Gemma_rag_movies.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.8/144.8 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m36.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m35.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q accelerate bitsandbytes transformers sentence-transformers faiss-gpu datasets langchain"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "#get movies dataset\n",
        "dataset = load_dataset(\"MongoDB/embedded_movies\")\n",
        "dataset_df = pd.DataFrame(dataset['train'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
            "03288c2ae40044dbad7c7a96ea7da5ed",
            "ddde02d0233b49cebbbb6c5f382b508f",
            "40dcdc299e0847ab8c8af26d72759774",
            "9c81135bbc0a470197e1d64b7ef7f1d7",
            "aecfa5d603264dd9a5a2b68bdd0e12af",
            "ef648d939c0b47f192741cb6f346266c",
            "ebedcc222e0e4481941e3553321ff6af",
            "85ba00cbc484445483c963b013acba77",
            "f3efcd39dec54ec39802c9af3e2e335c",
            "7fa90a1dba5c4b0ab72e1bec5d0b8c0b",
            "270ac16a0ad940208f81b3fd9ef725f3",
            "b8ac61b46357437583698cdd0d8729fe",
            "1a82f82c7099425b97c909160def62de",
            "f76e9161fb3e4700bb03c981db463bec",
            "01df5fea3ef24e81af65ed360db4f22b",
            "dde81a536384409fbe8904a0fe13a76a",
            "27f8df3888b4496e8a2a1aa806d7b37d",
            "763f699d528e4b33a7185087f9cb32c2",
            "8a1be15d0d794dd68a2ede0e34b06f58",
            "6c6e355b497d4f6bbee2348bc1072db1",
            "5159238fc7f249a19aae0a7fea21d704",
            "42c1d4b414ff4711bc4ba79b2f91e537",
            "b519848bdfbb4add8ff2392e642535e1",
            "6358aa3991a9480083bc18b123502b03",
            "43c94ff6cedb4baa8d965bb0fefc4cfd",
            "a28a5163ed50402aaedc3fa118810b27",
            "376ab9e834d54bbc813781aa4db3fc1b",
            "23b13bf0bf13465e88fb952407dcf77a",
            "066c14f401e546bf911cc5b87c95ade5",
            "8d2c61fb72e042e4805069df3fcf75ff",
            "593d7a7d622443af8f37517c64235f17",
            "f93aad01817842d4ba753f87dc366241",
            "48e9b5db64a44baabfe62d6b6cf1d696"
          ]
        },
        "id": "z0ApNzWdPowF",
        "outputId": "822eaed9-5a19-4eb7-b2bb-c99e07f7e8ff"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/6.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03288c2ae40044dbad7c7a96ea7da5ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/42.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8ac61b46357437583698cdd0d8729fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b519848bdfbb4add8ff2392e642535e1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display top 5 rows\n",
        "dataset_df.head()"
      ],
      "metadata": {
        "id": "7UK-f6kmRiNx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "c2b3a376-e861-4c51-dab6-4a1cd79774f8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                plot countries  runtime  \\\n",
              "0  Young Pauline is left a lot of money when her ...     [USA]    199.0   \n",
              "1  A penniless young man tries to save an heiress...     [USA]     22.0   \n",
              "2  Michael \"Beau\" Geste leaves England in disgrac...     [USA]    101.0   \n",
              "3  Seeking revenge, an athletic young man joins t...     [USA]     88.0   \n",
              "4  An irresponsible young millionaire changes his...     [USA]     58.0   \n",
              "\n",
              "                                                cast  languages   type  \\\n",
              "0  [Pearl White, Crane Wilbur, Paul Panzer, Edwar...  [English]  movie   \n",
              "1  [Harold Lloyd, Mildred Davis, 'Snub' Pollard, ...  [English]  movie   \n",
              "2  [Ronald Colman, Neil Hamilton, Ralph Forbes, A...  [English]  movie   \n",
              "3  [Billie Dove, Tempe Pigott, Donald Crisp, Sam ...       None  movie   \n",
              "4  [Harold Lloyd, Jobyna Ralston, Noah Young, Jim...  [English]  movie   \n",
              "\n",
              "                                      plot_embedding  num_mflix_comments  \\\n",
