPeHoeTxPaDR48WCdOnNCqVat069Ytde/e3eS6h4eHVqxY8Vw/14xi2LBhyYYon376aYKQNKU1bdZUAwYM0O9bf1e9evVkY22j/fv3a/Xq1cqaNavGfzPepH3FihX1Qd8PNHXKVFWvVl0dO3ZU4cKFFRQUpI0bY9eJatOmjUng+lG/j7R69WotXbpU586d01tvvSVHR0edOHFCixYtUtd3u2rG9BmPlpbi3mnzjhYvXqxhw4bp66+/lrOzs+wd7JUtazYVKlRI9erXU5cuXWRnZ6foqGiFhIToww8/1Pr161Wvfj052Dvo77//1qJFi5QlSxZ16NAh1WvGi6tly5YaPSp2I44rV67Ix8dHFf4N+/bu2WuM2rWyslLzt5ob98XExGjY0P/Wa2vQoIGWLV9mfHnSpXMXbdy4UVFRUfpm/DdavWa1JGn16tUm4VXr1q01avQoubi4yNfXV+vWrVOzZs0kSWt+WqNbt26paJH/dj1NahH38ePHG+GVnZ2dJkyYoFZvt1JYWJjGjhlrjLLavHmzfvvtN73xxhsJ+rhx44Zu3LghJyen2B1zixZJdmQ1kBQCLAAAgCScOXPGWNxckk6ePGlyPc+/W8bHTXuM+0a6fv366tev32P7j7tfip2i9+DBA2XJksU4d+7cuecp/5nY2dmpZs2a+vnnn+Xg4KBChQrJyspK+fLlU/369dW9e3eTaSJI2vRpyY886tu3b6oHWG+99Za6dOmiIYOHmExZqlSpkr777jsjTI1v5MiRKlqkqCZPnqxvv/3WOJ89e3Z98sknGjxksEn7rFmzavNvm9W/X39t2rRJhw4dkhT7+3v8+PF6s/GbqR5gxcTEaMP6DYqJiZGnp6c88njoQdgD3bt3T3fv3tWmTZu0YcMG7d+3X3PnzVWhwoW0bfs2DRs2TJs3bTZ26pSk0mVKa8yYMcZaQcCzKFq0qMqULaPjx45Lip0yGBdgxd99sGrVqiajIE8cP6Gz8dYX69e/n/ElhJ2dnT7q95E2btwoSdq7d6+xQPyva3817smZM6emTZ9mfLlSsmTJRP+sP050dLTW/fpfrc2bN1enzp0kxU7XHzturDZs2GBM2V+5YmWiAZYU++XHzl07k1yLDngSBFgAAABJmDt3rrp27aqsWbMqJibG5MO8JGPqhpOTk6pWrWqsezVt2jT17Nkz0UWwL126pPz580uSqlSpYkyvunfvnr7//nt99NFHkqSoqCj9+OOPqfbekrJ161ajhtGjR+uDDzLWItYpweeoz1Pfc/7C+SSvTZw0URMnTUz0Wt16dRUUHJTgfK9evdSrVy/juMHhBvK76Kfbd27Lw8Mj0d0+4+v6bld1fberLl++rBs3bsjZ2Vn58uVTpkyZEm2fK1cuLVm6RHfu3JHfRT9lc8ymwoULy8YmdufExGosWbLkU52PU6RIkQTXp02dpi+//FJTpkxJMCVQip0G1aRJE/30008aN36c3NzcVLRoUS1evFj379/XuXPnFBkZqdy5cyc6nRd4Fi1atPgvwFq/XkOHDVVMTIwRQElSy1am0wcf/aJk7JixJqN842908uDBA92+fVu5cuUyua927doJRgY/C38/f2OqfFy/8dnY2KhmzZpauXKlJOnYsWNJ9vVRv48Ir/DcCLAAAIBFKVOmTKLT006fPp0i/0H+NLy9vVW2bFnVq1dPf/31l/bv329cK1u2rMqWLWscDxkyxJiece7cOZUpU0Y9e/ZU/vz5de3aNV24cEFbtmxR+fLltXx57DohzZs3l6Ojo0JCQiTF7nq4d+9e5c+fXzt27Eiwlkla8PPzM16PGDFCBw4cMEYJZc6cWQUKFFCdOnVUqVKlNK8Nz6dgoYJJ7lyWlHz58pmMDnmcHDlyJFg3Ky3ELT7d2KtxotcLFCigalWryd/PXzdu3DDZhMDBwSHBBgpASmjZoqWGfxW7k+yZM2d09uxZBQcHK+DfBe6tra3VvHlzk3vibwgixY6ySs6DsAeSZLJGlXvulAmK4vcpSTndEk77i79WXPw1rh718kuMaMTzI8ACAAAW5dKlS4mej4mJSdM6smSJ3SL8r7/+SjCVL1OmTAkWpm7atKmGDh2qESNGSJLOnz+vIUNMtyGXTHc2dHZ21rhx49SnTx9Jse9x9erVxvW3337b5DgtvPPOO5o5c6aOHDmiGzduaOnSpYm269Spk+bPn89aWLAIHnliR02tWrVK77//foLrFy9c1LZt2+Ti4qKiRYsmuA6khoKFCqpChQrGroDbtm0zvrCQpBo1ayQYlZQps+kox+7duyc58lGSHJ1iv2CIvx7kvXv3nrt2SXJydjI5Dg0NTdAm/rnk6nz0fQHPggALGY6fn582btwoHx8f3bhxQzY2NnJzc1PVqlXVuHFjY1toAEDG9s4772jChAl6//339dNPPxm7OJUoUUIzZsxQjRo1EtwzfPhwVa1aVWPGjNGBAweMe6TYqRZ16tRRx44dTe55//33FRMTo6FDh+rWrVuSYqckDho0SJ999pkqVaqU6JboqWXNmjUJtm1PzKJFi9SsWTO9/fbbaVAVkLx+/fppw/oN+mzIZ/p17a+qVq2acuTIoQfhD/T3X39r8+bNCg8P15wf56T5SE5kbC1btjT+Dt+/f7+Cg4JNrj2qQIECJsddu3ZV6TKPHyGYO3duY/TW6X9OP0/JBg8PD5Odd33P+CZoE/9coUKFUuS5QFIIsJBhhIaGql+/fpo/f77JB4o4s2bNkr29vfr166eRI0caazYAAFJXv379nmjB8/jWr1+f7PWLFy8meS1btmxJjuaqVKlSgmsrV67U7du35efnJ2dn52R3F5Skxo0bq3HjxgoJCZG/v7/CwsKUNWtWFSxYUA4ODone06dPH/Xs2VNnzpyRFLv4b9xi7keOHHniWp/kmhQ7uiyx6xMmTNCAAQMkSdWrV9fQoUNVsGBBWVlZKTw8XOfOndPgwYPl6xv7gWX//v0EWLAIxYsX185dO/Xdd99p546dxnp0Uuz6XE2aNNGHH374REEAkJLeavGWvvzyS8XExOiPQ3/ofljsjrO2trbGtPP4qlWrJnt7e2MX3EmTJunHuQnXQ3z48KGCgoKML98rV6msv//+W5J06NAh7dixQ/Xq1TPah4aGatfOXfJq4iUp4Wip69evJ3iGvb296tatq99//12StHTpUr3X+z1jB8E//vjD2LRBksnzgNRAgIUMISQkRLVr137sWiJhYWEaM2aMTp48qV9++YVpEQAASZKLi4tcXFye6h5HR0eVKlXqidvb2dk9VfvU8P333xuvV6xYYSw2H6ds2bI6efKkhg2L3eI9/o6JgLkVLlxY06ZNkxT74T7sfpgcsjoYO7gB5pAvXz5VqVJFhw4dMta+kmIXRHd1dU3Q3tnZWd17dNe0qbG/l9esWaNr166pabOmcnJyUkBAgM76ntWWLVv02eefqVu3bpJip3UvXLDQ6KfNO230esPXlS9vPl2+fFm7d+9WhYoVjADL0dFRrq6uxsjfsWPHyt/fXzHRMfLx8dFvW36TJH300UfaunWrYmJidOXKFdWuVVuvNXhN9+/d1+bNm40vQxwdHdWte7dU+AkC/yHAQobwwQcfmIRXhQoV0oABA1ShQgVFRUVp//79+vbbb3Xz5k1JsVvbfvPNN/r000/NVDEApLzhXw2X71lfLV682NylwELFfeMvSTt27FDnzqa7ue3bt0+zZ882juvXr59mtQFPI1OmTMmuxwOkpRYtW5iMVJISnz4YZ+jQoTp18pR27NghKXYh98ct5l65cmX1/bCvpk6ZKik2xN2wfkOy97Ru3VqzZs2SJAUHBWvmjJkJ2tSsVVNffPmFRo4YKUm6evWqFi1cZNImU6ZMmjVr1mN3NwWeFwEWXnhnz541+bBWvHhxHThwwOSb9Fq1aql169aqUqWK8S3E+PHj1bdvX2O6x+nTpzV16lTjns8//9xkm+V79+6ZBF7dunVThQoVEtSzZ88eLV++XKdOnVJYWJg8PDxUp04dde/eXU5Opgslbtu2TT///LMkacCAASpcuLCOHDmiqVOn6p9//lH9+vX18ssvm/yDOGrUKGXPnt2kn7179xo7Xkmx36R4eno+/ocH4IUyd+5cBQcHK7d7bv3888+q/mp1c5cEC9OqVStNmjRJktSlSxd9/fXXKliwoO7du6fz58+bjB7o0KGDGjRoYKZKgYznxPET2rtvb6KL1MOyvfXWW/psyGeKjo6WFDvitkmTJkm2z5Qpk1avWa3Zs2frxzk/GtO242TNmlWNGjVS9eqm/46PHDlSJYqX0OTJkxPcU7hwYXXo0MHk3BdffqFr167pl19+MZlWXqZsGZN2AwYMUMmSJTXhuwn6888/jfO2traqXbu2vvjyi0Q/9wApjQALL7zly5cb/1hI0rhx4xKdBlKkSBENGjTICKFu376tTZs2qVWrVpJid8WKv+NU7969TQKssLAwk+s1a9Y0+Ys8IiJCvXr10vz58xM8e+3atRo/frzWrl2rKlWqGOd9fHyMPkuVKiUHBwf16NFDkZGRkmIXEq5UqZLJcytVqqSuXbua9D9p0iStWbNGUuw0mAkTJiTx0wLwIps4caJ69uypBw8e6M3Gb6p/v/4a9tUwc5cFCzJmzBgFBwdrwYIFio6O1unTp3X6tOliwO7u7ho0aNBTr1sG4PkMGTJEe/fu1dKlS7Vk8RIVKFjg8TfBIuTOnVu379x+qntsbGzUu3dv9e7dWzdu3FBgYKCiIqOUPXt25cufz2TXwfg6duqojp066tq1a7p+7bpiYmLklsvN5HNLnGzZsmne/HmaeGei/P39ZZfJTh65PZQ9R/YEbd988029+eabCg4K1uUrl2Vtba38+fMrW7ZsidZRvnx5BQUHPdV7Bh6HAAsvvPiLeGbJkkVeXl5Jtm3RooXJKKqDBw8aAdbzGjRokEl4VaVKFeXNm1d79+41/lFq1qyZ/v77b+XIkSPB/XPmzNGpU6eM8EqS3Nzc1KxZM3l4eBjfiq9cudIkwLp//742bdpkHLdr1+6FGFK/fPlyLV7ENCjgaZUvX17Hjh1TRESEJk6cqF9++UU+R33MXRYsRJYsWTR37lyNHDlSO3fu1IULFxQWFiZ7e3vlzp1bpUuXVuXKlVkjEjCDMWPGqEOHDjpx/IRq1a6lGdNnGOsZ4cXm5uYmNze3p7rH3d1d7u7uT9Q2e47siYZWiXHO7izn7M5PVQuQUgiw8MLz8/MzXnt6eia7kGexYsWUKVMmPXz4MMG9z8Pf399k+uGIESP05ZdfSpJu3rypKlWq6MKFC7p27Zq+//57DR48OEEfcdvvFitWTG+//bby5s2r8uXLy9bWVu+++66+/vprSdLWrVt1584dIwTbsGGD7t+/b/Tz7rvvpsh7MrdPB32q4ODgxzcEkKwLFy7owP4DTCeEibx58yaYamJuY8eMNXcJgNm1bNlSGzZskK+vrzp06KA2bdpocL265i4LANIEARZeePFDDmfn5L8tsLKykqOjo7EOVlBQUIrUsGbNGkVFRUmKndM+aNAg41rOnDnVq1cvDRkyRFJsAJVYgCVJjRo10tq1axOMoOrZs6fGjh2r6OhoRURE6OeffzZ2JFm1apXR7pVXXlHFihVT5D2Z27jx4xiBBTyD4OBgnTp1ypha3aBBA8IrpAtjxxJgAY9asWKF3itb1txlAECaIMDCCy9z5szG67iRVcmJ3yapueVP69ixY8ZrGxsbNW/e3OT61atXjddXrlxJsp9x48YlOv2vUKFCev311/Xbb7Hb3a5cuVLdunVTWFiYNm7caLR7dG2s9Kxt27Zq27atucsA0pWZM2ZqyGdDpJjYv4tmz56tlq2S3gUJsCRJfbkDZCQPHjzQTz/9JH9/f0lSjx49lMMlhwIecx8AvAgIsPDCc3V11blz5yQlHw5J0t27dxUSEmIc58qV64mfE3+h+Efdvv3foo1hYWFG0JSY+FuYx2dlZaWXX345yfvee+89o99t27bp9u3b2rFjh+7duycpNozr2LFjsu8BwItt7NixUkzsuhi///47CwAjXRk8hAALGduJ4yfUpGkTBQcFy9nZWUuWLFHNWjV1Ydkyk3aRoaFmqjDjCAsMNHcJQIZEgIUXXrly5fTHH39Iig2wLl68qEKFCiXadv/+/SbHpUqVeuLnJLceU/xRYG5ubmrfvn2SbRPbIVGSrK2tk12/q2nTpsZi7pGRkVq7dq22bdtmXG/UqNETL+QI4MU0ceJE7dixQ1OnTX18YwCARRkyZIiCg4JVs2ZNLVmyJMmFtG8ePiL32rXTuLqMIyIkhAALMBMCLLzwXn/9df3www/G8eTJkzVx4sRE206ZMsXkuFGjRsbrR3dcilsnK87JkyeTrCF+YBYZGamJEyfKysrqsbU/DVtbW3Xr1k2jR4+WJO3evVt79uwxrr9I0wcBPJuWrVoyZRAA0qkxY8boxIkTat8h6S9CJenypk0q3rOH7Bwd06iyjOXUxEnmLgHIsNgDGS+8t956S/ny5TOO4wKsuEXVpdj1BPr166dNmzYZ52rXrq3SpUsbx9mzZzfpN/7aUg8ePEgyFJNiQ7Q4d+7c0ffff59ou9u3byv0OYZ99+zZ0wjadu7caayP4OrqqqZNmz5zvwAAADCv0mVKPza8kmKnEB783weKiLcsBlLG5Q0bdCXe5wUAaYsRWHjh2draavbs2WrcuLFiYmIUExOjjz/+WGPGjFHp0qUVFRUlb29vk7WvHBwcNG3aNJN+SpYsqaxZsxprSn3zzTe6cOGC3NzctH37dv3zzz9J1vD666+rUqVKOnz4sCSpT58+OnDggGrVqiVJunz5snx8fLRlyxb9+eefeuWVV57pvRYsWFANGzbU5s2bdfHiReN8u3btEl38HQAAAC+eu76+2tO5iyqNHycnT09zl/NCODVxki6uXGnuMoAMjQALGUKjRo20YMEC9ejRw9hl8MaNG9q+fXuCttmzZ9fKlStNRl9JUpYsWdSvXz9jil5MTIxWrVplXM+fP79y5cqlI0eOJOjTyspKK1euVL169eTn56eYmBgtXLhQCxcuTMm3KSl2MffNmzebnHv33XdT/DkAAACwXGGBgdrTuYsKvfOOivfozpTCZ3Rt926dmjiJda8AC0CAhQyjU6dOql69usaOHat169bp+vXrCdq4uLjo2LFjJlMO4xs+fLik2GmIcVP9rK2t9cYbb2j69Olav359ogGWJBUuXFje3t4aPXq0Fi9enOD5Hh4eevvtt5N89pNq0qSJsZi7JJUuXVoVKlR4rj4BIC3cuHFDO3bs0OHDh3Xz5k1FRETIw8ND+fPnV4MGDZLdiRUAkLiLK1fq8saNyte4sYq0bSN7Dw9zl5QuXN64UReWr9BdX19zlwLgX1ZBwUEx5i4ClsndNaf27t+vSpUrm7uUFBcTE6OrV6/q5s2bmjZtmubMmWNce++99zRr1qxk74+IiJCvr68ePnyoggULKkeOHE9dg5+fn27duiUrKyvlypVLefLkSZGF3R88eKC8efPq9u3bkqTvvvtOH3/88XP3aylWrVypaVOn6tcN681dCvDCqVKxoiZMnCSvJk3S9Ll37tzRkCFDNH/+fIWHhyfZrnLlyjp06FCKb4LxIjlw4IDCw8NVsmRJ5cqVy9zlpIjdu3er4Wuv6dqtm+YuBbBIF5Yt019T/ttd1r1WLV2Lt5HPo1zKl1f+Jl5yr1WLUVmPuOvrqwvLVyhw925FJrEurWOxYooMCVHYtWvGucZ798jKxiatygQyLEZgIUOysrJS3rx5lTdvXs2YMUO+vr7atWuXJOn7779X3rx59eWXXyZ5v52dnUqWLPlcNRQsWFAFCxZ8rj4SM3XqVCO8ypw5szp27JjizwCAlOLr66s33nhDFy5ceGzbqlWrEl4l4+LFi6pRo4ZiYmK0bNkytW3b1twlATCDwm3bqHjPHjo1YaJuHz2a4PptHx/d9vGRFBtmedStI5fy5TPkWlkRISG6ffSoAnfuSja0kiTbrFlVvGcPFW7TRgfe72MSYAFIGwRYyPDs7Oy0evVqVapUSX5+fpKkoUOHKk+ePOrevbuZq3syv/zyi7Jmzart27frm2++Mc737NnzhfkGHsCLJyQkRE2bNjUJrzw8PPT++++rSpUqsrW1lb+/v/bv369Vq1bp/fffN2O1lm/hwoWKiWFgPQDJydNT1WfO0IUVK3Rh2fIkw5b4YZZttmxy8vRUzooV5VqhvByLFXvhRmjd9fXVXV9f3Tribbx+EnnffFMlevZg+iVgZgRYgKScOXNq7dq1aty4sTF95YsvvlDx4sWNnQItWdu2bRNMuylUqJC++OILM1UEAI83adIknT592jiuVauW1q1bJ2dnZ5N27777rqZPn57kbqpRUVFauXKl1q9fb+zAWrhwYTVt2lRvv/22bB6Z1jFr1iydPHlSNjY2mjBhgsLCwjRr1ixt27ZNt27dkpubmxo2bKhevXrJ3t4+0WfevXtXc+fO1Y4dOxQYGCh7e3uVKlVK7du3V40aNRK0HzBggMLDw/XKK6+od+/eevDggWbOnKlNmzbp3r17WrhwoYoWLSpJun//vtatW6ft27fr9OnTCg4OVpYsWeTp6anmzZurZcuWCUaieXt764cffjCO582bp71790qSXF1djTUc49y7d08LFizQ9u3bdeXKFdnb26tYsWJq37696tatm6B+f39/jR8/XpLUokULvfbaawoICNCECRN06NAhOTk5af16pnYDlqZwmzYq3KaNLm/YoEvrNyQ6IitOZGioSaAlxQZhTp6ecsiTR07FPWWXLZtcypdPg8qfT1hAgMICA3XX11cRIaG6eeSI7vr6JjvC6lG2WbPGrhvWrq1FBlc3b97UqpWrTE9aSfZZ7JUjRw6VLFVSno8ZVZdUH3Z2dsqaNavy58+vMmXKyMnJKYWrB54NARbwr7Jly+rKlSvmLiNFVK1aVcuWLZO7u7u5SwGAJM2YMcN47eLiojVr1iQIr+IkFV4FBASoadOmCTbQ2L9/v5YsWaLq1avr119/Vc6cOY1r69ev14YNGyRJ9erV08CBA3Xu3DmT+zdu3KglS5Zo165dCUKsffv2qUWLFrpx44bJ+V27dmnGjBl67733NHPmTJOQ6fvvv9e9e/fk4eGh9u3bq0GDBvrzzz+N63EhW1RUlEqWLGmMCI7v4MGDWrRokRo2bKi1a9cqS5YskqTRo0dr6NChio6ONtpu2bJFW7ZskRQ7ZT1+gHXo0CG1aNHC2Owjzo4dOzR79mw1a9ZMCxcuNPn/4vr165o+fbok6fbt28qZM6caNmxobEiSGlPiAaScfF5eyuflpVve3rFBlrf3E02BS2qUkn3u3LL38JCTp6cy/RtuxAVcklJ19Fb8ICouoJKk4NOnjdDqebjXqqXcdWrLvXZtix6BFnA1QEOGDEm2TZ48edSzV0/17t070S9knqQPW1tbNXqzkb7++msVKFDguWoGnhcBFvACmDFjhi5duqQcOXKofPny6WLUGICM7dSpUwqMtyV5t27d5Obm9lR9REZGqnnz5kZ4ZWdnp9dee01WVlbatm2bHj58qAMHDqhjx47avHlzon20bNnSmHZnZ2eniIgI49qff/6pyZMna/DgwcY5Pz8/NWnSREFBQZJiRzfVrl1bAQEBOnjwoKTYsKpw4cL69NNPEzwvICBAXl5eJuGVJCNgs7GxUdu2bTVu3DhZW1urSJEiKlCggM6cOaPLly9Lig2nJkyYoM8++0yStGfPHpPwKjl+fn568803defOHeOctbW1yf2//vqrOnbsqHXr1iXax7Zt23TkyBGT3XTjB4QALJdrhQpy/Xd36ru+vgrctUuBu3Yr5OzZp+onLDBQYYGBJqO1khMXeD2LJ33Gs7LNmlXutWsba4FZcmj1tK5evarhXw3XmtVrtHTpUhUo+PQBVGRkpNavWy8fbx/t379fztkT/6IJSAsEWMALoFu3buYuAQCeiu8j35DXq1fPeH316lWdP38+0fuqVasmW9vY/3xZsWKFEQRZW1vr999/V506dSTF7sZXq1YtRUVF6bffftOhQ4dUtWrVBP3FxMTo1Vdf1cKFC1WkSBFt375djRs31sOHDyVJ69atMwmwRo0aZYRXefLkkbe3tzHadfz48UZoNXbsWPXr10+ZM2dO8My9e/fK2tpa77zzjipUqCBra2tl+3fUghQ73dDNzU3t27eXx78f+B4+fKhatWrpjz/+kCT9/PPPRoC1efNm3bx50yQATGoR9xEjRhjhlZ2dnWbOnKl27drp/v37+uqrr4xRVnGj1Ly8vBL0cf36dV2/fl1OTk7q3LmzPD09nzp8BGB+cdMDi/fooYiQkNi1oby9dfeMr+6eOZOii5THBV6WwKVcOTl5esq5RHE5FS/+wixe36VrF5UqVUoPwx/Kz89PmzZtMr74OHnypFq3bq3ft/6e7HTA+H1cunRJP/30kzHa+MqVK1q9ZnW6WSMYLyYCLAAAkObidkuN4xHvm/mVK1eqf//+id5348YNY7TPypUrjfO1a9c2witJql69uurXr6/ff/9dkrR169ZEAywrKyutWLFC+fLlkyS99tpraty4sX755RdJ0qVLl4y2MTExWrNmjXHcq1cvk6naAwYM0FdffaWwsDAFBQXp8OHDia6HZW1trW3btiW61pQkubm5acCAASbnMmXKpIYNGxoBVtyHkqcRHR1tUv/bb79tfBBxcHDQ5MmT9csvvxjT6RcvXpxogCXFhndHjhxR7ty5n7oOAJbHztHRZHSWJCPUip2mF6JbR7wVERr61KO1zME2a1Y5eXrK3sNDDnk85Fy8uDHl8UXV8PWG8mry39/Zo78erQEDBmjRwkWSpNOnT2vypMn6cmjSO60/2kenzp1Us0ZN4/hsOvj/Hi82AiwAAJDmHl3TKioq6qn7OHbsmPHa19dXjRo1Mrn+119/Ga+TCnw8PT2N8CpOkSJFjNdxI7Gk2Ol38aferVmzRgcOHEiyvqSe2bx58yTDK0kKDw/Xhg0btGfPHp0/f163b99WWFiYSX/xpzo+qYsXLyo4ONg4rl+/vsl1Gxsb1a1bV0uWLJEUuzB8Uj799FPCK+AFlyDU6vHftbhQKzI0VMFnzkiSbh357++M5BaLf1727u7GdEQnT0/ZOTnKztHRCKfih3AZWaZMmTRx4kQd/vOw/v77b0nSnDlzNOSzIcZI5sd5dH3DHDlypHidwNMgwAIAAGnOxcXF5Pjy5cuqVKmSpNhQqVWrVpJipyzErS31qPijuK5cuZLsRhxhYWGJnk8shLGzs3vs8yTpxIkTOnHixFM/s1SpUknec+TIEbVs2VL+/v5JtnlW8cM3ScqVK1eCNvGnAl5LZvpQcu8BwIsv/kgm99q1Y1/0SKKxpFvJBOKP4+DhYZG7AKYHtra26tixoz7//HNJUnBwsLy9vVWlSpXH3nvnzh2N+XqMybnXX389VeoEnhQBFgAASHNly5Y1Od61a5feeustSZKXl5cxdW316tVq3bp1on1kzpxZISEhkqTy5curdtyHqEQk9R/rSYVVST0vvqZNm5qM1npUyZIln6ifOFFRUWrVqpURXmXJkkUffPCBatWqJQ8PDy1cuFDTpk174nof9egOj3E/u6TOJVXn464BwKMYFWU+ZcuZ/nt7/vz5JP9N7Nmzp7Ghyf37943z1tbWGvTpIJUvXz5VawUehwALAACkubx586p48eI68+/UkwULFujzzz9/qt3sChUqpJs3b0qSChQooEmTJqVGqYb8+fOb7NhXv3599evXL8X6P3r0qPz8/Izjr7/+2mQtsFWrVj1X/3nz5jXZafGff/5J0Cb+ueTCOQBA+vDotL+4jUgSEz+0iq9Xr17q0SOZIXZAGrE2dwEAACBjev/9943Xd+7cUZMmTXTx4sUnvj/+VIaNGzfq+PHjibZLqel4Tk5OJgvBT5s2Tffu3Uu0bfzF359U/PWpJOmll14yXj98+FAbNmxI8t5HR0QlNv3P3t5eDRo0MI7nz59v7C4lxe7cuH//fuOYqSIAkP6Fh4ebHGfLmi2JlrEbdHh6eqpIkSImu+POmjVLNV6todOnT6dancCTYAQWAAAwiz59+mjhwoXy8fGRJB06dEjFihVT5cqV5ebmphs3buj8+fNJ3v/BBx9o2rRpCgkJUUREhGrWrKk+ffqoVKlSunv3ri5duqQ9e/bowoULunr1aorUPGTIEDVr1kySdO7cOZUpU0Y9e/ZU/vz5de3aNV24cEFbtmxR+fLltXz58qfqu3DhwibHX375pUJCQnT//n398MMPJovSP8rR0VE5c+Y0RqR99dVXunjxoqKjo3X48GHt27dPkjRo0CBt3rxZMTExunz5ssqXL69GjRrp3r17WrdunWJiYoz+4geMAID06dF/R3O5J1z/MM4333xj7EIYHR2tX9f+ql69eunhw4e6du2aRgwfoSVLl6RqvUByCLAAAIBZZMqUSevXr1fjxo2NHQWjoqKSXLT9UXny5NGyZcvUunVrhYWFKSQkROPGjUvQzt3dPcVqbtq0qYYOHaoRI0ZIiv1gMGTIkATtnmWdkMKFC+u1117Ttm3bJMUu6N6mTRvjeufOnbV8+XKTnRHja9++vaZMmSIpdopIYlMq69atq1GjRhkL+l65ckU//vijSZtMmTJp4cKF8mDRZABI9zZv2my8tra2VpXKj1/APa7tWy3e0sqVK7Vx40ZJSnbnXSAtMIUQAACYTZ48eXTo0CFNmjRJpUuXlpWVVYI22bNnV4MGDTRu3Dg5OTmZXPPy8tLhw4fVunVrOTg4JLj3lVde0cCBA1O05uHDh2vDhg2qWbOmbGxsTK7Z2Niofv366tix4zP1vXTpUjVu3NjkXI4cOTRmzBjNnz/fWOg+MaNHj9Y777yT4Gf4aJj22Wef6ddff1W1atVMztva2qphw4bau3dvss8BAKQP+/bt088//2wc161bV9lzZH+qPuJPQbx7925KlQY8E0ZgAQAAs8qcObM++ugjffTRRwoKCtLVq1f14MED5ciRQy4uLgl2z3tUyZIltXLlSkVEROjChQu6e/euMmfOrLx588rFxSVB+/Xr1yfb39ixYzV27Nhk2zRu3FiNGzdWSEiI/P39FRYWpqxZs6pgwYKJBmmhoaHJ9hcnV65c2rBhg65fv65Lly7J0dFRRYoUka1t7H+yrVixQitWrEj03mzZsmnFihWaNWuWLl68qEyZMilPnjwJFvCVYkeSNW3aVEFBQbp06ZKsra1VsGBBkzVP4qtUqZIxvRAAYNlu3Lih5cuWa8yYMYqKipIU+wXLZ59/9sR9REdHa+XKldqxY4dxrmjRoileK/A0CLAAAIDFyJ49u7Jnz/5M99rZ2al48eIpW9BjODo6qlSpUineb65cuZQrV9LrlCQnR44ciYZWiXmenzcAwHJ89NFHGjJkiEJDQ3X79u0E178e87UqVar0RH1ER0frxo0bCRaA79q1a0qWDDw1AiwAAAAAANKxuE08HuXm5qaJEyeqSdMmz9yHJHXo2EG93uv1zPUBKYEACwAAAACAdM7a2lqOTo5yy+mmcuXKqf5r9dWyZUtlyZLlqfvKnDmz3N3dValSJXXu3Fl169VN8XqBp0WABQAAAABAOlK6TGkFBQeZvQ8gLbELIQAAAAAAACwaARYAAAAAAAAsGgEWAAAAAAAALBoBFgAAAAAAACwaARYAAAAAAE8o8t49c5cAZEgEWAAAAACQTtk5O5sc3/X1NVMlGcejP2Mraz5WA2mBP2kAAAAAkE5lyZXL5PjC8hVmqiRjuLxhg8mxbdaskpWVmaoBMhYCLAAAAABIp5w8PU2OwwIDdWzkKDNV82K76+urU5Mmm5wr3LaNmaoBMh4CLAAAAABIpzI5Oyvvm2+anLu8caOOjRyliJAQM1X14rm2e7cO9PmfIkNDjXO2WbMqv5eXGasCMhYCLAAAAABIx0r17yfHYsVMzl3euFF7OnfRLW9vM1X1YogICdGxkaN0+NPBJuGVFPtzt/fwMFNlQMZDgAUAAAAA6Zido6Mqjx8Xux5TPGGBgTr4vw90oM//CLKeUlhAgHx/nKvtLVvp8saNCa6X7PeR8jH6CkhTtuYuAAAAAADwfOw9PFT/5590oM//FHL2rMm12z4+Ovi/D2SfO7eKtGsr91q1GDmUhGt79ihw565EQyspdtpgqf79CK8AMyDAAgAAAIAXgJ2jo2ovWqgzc+bI98e5Ca6HBQbq1MRJOjVxkpw8PZW/iZdcypdPsBB8RhIREqLbR48qcOcuBe7enWCaYHyOxYqp3NAvM/TPCzAnAiwAAAAAeIEU79FD+b28dHTESN0+ejTRNnd9fXVq4iRJkn3u3HLy9FTOShXl5Okpl/Ll067YNBYWEKC7Z8/q5uEjuuvrq9s+Po+9h1FXgGUgwAIAAACAF4y9h4eqz5yhW97eurR+g65s2pRk27DAQIUFBuranj3GOZfy5ZWzYkU5FfeUXbZs6TLUCgsIUFhgoO76+hqBVVhg4BPfb+/uruI9e8i9dm3ZOTqmYqUAnoRVUHBQjLmLgGVyd81p7hKARFWrXl1r168zdxnAC6dqxUq6ePGiucsAEsicObP8r14xdxlAuhYWEKDzy5br2u7dCrt27Zn6sM2WTU6ennLy9FQmJye5Vihvcj6tRYSEGOt93fX1VURIqG4eOaLI0FDd9fV95n7da9VS/iZecq9dO6VKBZACCLCANDBp4iTdvnNbI0aMMHcpAAAAyODu+voqcNcu3TrineQUw2f1aJiVs2LF5+7z/tWruh8QIEnPHU4lxt7dXS4VKihnxQqMtgIsGAEWkAby58uvkJAQBQUHmbsUAAAAwMQtb2/d8vbW3TO+unvmzDOP0EovXMqVk5Onp5xLFJdrhQrsyAikEwRYQBrwyO2hsLAwXQ24KgcHB3OXAwAAACTrlre37vr6Kvj0GYUFBKT4SK20YO/uLnsPD7lWrCDn4sXl5OlJWAWkYyziDgAAAAAw4VqhglwrVEhw/pa3tyJDQxV85owi7obErj0VGmqsRZWWbLNmlZOnp+wcHWMXm3d0/O/YDGtyAUhdjMAC0gAjsAAAAJBR3Lt82XgdcfeuIoPvJtn2dmCgQmNiVCCZkVH2+fMZrzM5OcnOySllCgWQrjACCwAAAACQYrLmy/f4Rv9qXbeefHx8dMb3jHLlypWKVQFI76zNXQAAAAAAIGO6ezd2dFZwcLCZKwFg6QiwAAAAAAAAYNEIsAAAAAAAAGDRCLAAAAAAAABg0QiwAABAuhIZGSl/f38FBAQoOjra3OW8EEaOGKk+7/cxdxkAAABJIsACAADpwsGDB9W2TVvlds+tMqXL6OWXXla+vPnUp08fXbhwwdzlpapvv/1W7rnclds9ty5dupRku6VLlso9l7v27dv3xH1HRERozo9z5JrTNSVKBQAASBUEWAAAwOL9+OOP8mrspSNHjqjXe700efJkfTfhO73d+m398vMvqlunrnbt3GXuMlPNyhUrlT17dj148EA/rfkpyXaRkZEKDw9XTHTME/e9b+8+BQcFy8vLKyVKBQAASBW25i4AAAAgOd7e3vp00KeqVKmSVq5aKWdnZ5PrH/f/WM2aNVP37t114OABubm5manS1HH82HGdOXNGEyZO0Nwf52r16tX6qN9HKdb/hg0b5ObmpipVqqRYnwAAACmNAAsAAFi0MV+PkSTNnjM7QXglSYUKF9KkyZPUskVLzZo5S18O/dK4tnLlSl2+fFkff/yx/vzzTy1dslRXr16Ve253tWzRUnXr1U30mf5+/lqyZIn++vsvRUVG6ZVXXlGXrl2UN29ek3bjx49XmTJl1KBBAy1atEi7du5SWFiYXnr5JfXo0UP58+dP0Pdff/2llStWytfXV5kyZdLLJV9Wz549lSNHjkRrWb1mtWxsbNSsWTPduXNHI0eM1OnTp1WiRIkn/Akmb9OmTWr0ZiNZWzMwHwAAWC7+SwUAAFis4KBg7dy5U3Xq1FGBAgWSbFe/fn3ly5dPP//8s8n59evXa87sOZozZ46aNmmq8xfOy9raWhvWb9Bbb72l4V8NT9DXhg0bVK1aNS1atEiZ7DLJLpOdfvjhB71a/VUdPnzYpO2smbO0Y/sOdWjfQePGjlNEZIRiYmI0a+Ys1a9XX9evXzdp/80336hWzVpatWqVbO1s9fDhQ02ZPEU1a9RU0J2gBLXExMTo559+Vq1atZQzZ061aNFCkrRm9Zon/REm66jPUV2+fFmNGzdOkf4AAABSCwEWAACwWGd8zygiIkLly5d/bNuyZcvq/PnzCg8PNzkfEBCgGdNn6I8//tDatWu1bPkynTh5Qo0aNdLEiRO1f/9+o+2VK1fU+73eqlq1qv48/Kd+nPujFixYoEN/HFLWrFnV94O+CZ67aNEiZc+eXT5HfbRkyRKtXLVSq1at0o0bN7R48WKjXWRkpH75+Rd9+OGHOn7iuBYsWKAlS5do06ZNunLlilavWZ2g7wMHDujSpUtq2bKlJKlIkSIqW66sVq9O2PZZbNy4UQ4ODqpXr16K9AcAAJBaCLAAAIDFun4tdgRTTrecj23r4uoiSbp27ZrJ+ZiYGI0cNVIFCv43gsvBwUETJ02UjY2Nli1bZpxfsmSJQkJCNG78OGXNmtU47+7urp69eurvv/9OsOOhnZ2dJk6aKHt7e+Nc7Tq1lTNnTp06eco4Z2trq/Ub1mvYV8NkY2NjnHfOHjstMrGdFNesXiNbW1s1adrEONfirRY6f/68vL29H/szeZyNGzeq/mv1lSVLlufuCwAAIDURYAEAgBeCsYbTIxvw2draqkGDBgnae3h4qESJEvLx9jHOHdh/QI6Ojrp37558fHxMftlYx4ZOly9dNumnQoUKcnBwSNB/jhw5dO/evQTnJCksLEwHDhzQpImT1KxpM0lSdFS0SdvIyEj98ssvqlu3rlxcXIzzcdMIn3cUlt9FP508eZLpgwAAIF1gEXcAAGCx3HLF7ih4+9btx7a9efOmJCmXey6T89mzZ1fmzJmT7P/M6TMmfYSEhKhe3aSn1EVHmwZNrq6uSbaNiTFN0/7++28N/2q4tm7dqsjISBUoUEBt2rTRlClTEty7Y8cO3bp1S6+88op8fHxMrhUpUkQ///SzRo0a9cyLr2/cuFE2NjZq1KjRM90PAACQlgiwAACAxfL09JStra2OHj362LYnjp9QwUIFE0yHe/DgQZL3hD8INxk9lSVLFhUtWlQbN21M8h43NzeT4ycNkK5du6ZGjRrJPZe7FixcoBqv1lD2HNklSTNmzEjQPm6h9kmTJmnSpEmJ9rlv7z7Vql3riZ7/qA0bNqh69eomo7sAAAAsFQEWAACwWDly5FCtWrW0c+dOXb16VXny5Em03d49e+Xv76+P+n2U4FpoaKguXrioQoULmZyPiorS6dOnTRaIL1SokE6cOCFnZ+cUXxdqy29bFBwUrAXzF6huvbom9T061fDBgwfasGGDateprbFjxybo6/at22rSpIlWr1n9TAHWnTt3dODAAY0aNeqp7wUAADAH1sACAAAWbdCngxQZGan33ntP9+/fT3D96tWr+uijj5Q9e3b17t070T6mTp2a4NyaNWt0584dvdn4TeOcl5eXHjx4oPnz5ydov337dvXs0TPZEV3JiRuplc0xm8n5X37+JUHbzZs2KyQkRG3btlXJkiUT/KpZq6YqVqyotb+s1cOHD5+6lk2bNikqKor1rwAAQLrBCCwAAGDRqlevruHDh2vo0KGqWqWqOnToIM/inoqKitLRo0e1ZPESRUZGau68ufLw8Ehwv4ODgzZu3KgH4Q/U5p02ymKfRbt27dKE7ybopZdeUufOnY22zZo3U82aNfXZkM907uw5NXqzkbJly6Y/Dv2h8ePHq0qVKs88MqtqtaqytbXVqJGjNG78OLm4uGjLb1v07bffKm/evPrjjz/0xx9/qEqVKlr9//buO7yn8//j+Cs7BIkRJLFXtXat2FtjK7UVVR0UrVKrVZRSOmhtWpQmiD1i1qq9SsUoasWIGJEQYmT8/sgv5+sjCTE/J/F8XFeu63Of+z7nvE+Sq+Xlvu+zcIGcnJzUoEGDJK/XrFkzffnll1q/fr3q1auX5LjErFy5UkWLFlXuPLmf6lkAAABeNgIsAABgej169lDRYkX1008/adSoUcbm6I6Ojqpfv7769e+n119/PdFz3dzctGTpEn3S7RM1bhz3xj9bW1vVq1dPY38ea7HBu62treb5z9OgrwbJz89P06ZNkyQ5OTmpRcsWGjVq1FM/Q4ECBfTLL79owMABKl+uvCQpV+5cmjZtmk7+d1KfffaZGjZoqCNHj2jd2nWqXae2XF1dk7xe07eb6quvvtKC+QueKMC6c+eONm7YqE8++eSpnwUAAOBlswkLD4t9/DAAz8Iju4ciIyN1Mfhioq9aBwAk361bt3ThwgU5ODjI09MzyTcMSlKHDh20d89eHTl6RJJ06dIlhYaGysvTS65uSYdDknT37l2dP39eMTEx8vLyem7//b5//77Onj0rOzs75cmTRzY2Nsbx6Ojo57731sNWrVqlNq3baPPmzSpRssQLvRcAPE7pN0vr5MmT2rN3jwoWLGjtcgCYGDOwAABAiuLi4qJChQo91bnZs2dX9uzZkzXWyclJ+fPnf6r7PIqDg4MKFCiQ6HEHB4fnfr+HrVy5Ujly5CC8AgAAKQoBFgAAwCsksQ3tAQAAzI63EAIAAAAAAMDUmIEFAABSrX79+unGjRvWLgMAAADPiAALAACkWkWKFLF2CQAAAHgOWEIIAAAAAAAAUyPAAgAAAAAAgKkRYAEAAAAAAMDUCLAAAAAAAABgagRYAAAAAAAAMDUCLAAAAAAAAJgaARYAAKnAnTt3dOb0GQWdDdKdO3esXQ5SoF9//VWVKlZS7ly5VapkKX399dfWLgkAAMBgb+0CAADA0zt16pQGDRqkdWvX6d69e5IkR0dHVatWTYMGDVLxEsWtXGHq0uKdFvrrr78eO+7tt9/W5CmTX0JFz8fsWbPVp3cfNWnaRF27dtWZM2cUGxtr7bIAAAAMBFgAAKRQISEh8nnLR9HR0erdp7eKFikqSTpw4ICmT58uHx8f7d6zWzly5Hgp9Zw8eVJNmzTVgIED1LZt25dyz5et03udVKduHaO9/+/9mjNnjrp06aJCrxUyjhcoUMAa5T21ef7zlDtPbs2YMUO2tkzQBwAA5kOABQBACjV79mxdvnxZASsDVKlSJeN4g4YN9MGHH2jmzJkvLbySpNu3buvcuXNSKp6406BBA4v2vHnzNGfOHPnU81Ht2rWtVNWzu3vnrtyzuBNeAQAA0yLAAgAghTp37pwkqUyZMgn6smbNqr59+yZ6XtDZIPn6+urI0SOKjopW0aJF1bFTR3l5eVmMGz16tIoXL67atWtr9uzZ2rxpsyIjI1X49cLq0qWLcubMKUmKiYnR119/rWtXr0mSFi1apCNHj0iScufOrQ8++MC4ZnR0tAJWBGjN2jW6cvmKsmTJIh8fHzVq3Eg2NjbGuN27d2tlwEr1H9BfZ86c0YzpM3T69Gm5urrqLZ+39M477yT6bEePHpWfn59O/ndSzs7OKlqsqNq3b6+sWbMmGLtnzx4tXLBQZ86cUZo0aeRdwVvt27eXi4tLkt/zJ3X58mVNnDBRrdu0VhrnNPL19dXVq1dVpmwZi1lqGzZs0IrlK3ThwgVlypRJ5cuXV7v27eTg4JDotbJmzaqpU6fq4D8HZWtnq/Llyuv9Lu8rbdq0FvcPDg7WjOkzdPjwYcXExChPnjxq1aqVSpYqKUnavGmz1v25TteuXdPVq1f11VdfGee+9957yp8/v6S4n/GqVau0Zs0ahVwKUeYsmVWzZk01adLEokZJ8vf3V2hoqD7++GMtXLhQASsCdPfuXZV6s5Q++eQTpUmTRsePH9dvv/6ms2fPysvLSx07dkx0uevT/L7s379fy5YtU3RUtN5u9rYqVKggSVqxfIVWrlqpK5evKEOGDPKu4K13331Xzs7OT/nTBQAALxP/zAYAQAqVP19cuLB+/fpknxMQECBvb2/Nnj1bjg6OcnB00NSpU1WxQkXt3bvXYuzkSZO1ccNGtWvbTqO+G6X7UfcVGxuryZMmq2aNmrp8+bIxNjAwUCdOnJAknT9/XoGBgQoMDNSpU6eMMZGRkXqn+Tvq2LGjgoKC5J7VXcePH1eHDh3U5f0uFnsuHThwQGPHjtX69etVq2Yt7d+/X+nTp9f+/fvV5f0uGjliZIJnmzhhoipXqqy5c+YqJiZGV69d1Xcjv1PFChUVeDDQYuzwYcNVp3YdbdmyRZkyZVJERIS+HPilatWspevXryf7+/k4V69e1dixY7Vh/QZVq1ZNU6dO1Zo1a7Rq1SpJcZvvt2vbTs3ebqY9e/YoXbp0uhh8UZ9//rnat2uf6LV27tip6tWra/GixXJyctLVK1c1aNAgtWzR0mL8mdNnVKliJc2ePVtuGd3kntVdq1atUs2aNbVs6TJJUsjlEAUGBioiIkJ37twxfm6BgYG6FXFLknT79m290/wdtWvbTtu2bpONjY32/x33c6hfr75CQ0Mt7rtixQr5/uGrQYMGaUD/Abp7765CQ0M1fNhwffzxx9q1a5dq1aylgwcPysHRQYsWLVLNmjUVGGj5M3qa35cF8xeoQf0GWrpkqfz9/XXw4EFJ0oABA/Tuu+8q+GKwcuXKpVu3bmlA/wHyectHUVFRz+EnDQAAXjRmYAEAkEK92+Fd/fLLL+r8Xmd1+aCLGjdurFKlSiWYERPvwoUL+vijj1W+fHn94fuHMdMoJCRENarXUI/uPbRj5w6Lc2bPnq1GjRpp/4H9SpMmjSTpr81/qXHjxvrjjz/0+eefy9bWVkuXLlXgwUBVqVJFPXv2VNt2CffAGjZsmDZv3qwFCxeoVq1axvHvv/9e3w7/Vo0bN1aTpk0szun9eW8tWLjAmEUTHR2tpk2basKECerzRR/jWbf8tUVffvmlmjVrpomTJsrJyUlS3Gyzhg0b6qefftKMmTMkSStXrtQPP/ygPn366MuvvjRm8mzbtk2NGjbSiG9H6Psfvn+yH8ZjDB8+XMOGD1Pnzp1lY2OjyMhISdKl4Evavn173Pe5cSNj/KSJkzRgwAD9+++/Kly4sMW1Bg4cqMFDBuujjz4yjo34doRGjx6t3bt3q1y5cpKkmTNnKiIiQnv27lHmzJklSVFRUfr5559VoWLc97Nly5Zq2bKlOr/XWVu3btXSpUsT1D7sm2HauHGjxo0bp3c7vGscX7Z0mbp06aI+vfto+ozpFuccOnRIWbJk0d59e5UhQwZJ0ldffaXx48Zrx/Ydmj5juurUidtLLOhskMqWLavffv1NY38e+7/7PsXvy4gRI7Ry1Up5e3srOjpa0dHRioiI0NQpU9W1W1eNGDHCGHtg/wEdPnxY9vb8cRgAgJSAGVgAAKRQGTNm1Pr161WhQgVNGD9BdevUVZ7cedTinRaaP3++YmJiLMb7+vrq5s2bGjV6lMUyuWzZsumDDz/Q0aNHdfr0aYtzHBwcNGbsGCO8kqSq1aoqS5YsOnzocLJrjYyM1KzfZ6lp06YWYYQkffbZZ0qfPr1WrlyZ4LwuH3QxwitJsrOzU5MmTRQREaEzZ84Yx8eNGyc3Nzf9/MvPRnglSbly59KixYs0afIk49jkyZPl6emp/gP6WyxDq1SpkmrUqJFoHc/K29tb77//vnG/+O9nnrx59Pf+vy3CK0lydXOVpAQ/D0kqV66cRXglSc2aN5MkHT78v5/Jvfv3ZGdnZ/H9sLe3V+/eveXu7p6suiMjIzVr1iw1adLEIrySpMZNGqtjx45asmSJQkJCLPpiY2M18ruRRnglSXVqxwVW1atXN8IrKe5nVOi1Qjp12nK23tP+vnh7e0uK+11xdHQ0gqx0LuksxpYsVVLt2rdL1vcBAABYH//kBABACpY7T24tXrJYFy5c0Lq167R161at+3Od1q1bpxnTZ2jhooVGWLJj+w6lT59et27d0v79+y2uY2drJ0k6f+688ubNaxx/8803E+yrJMWFZ7du3Up2nQcPHlRERIQ8vTwT3FuS3N3ddf78+QTHq1Spkui9JRlL3CTpr7/+Ut236ipdunQJxj/8RsCdO3aqXPlyCZasSZKrq6suXryo6Oho2dnZPf7Bkil+xlNi4p8nNDRU+/bt019//aXfZ/4uSYqJjkkwvkrV5H1PGjVqpIkTJqpBgwb68IMPVbVaVWPfsuQ6ePCgbt26perVqyfaX71GdU2bNk27d+22COHSp0+fYOZYWpe436Ny5csluI5LWhfdvnXb4r5P8/vyYNgZz9XVVdWrV9fYsWMVcStCDRs2VOnSpS2CPQAAYH4EWAAApAJeXl7q9F4ndXqvk27fvq1hw4Zp0sRJGjtmrAYMHCApbg+lmzdvqkb1Gkle5+FZW/FLzxLz4B5Ej3P1ylVJ0vhx4zV+3PhEx2TLli3BsUfe//9fdxgeFq47d+7II7vHY+sIDwvXvXv3tHXL1kd+H563LFmyJNkXEBCg70d/rwMHDsjOzk5vFHlD7dq306SJkxIdnzlT8n4mFSpU0Jy5czRy5Eh98sknkqR8+fLpvffe0yfdP0nWGwfjf27uWROfsRW/Of6Vq1csjqdPnz7Ja7q5uSX7vk/6+5LU9/n3Wb9r8NeDNXvWbE2cELfEtE7dOvryyy/1+uuvP7YeAABgfQRYAACkMmnTptWIESPk5+enNWvWGAGWs7Oz8ufPr5Wrkl4i9/DSsuSEHMnhnCbuTW9jx45Vvfr1Eh3z4DLFJ7l/mrRpZGNjo9uRtx87Nr6Otm3bavCQwYmOsbe3f66zr6T/zXB72J9//qn27dqrbt26Wr1mtUqWLClnZ2ft378/yQDrSX4m9erVU7169XT+/Hlt27ZNCxcs1KBBgxQaGprk8z8oTdq4n8ndu3cT7b97J+54YrP0kvLgss2kPO3vS1I/N1dXV439eaxGjR6lffv2afOmzfrtt9/k85aP9uzdk+hbKgEAgLkQYAEAkEIdP35czk7OypU7V4I+GxsbZcuaTeHh4caxPHnyKDAwUK6urnJ2dn6ZpSpPnjySpEuXLiU6c+ZZODo6Kk+ePDpw4ECi/deuXVOaNGmUNm1aOTk5ycPDQ0FBQc+9jqcx33++7O3tNWPmDIsQ6ME3PD4POXLkUKtWrdSqVSs1b9Zcc+bMSVaAlS9vPknS0SNHpWYJ+w8fidtz68Flp8/Di/p9cXJyUsWKFVWxYkXVql1LdevU1cqAler0Xqfndg8AAPBisIk7AAAp1Lvt31W7du106dKlBH1HjhzRyZMnVaJECeNYgwYNdOfOHc2cOTPB+A0bNuiDLh/ozp07T11PuvRx+09dv349QV/+/PlVuHBhYyP5B8XExKjze531559/PvW9W7RoocCDgVq7dm2Cvs8//1zNmzU32g0aNND27dt18J+DCcZ+//33Gjtm7FPX8aRsbW3l6OiYIFBcvGjxM197zpw5WrVqVYLjGTJkSHJG1cPy5M2jYsWLac6cORZhqKS436UZM+Xp6amyZcs+c70Pep6/L+Fh4er7Rd8Ez+zqGrdR/t17yfteAAAA62IGFgAAKVTXrl3Vq1cvVaxQUW3btVWpUqVkb2evo/8e1bSp0+To6KjPP//cGN+4SWNVrlxZAwcM1Mn/Tsqnno/SpUun3bt2a/To0SpXrtwzzczKmTOnMmfOrGnTpqnw64UVGxurwMBA9erVS5I08ruRat6suapXq67evXurQMECunr1qn6d9qs2b96sDz/68Knv/elnn2rJkiXq2KGj+nzRR5UrV1ZERIR+//13LVu6TFOnTTXG9u3XV0uWLFHDhg3Vu3dvlSlbRtFR0VqxYoWmTZumrwd//dR1PKnKlStrzpw5GjRokD755BNFRUVp+m/TdfjIYdnY2GjturUqWKigChUq9MTXnjRpkv49+q+6d++uWrVrydbWVps3bdbSpUvVtl3bZF/n+++/V6OGjVSjeg317t1b+QvkV1BQkH75+Rf9+++/+uOPP57bUtMHPa/fl23bt2nq1Knas2ePuvforjx58ujihYv64ccf5OjoqLp16z732gEAwPNHgAUAQArV6b1OKlCwgL7//ntNnjRZUVFRkuKWD3p7e2v4t8NVvERxY7ytra3m+c/ToK8Gyc/PT9OmTZMUt6yqRcsWGjVq1DPVY29vr/ETxqtb127GjKdSpUqpe/fucnBwUI0aNbRg4QJ9PehrdevWzTivUKFCmjtvrry9vZ/63i4uLlq9ZrX69+uvUd+N0rB7wyRJnp6emjhpolq2bGmMzZo1q9b9uU5f9PlCQ4cONTauz5w5s4YOHaqen/Z86jqeVNt2bRUYGKjJkyZrwvgJkqRKlSpp/vz5GjlypH6f+btOnjypFStWPPG1Fy1apO9GfqeJEyfqp59+khS33LJVq1ZP9LP29vbWsuXLNHDAQGMzeEl6/fXXNXfeXL311ltPXFtyPK/fl/r168vXz1cjR4zU+53fN47nyZNHf/j+8dyXPwIAgBfDJiw8LPmvEALwVDyyeygyMlIXgy8+0Ua3AJBct27d0vnz5xUTEyNPD0+5urk+cvzdu3eN8V5eXs/1v0337t3T2bNnlTZtWnl6eia6afeVK1d0+fJlZcqUSR4ej3974JOI/164uLjI09PzkbODbty4oQsXLihtmrTyyuEle3vr/NverVu3FBQUJDc3N4vvR2RkpBwdHZ9pU/m7d+/q9OnTkuJmybm4uDz1ta5evarLly8rc+bML3UPsef1+xIcHKyrV6/Kzc1NOXPmfI4VAnhapd8srZMnT2rP3j0qWLCgtcsBYGIEWMBLQIAFAAAAJESABSC52MQdAAAAAAAApkaABQAAAAAAAFMjwAIAAAAAAICpEWABAAAAAADA1AiwAAAAAAAAYGoEWAAAAAAAADA1AiwAAAAAAACYGgEWAAAAAAAATI0ACwAAAAAAAKZGgAUAAAAAAABTI8ACAAAAAACAqRFgAQAAAAAAwNQIsAAAAAAAAGBqBFgAAAAAAAAwNQIsAAAAAAAAmBoBFgAAAAAAAEzN3toFAAAA6ypTspTOnTtn7TKABKpVqyb/RQutXQYAADABAiwAAF5x586d0y/jx+u1woWtXQpg2LRxo/6YNcvaZQAAAJMgwAIAACpVqpTKlitn7TIAQ8ilS4qNjbV2GQAAwCTYAwsAAAAAAACmRoAFAABSpPDwcF27dk0xMTHWLgUAAAAvGAEWAABIEe7fv69ff/1VNWrUUJo0aeTm5qYsWbLIwcFBZcqU0bfffqsbN25Yu0wAAAC8AOyBBQAATO/cuXNq3LixDhw4kKAvJiZG+/bt0759+zR+/HgtW7ZMZcuWfflFAgAA4IVhBhYAADC127dv66233rIIr9KlS6fq1aurbt26yp49u3H80qVLqlOnjs6cOfPyCwUApAoLFy6Uz1s+2r59u7VLAfAAZmABAABT++6773T06FGj3bFjR40fP17p0qWTFDcD68cff1Tfvn0lxe2N9dlnn2nJkiXGObdv39bly5clSTY2NsqdO7fFPW7cuKHQ0FBJkoODg7y8vBKt5fr169q7d6+uX7+ujBkzqkyZMsqYMWOCcWFhYQoLC7O4V1RUlLZv367Lly+rdOnSsrGxMcZnyZLFeJ549+/f14ULF4y2u7u7XFxcHv3NAoBU6puh32jx4sUJjqdJk0YeHh6qU7eOOnXqJGdn52e+18ULF7Vz505dD73+zNcC8PwwAwsAAJhWTEyMJk+ebLRLlSql6dOnW4Q9tra2+uKLL9SpUyfj2PLlyxUUFGS0V65cqbx58ypv3rzKnz9/gvtMnz7d6K9WrVqC/vDwcHXq1Enu7u6qW7euWrVqpbp168rd3V3vv/++IiIiLMb/8MMPyps3r/LkyaNTp05p9+7dKliwoKpVq6YWLVpo165dKlasmHHPfv36Jbjn3Llzjf68efPq/PnzT/S9A4DU5MrVKzp9+rTc3NyUL18+4yt9+vTau2+v+vfrr2pVq+n69Vc3dDqw/4A+6PKBVq9ebe1SgBeCAAsAAJhWYGCgrly5YrS7du0qW9vE//jy8ccfG59jYmK0efPm51JDZGSkqlevrt9//13R0dEWfdHR0Zo+fbp8fHx0//79RM+fPn266tata7GsMWfOnGrTpo3RXrhwYYK3Kc6fP9/47O3trddee+05PA0ApGyDBw/WwkULja81a9foyJEjatq0qY4dO6Zvhn5j7RKt5siRI5o/f74uh1y2dinAC0GABQAATOvw4cMW7fLlyyc5tlSpUrK3/9/uCIcOHXouNYwcOdLYf8vd3V3r1q1TRESENmzYIA8PD0nStm3bNGvWrETP//bbbxUeHi5HR0cVKVJE3t7e8vT01EcffWSMCQkJ0V9//WW0b968qbVr1xrtB2eXAQAsubi4aMTIEZLiZtw+Stj1MB07dkyXLl16GaVZiIyM1MmTJ/Xff//p7t27yT4vKipKQWeDdOLECd25c+cFVgiYGwEWAAAwrYeXgjy4YfvDHB0d5ebmluS5T+vXX381Pg8aNEi1a9eWi4uLatSooSFDhhh9D86Yeli1atV08uRJHTp0SDt27FDevHlVunRpvfnmm8YYf39/4/OyZcuMv9w4OzurdevWz+VZACC18vT0lKOjo7Gf4cMCVgSoatWqypMnj8qXK6/CrxVWqZKlHvnf7gft3LlT2bJm03ud3ktyzP379/V64ddVrGgxRUVFGccPHz6s1q1aK1fOXCr9ZmmVKV1GuXLmUs+ePRMNpLq830XZsmZTdHS0fv31V71e+HUVL15cZcuUVe5cufVFny907949Y/yE8ROUOVNmde/eXZL02WefKXOmzMbX1q1bk/WMgNmxiTsAADCt2NhYi/aDG58n5sH+pJYaPomzZ88qODjYaJ88eVJjx4412qdPnzY+Hzt2LNFr2NnZac6cOcZsrQd9+OGHxtLHRYsWady4cbKzs9OCBQuMMW+//bZcXV2f9VEAIFULDg7WvXv3Et3ncNLESRowYIDy5MmjoUOHKlfuXAo6G6SpU6fqgy4f6ObNm+rcufMjr+/t7S0vLy8FBAQoNDRUmTJlSjBmzeo1Cg4O1ifdPzFmBK9Zs0bvtn9XDg4O6tixo0qUKKGo6CgFrAjQrN9nydXVVcOGDbO4zr3793T37l1NnTJVI0eOVIeOHfTGG28oPCxcs2bN0rRp0+Tq6qqvBn0VV1sFb3016CsdOHBAS5csVYOGDSz+geThF5cAKRUBFgAAMK2H3/B35coVubu7Jzo2KipKYWFhRjuxwCgpDwdl8R5eYvLzzz8neY2HN3KPly9fviRradu2rfr06aOIiAiFhIRo8+bNKleunMUGvCwfBIBHi4yM1KCvBkmS3n//fYu+Y8eO6euvv9abb76pgJUBSpMmjdHXoWMHVapYSd8M/UatW7dW2rRpH3mftu3aaviw4fL397fYdzHenDlzJEnt2rUzjjk6Oiq7R3YtXrzYIlzr2LGjypQuo4ULFiYIsOKNHj1aGzdttDivTZs2KlGihGbNmmUEWKVLl1bp0qXl5+unpUuWqk7tOurQscMjnwVIiQiwAACAab3++usW7X379umNN95IdOzBgwctNlLPmTNnsu9z+/btZI0rW7asHB0dE+17cPnigzJkyJDk9dKnT682bdpo2rRpkqQFCxbo6tWrxpKSHDlyqHbt2smqDQBeBcOHD9ekSZOMdkREhI4ePapbt26pT58+6tqtq8X4P/74Q/fv31e//v0swisp7h9J2rRpox9//FG7d+1W9RrVH3nvNm3aaMS3I+Tr65sgwLp69arWrl2rUqVKWfx/qkaNGtqxY4dcXFwsxtvY2CgmJkYXL17UvXv3Ev1/S4+ePRLMKHPL6KayZcvqzz//1PXr1xP8Qw+QmhFgAQAA0ypevLgyZsxo7Gf122+/6d1330107G+//WZ8trOzU4MGDYz2g0sLo6OjFRkZafEXmePHjyd6TS8vL4v2Tz/9pMqVKz/5gzzCRx99ZARY69ats+jr0KHDc1kKCQCpxenTp3Xx4kVJ0p07dxQaGiobGxstWrxINWrUSDB+65a4/Z9KliyZ6EzZ+OV158+ff+y9vby8VKNGDa1fv16BBwNVrHgxo2++/3zdv39f7dq3S3Deg+FVcHCwdu3cpZkzZxpvp334LbTxqlevnujx+NDq5s2bBFh4pfAnIgAAYFr29vYWy0E2b96sAQMGWGyOK0kzZszQlClTjHbt2rWVLVs2o50uXTqL8du2bTM+X7p0ScuXL0/0/jly5LD41+/JkycnOi4+FHsaD27mfurUKW3YsMHo69ix41NdEwBSq99++01Hjh7RkaNHdOr0Ke37e5+KFiuqNq3baO/evQnGh4SESJJeK/SacnjlSPDVs2dPSdLde8l7K2B8QOXr62tx3G+On5ycnPRO83cSnHP06FH17NlTRYsU1euFX1enTp107tw55cmT55H3ypo1a6LHjX+USXz1O5BqMQMLAACY2pdffil/f38FBQVJkr777jv98ccfqlixouzt7bV//34dPXrUGO/s7KxRo0ZZXKNEiRKysbEx9rpq166dunbtqrt378rPz++Rbyzs1auX8WYnX19f3b59Wy1btlSGDBl08eJFHTp0SAsXLtScOXOeenZW/GbuMTExxmbwFStWVKFChZ7qegDwqsifP78mTpyoKpWr6Ksvv9LqNast+mNjY+Xk5KRhwxPfZypeUrOdHtagQQNlzJhR/v7++mbYN3J0dNShQ4cUeDBQzZo1k1tGN4vxAQEB6tSxk5ydndWseTOVLVNWRYsWVfESxdWpUydjFlZiHBwcklUT8KogwAIAAKbm5uamVatWqX79+jp79qykuKUe/v7+CcamS5dOc+bMUYkSJSyOZ8+eXa1btzY22L18+bKGDh1q9NepUyfB8r143bp109atWzV37lxJ0uLFi7V48eLn8mzxHtzMPR6btwNA8hQrVkylSpXSzp07dfLkSYuZs+5Z3RUcHKx27dol2IfqaTg5OalFixaaOnWq1qxeo0aNGxmzsRJbPvjVl3EbrW/+a7Py5ctn0Xcn8s4z1wO8SlhCCAAATO+NN97QP//8o8GDByf6inRJcnV11YEDB9SwYcNE+6dOnapOnToZrzaX4vYl+eyzz7R8+fIEoVc8Gxsb+fn5acqUKQk2lY+/b6dOnVSgQIGneLI48Zu5x0uTJo1atWr11NcDgFdN4yaNJUnLli2zOF6+XHlJ0qZNmxI97/bt27p8+fIT3Sv+LYO+vr6KiorSgvkL5OnpmWAPrrt37+r06dN6/Y3XE4RXkixmDz8X/7+yMKk9tYCUjgALAACkCK6urhoyZIj+++8/Xb58WQcPHtTw4cON/vDwcI0cOTLJ89OlS6cZM2bo+vXrOnDggA4dOqSrV69qzJgxcnJy0oEDBxQbG6v//vsvwbk2Njb68MMPdeTIEYWEhOjAgQPau3evTp48qatXr2rGjBnKnj27MX748OGKjY1VbGxsonuyJObBPbSaNWv2yLcXAgAsNW4cF2AtX2a5p2HrNq1lY2OjYd8MS3S5+NeDvlaZ0mUUHBxsHHNydpIk3bmb+AypEiVLqFixYlq3bp1mz56tK1euqE2bNgleuuHk5KSMGTMq5FKIxVtyJcnf31/nzp2TpAT7Oj6t+P8PHT+R+ItJgJSOJYQAACDFcXd3l7u7u4oVK6bTp08bbyD87bff5OHhoWHDkt7rJF26dEnOtkqOrFmzJrmx7tM6fPiw5s2bZ7S7dOnyXK8PAKld/vz5VbRoUf399986f/68cuTIISnuRRnde3TXuF/GqYJ3BbVv31558+ZVWFiYVq5cqW3btqlVq1by8PAwrvV64bjZtuPHjVfEzQi9+eabFm8clKS27dpqQP8B6vVZL6OdmMZNGuv3mb+rY4eO+vDDD+Xk7KQN6zdoypQpatS4kZYvW65h3wxTw4YNVaVqlWf6HpQqVUoZMmTQ9N+myyWti7J7ZNea1Wv0ySefqFr1as90bcAMCLAAAECKNnHiRB09elTbt2+XFDf7ydPTU127drVyZY+2e/duHTt2TBcvXtSYMWOMf52vUqVKsjcTBgD8T6PGjXTo0CGtWLFCH3/8sXF82LBhyp8vv37++Wf98MMPxnE3Nzd98cUX6j+gv8V1qlStorbt2srP10/79+/X559/niDAatWqlQZ/PVj37t2Tt7d3ksvbv/nmG92KuKUFCxZo5cqVkuLecDtj5gwVLVpUx/49pilTpuj8hfPPHGBlzJhRkyZPUs8ePfX9999LiluS3vTtps90XcAsbMLCw3j5JvCCeWT3UGRkpC4GX1TatGmtXQ4AWMiWOYu2bNumsuXKWbuUpxYSEqI2bdro9u3bkiQ7OztNnjxZxYoVe8yZ1jN8+HANGjTI4ljGjBm1efNmU9f9svjPm6eB/ftr3z8HrF0KgBeo9JuldfLkSe3Zu0cFCxZ84fc7f/68rly5IldXV+XIkUOOjo5Jjr1w4YJCQkL02muvPfMG8OHh4Tpz+ozSuqRV/vz5LZYbhoeFK136dLKzs3ume8S7d++ezpw5o+joaOXNm1fOzs7P5bqAtTEDCwAApHjZsmXThg0brF3GM6lYsaImTJhAeAUAL1COHDmM5YWP4+XlJS8vr+dyX1dXV5UomfjydVc31+dyj3iOjo4qVKjQc70mYAYEWAAAAFbw4YcfytvbW3fv3lXBggX5ywYAAMAjEGABAABYQdasWVW7dm1rlwEAAJAi2D5+CAAAAAAAAGA9BFgAAAAAAKt6cFNzAEgM/5UAXgInJydJMl6RDgAAAEC6fv26pLhNzgHgUQiwgJcgS5YskqQrV65YuRIAAADAHKKiohQaGiobGxtlzpzZ2uUAMDkCLOAlcHd3lyRdvnzZypUAAAAA5hASEiJJyp49u2xsbKxcDQCzI8ACXoIs7nEzsA4dOmTlSgAAAABz2P/3fklS8eLFrVrH1i1b5fOWj5YsXmLVOgA8GgEW8BK8+eabkqT1f663ciUAAACAOSxfvlySVPetulat49q1a9q5c6eCg4OtWgeARyPAAl6C5s2aS5I2bdqku3fvWrkaAAAAwLpCQ0O1dOlSSVL9+vWtXA2AlIAAC3gJcuXOpaJFi+ru3buaO2eutcsBAAAArGr0qNG6c+eO6tatKw8PD2uXAyAFIMACXpJu3bpJkr799lvduXPHytUAAAAA1hESEqLp06dLkgYPGfxS733r1i2dOHFCly5deuJzb9y4oePHjz/xm8WvXr2q48ePK+x62CPHRUVF6eyZszp+/LiuX7/+xPUBqR0BFvCStG7TWm+88YYuX76sCRMmWLscAMBzcO/ePW3atEmbNm1SaGiotcsBANOLjo7W+53f171799S8eXMVKVLkpdz35MmTatmipXLmyKmyZcqq8GuF5V3eWxs2bHjsuQErAlSjeg3lyplL5cqWU8ECBVWsaDGN+2WcYmJikjxv+vTperPUmyqQv4DKlS2nPHnyqIJ3BS1atMhiXFRUlIYMHqL8+fOrRIkSKle2nPLmyavq1apr65atz/zsQGphb+0CgFeFra2tRo0apUaNGun70d/L29tblSpVsnZZAIBnEBoaqho1akiSVq1aJR8fHytXBADm1rNnT23dulXZs2fXqNGjXso9T506pdq1auv69euqV6+eqlatKknauHGjWrZoqRYtWiR57vhx4/XVV18pc+bM6t6ju/LkyaNr165p8aLFGjRokPbs3aNZs2YlOK9Hjx6aPWu2cubMqd69eytb9mw6c+aM5s2dp87vdVZYWJg6d+4sSRo8eLAmjJ+g+vXr6+1mbytt2rQ6dOiQpkyeolatWinwUKAyZcr0Yr45QApCgAW8RFWqVtFnn32msWPHqmWLllq8ZLHKlStn7bIAAACAF27gwIHy/cNXkjRv3jxlyZLlpdz3iz5f6Pr16/ppzE9GaCRJXbt11fTp0/V5r88TPe/o0aMaOnSo8ubNqzVr1yhr1qxGX+/evdWxQ0ctW7pMc+bMUZs2bYy+xYsXa/as2apQoYLmL5ivdOnSGX3du3fXW3Xf0tKlS41a/Of5K1++fPL185WNjY0kqUGDBmrTpo2OHz9OeAX8P5YQAi/Z4CGD1axZM926dUuNGjZSQECAtUsCACDFCDobxJIaIIWJjIxUx44dNXHCREnSr7/9qhIlS7yUe1++fFkbN25UsWLFLMKreJ07d5a3t3ei5/r6+ur+/fv6evDXFuGVJNnb22v096Nla2urmTNmWvRNmTxFkvTzLz9bhFeS5OnpqWXLl2nhwoXGMRsbG0VFRyk2NtZibK5cuVS7du1kPyuQ2hFgAS+ZjY2Nps+YrhYtWuju3btq17adxo4Za+2yALwCvhv5ndq1a6fwsHBrl5JixcbGKioqyviKjo42+mJiYiz6Hv6LCJ6dn6+fqlStooYNGyrobJC1ywGQDAEBAapdq7aWLlkqFxcXzZ03V++8885Lu//evXsVExOjatWrJTmm7lt1Ez2+bes2SVLNmjUT7ffy8lLhwoW1e/du3b17V5J0+/Zt7dq1S6+99poKFSqU6Hl58+aVvf3/FkM1aNBAQWeD1LZNW23auMm4FgBLBFiAlUz7dZq+Hvy1bGxsNGTIEL1Z6k35/uFr8ZchAHie/Pz8FLAiQMVLFGcGy1MKCAiQg4OD8ZUjRw6jr0GDBhZ93377rRUrTV3Cw8LVrVs3devWTeFh4Wrbtq1y5c5l7bIAJOHChQuaPn26KlaoqHZt2+nw4cPKlSuX/lz/50vfK/DK5bg3Bnpk90hyjJeXV6LHQ0JClD59erm6uiZ5roeHh2JjYxUSEmKcExsbq1y5kv/fqKHfDNXbb7+tNWvWqGnTpsqVM5caNmio3377Tbdv3072dYDUjj2wACv6/PPPVbVqVfXq1UuBBwP1ySefaODAgapeo7pqVK+h0mVKq1ixYtYuE0AqsWXLFrVr105bt25Vw4YN1X9Af/Xv39/aZQGPFHgwUJ07d9aJEyfk4uKiDz74QLVr1yaEBUwk/Ea4jhw5on+P/quDBw/qxIkTRl/atGnV89Oe6tGjh1xcXF56bfGzYeP3lkrMo/oexzg3Nvn3e1iGDBk0Y+YMDT07VCtXrdS2bdu05a8t2rp1q2bOnKnVq1db5XsHmA0BFmBlZcqU0ZYtWzRjxgwNHTJUYWFhWrpkqZYuWWqMKViwoLJly2bFKgGkJrly5VJQUJC+G/kdIcATKlWqlGbPnm20w8PD1b17d0nSF198oeLFixt9JUuWfNnlpUpdu3Y1/jJ869YtjR07VmPHjrVuUQAeK1euXGrevLk+6f7JS9usPTHu7u6SpIvBF5Mcc+HChUSPZ82aVcHBwbpx44YyZMiQ6Jjg4OC4sdni9sjKnj27JOncuXNPXGuu3Ln08ccf6+OPP9bdu3f1+eefy/cPX82YPkPde3R/4usBqQ0BFmAS7733ntq0aaP1f67X8uX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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "aYpmmfQ-ZbIh"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade langchain openai  -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"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\""
      ],
      "metadata": {
        "id": "5D5W_BeBZf5X"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install unstructured -q\n",
        "!pip install unstructured[local-inference] -q\n",
        "!pip install detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2 -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbL5zdf9aG8a",
        "outputId": "60cf911c-3d70-4b71-edb0-4b04f8a6d6fa"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install poppler-utils  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwalYTVZoRlH",
        "outputId": "9f475bea-721c-479f-c1fb-ea0ad263c9b7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "poppler-utils is already the newest version (0.86.1-0ubuntu1.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/directory_loader.html"
      ],
      "metadata": {
        "id": "2AssnWUVlxtH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import DirectoryLoader\n",
        "\n",
        "directory = '/content/data'\n",
        "\n",
        "def load_docs(directory):\n",
        "  loader = DirectoryLoader(directory)\n",
        "  documents = loader.load()\n",
        "  return documents\n",
        "\n",
        "documents = load_docs(directory)\n",
        "len(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fulYnj9nZr3n",
        "outputId": "2b9f6003-0b72-407a-de44-df9b7e216890"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html"
      ],
      "metadata": {
        "id": "VwaCosqsogzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "def split_docs(documents,chunk_size=1000,chunk_overlap=20):\n",
        "  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
        "  docs = text_splitter.split_documents(documents)\n",
        "  return docs\n",
        "\n",
        "docs = split_docs(documents)\n",
        "print(len(docs))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lF8jA6xZ0Hm",
        "outputId": "7fd4a04f-f80d-465b-a028-e8a611ebb9f4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(docs[0].page_content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVcFjgHJZ8S9",
        "outputId": "c8011cb1-9cd9-4223-ad71-60bbc83f9cbf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "India is a vast and diverse country located in South Asia. With a population of over 1.3 billion people, Indi a is the second most populous country in the world. India is a land of many contrasts, with ancient traditio ns coexisting with modern technologies, and rural poverty alongside bustling cities. India has a rich histor y and culture that dates back thousands of years, and it is home to some of the world’s most iconic landm arks and spiritual sites.\n",
            "\n",
            "Geography and Climate\n",
            "\n",
            "India is a subcontinent that stretches from the Himalayas in the north to the Indian Ocean in the south. Th e country is bordered by Pakistan to the west, China and Nepal to the north, Bhutan to the northeast, and Bangladesh and Myanmar to the east. India has a diverse range of landscapes, from the snow-capped aks of the Himalayas to the arid deserts of Rajasthan, from the lush tropical forests of Kerala to the rugge d coastline of Goa.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(docs[5].page_content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGXlt51N2xyJ",
        "outputId": "733f6ac1-713a-401c-decf-3fd13ba4456b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In the 1950s and 1960s, India and the United States had close ties, with the United States providing aid and support to India's economic development. However, relations between the two countries deteriorated in the 1970s, when India conducted a nuclear test and moved closer to the Soviet Union during the Cold War.\n",
            "\n",
            "In the 1990s, relations between India and the United States began to improve again, with the two countries signing a number of agreements and increasing economic and military cooperation. The relationship has continued to strengthen in recent years, with India and the United States collaborating on a range of issues, including security, trade, and climate change.\n",
            "\n",
            "Economic Relations\n",
            "\n",
            "India and the United States have a strong economic relationship, with bilateral trade reaching over $146 billion in 2019. The United States is India's second-largest trading partner after China, and India is one of the fastest-growing sources of foreign investment in the United States.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#requires for open ai embedding\n",
        "!pip install tiktoken -q"
      ],
      "metadata": {
        "id": "6PiPwt-FaYwl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
        "\n",
        "embeddings = OpenAIEmbeddings(model_name=\"ada\")\n",
        "\n",
        "query_result = embeddings.embed_query(\"Hello world\")\n",
        "len(query_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5GY9voPa0av",
        "outputId": "5fd540c3-8e46-4863-aa07-8fcde3e0d822"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1024"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pinecone-client -q"
      ],
      "metadata": {
        "id": "LXhIY5SrrRec"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pinecone.html"
      ],
      "metadata": {
        "id": "vySq5oI5sU5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pinecone \n",
        "from langchain.vectorstores import Pinecone\n",
        "# initialize pinecone\n",
        "pinecone.init(\n",
        "    api_key=\"f7aa89af-ac99-4619-8914-8d08740f7b38\",  # find at app.pinecone.io\n",
        "    environment=\"us-east-1-aws\"  # next to api key in console\n",
        ")\n",
        "\n",
        "index_name = \"langchain-demo\"\n",
        "\n",
        "index = Pinecone.from_documents(docs, embeddings, index_name=index_name)"
      ],
      "metadata": {
        "id": "hfIpYLV-acks"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_similiar_docs(query,k=2,score=False):\n",
        "  if score:\n",
        "    similar_docs = index.similarity_search_with_score(query,k=k)\n",
        "  else:\n",
        "    similar_docs = index.similarity_search(query,k=k)\n",
        "  return similar_docs\n",
        "\n",
        "query = \"How is india's economy\"\n",
        "similar_docs = get_similiar_docs(query)\n",
        "similar_docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5r7YLpbchAD",
        "outputId": "0433cb24-6adb-4daa-d629-337669379a0c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='India is a vast and diverse country located in South Asia. With a population of over 1.3 billion people, Indi a is the second most populous country in the world. India is a land of many contrasts, with ancient traditio ns coexisting with modern technologies, and rural poverty alongside bustling cities. India has a rich histor y and culture that dates back thousands of years, and it is home to some of the world’s most iconic landm arks and spiritual sites.\\n\\nGeography and Climate\\n\\nIndia is a subcontinent that stretches from the Himalayas in the north to the Indian Ocean in the south. Th e country is bordered by Pakistan to the west, China and Nepal to the north, Bhutan to the northeast, and Bangladesh and Myanmar to the east. India has a diverse range of landscapes, from the snow-capped aks of the Himalayas to the arid deserts of Rajasthan, from the lush tropical forests of Kerala to the rugge d coastline of Goa.', metadata={'source': '/content/data/india.pdf'}),\n",
              " Document(page_content=\"India is also home to many spiritual sites, including the holy city of Varanasi, which is considered one of th e oldest continually inhabited cities in the world. The city is located on the banks of the Ganges River and is a sacred site for Hindus. Other popular spiritual sites in India include the Ajanta and Ellora Caves, whic h are ancient Buddhist and Jain temples located in Maharashtra.\\n\\nis the sixth-largest economy in the world, with a GDP of over $3 trillion. The country's economy has rapidly over the past few decades, thanks to its large and young workforce and its expanding middl class. India is also a major exporter of software and IT services, and it has become a hub for outsourcin and offshoring.\\n\\nDespite its rapid economic growth, India still faces many challenges, including poverty, income inequality, and unemployment. The country has made significant progress in reducing poverty over the past few dec\", metadata={'source': '/content/data/india.pdf'})]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "rtF9QZSvbLD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import OpenAI\n",
        "\n",
        "# model_name = \"text-davinci-003\"\n",
        "# model_name = \"gpt-3.5-turbo\"\n",
        "model_name = \"gpt-4\"\n",
        "llm = OpenAI(model_name=model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuevPx4dbI4W",
        "outputId": "76a1ed52-c785-40a2-a0e9-828837972c18"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/langchain/llms/openai.py:170: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/langchain/llms/openai.py:624: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://python.langchain.com/en/latest/use_cases/question_answering.html"
      ],
      "metadata": {
        "id": "lvqmkpJvss17"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.question_answering import load_qa_chain\n",
        "chain = load_qa_chain(llm, chain_type=\"stuff\")\n",
        "\n",
        "def get_answer(query):\n",
        "  similar_docs = get_similiar_docs(query)\n",
        "  # print(similar_docs)\n",
        "  answer =  chain.run(input_documents=similar_docs, question=query)\n",
        "  return  answer\n",
        "\n",
        "query = \"How is india's economy\"  \n",
        "get_answer(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "RqCE-C3Ubty0",
        "outputId": "f78b567f-5483-4ea9-90fb-3698ce1bf325"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"India's economy is the sixth-largest in the world, with a GDP of over $3 trillion. It has experienced rapid growth over the past few decades due to its large and young workforce and expanding middle class. India is also a major exporter of software and IT services, and has become a hub for outsourcing and offshoring. However, the country still faces challenges such as poverty, income inequality, and unemployment.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"How relations between india and us has improved?\"\n",
        "get_answer(query)"
      ],
      "metadata": {
        "id": "RTnCqRJ3r1kf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "07b97326-64bf-4e25-9f61-a2eef3081784"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Relations between India and the United States have improved in recent years through increased economic and military cooperation, cultural and educational exchanges, and collaborations on issues including security, trade, and climate change.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W38irJY3tfmT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}