              "0  [0.00072939653, -0.026834568, 0.013515796, -0....                   0   \n",
              "1  [-0.022837115, -0.022941574, 0.014937485, -0.0...                   0   \n",
              "2  [0.00023330493, -0.028511643, 0.014653289, -0....                   0   \n",
              "3  [-0.005927917, -0.033394486, 0.0015323418, -0....                   1   \n",
              "4  [-0.0059373598, -0.026604708, -0.0070914757, -...                   0   \n",
              "\n",
              "                                              poster                  title  \\\n",
              "0  https://m.media-amazon.com/images/M/MV5BMzgxOD...  The Perils of Pauline   \n",
              "1  https://m.media-amazon.com/images/M/MV5BNzE1OW...     From Hand to Mouth   \n",
              "2                                               None             Beau Geste   \n",
              "3  https://m.media-amazon.com/images/M/MV5BMzU0ND...       The Black Pirate   \n",
              "4  https://m.media-amazon.com/images/M/MV5BMTcxMT...      For Heaven's Sake   \n",
              "\n",
              "                       genres  metacritic  \\\n",
              "0                    [Action]         NaN   \n",
              "1     [Comedy, Short, Action]         NaN   \n",
              "2  [Action, Adventure, Drama]         NaN   \n",
              "3         [Adventure, Action]         NaN   \n",
              "4   [Action, Comedy, Romance]         NaN   \n",
              "\n",
              "                                              awards  \\\n",
              "0    {'nominations': 0, 'text': '1 win.', 'wins': 1}   \n",
              "1  {'nominations': 1, 'text': '1 nomination.', 'w...   \n",
              "2    {'nominations': 0, 'text': '1 win.', 'wins': 1}   \n",
              "3    {'nominations': 0, 'text': '1 win.', 'wins': 1}   \n",
              "4  {'nominations': 1, 'text': '1 nomination.', 'w...   \n",
              "\n",
              "                              directors   rated  \\\n",
              "0  [Louis J. Gasnier, Donald MacKenzie]    None   \n",
              "1       [Alfred J. Goulding, Hal Roach]    TV-G   \n",
              "2                      [Herbert Brenon]    None   \n",
              "3                       [Albert Parker]    None   \n",
              "4                          [Sam Taylor]  PASSED   \n",
              "\n",
              "                                             writers  \\\n",
              "0  [Charles W. Goddard (screenplay), Basil Dickey...   \n",
              "1                             [H.M. Walker (titles)]   \n",
              "2  [Herbert Brenon (adaptation), John Russell (ad...   \n",
              "3  [Douglas Fairbanks (story), Jack Cunningham (a...   \n",
              "4  [Ted Wilde (story), John Grey (story), Clyde B...   \n",
              "\n",
              "                                          imdb  \\\n",
              "0    {'id': 4465, 'rating': 7.6, 'votes': 744}   \n",
              "1   {'id': 10146, 'rating': 7.0, 'votes': 639}   \n",
              "2   {'id': 16634, 'rating': 6.9, 'votes': 222}   \n",
              "3  {'id': 16654, 'rating': 7.2, 'votes': 1146}   \n",
              "4   {'id': 16895, 'rating': 7.6, 'votes': 918}   \n",
              "\n",
              "                                            fullplot  \n",
              "0  Young Pauline is left a lot of money when her ...  \n",
              "1  As a penniless man worries about how he will m...  \n",
              "2  Michael \"Beau\" Geste leaves England in disgrac...  \n",
              "3  A nobleman vows to avenge the death of his fat...  \n",
              "4  The Uptown Boy, J. Harold Manners (Lloyd) is a...  "
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>plot</th>\n",
              "      <th>countries</th>\n",
              "      <th>runtime</th>\n",
              "      <th>cast</th>\n",
              "      <th>languages</th>\n",
              "      <th>type</th>\n",
              "      <th>plot_embedding</th>\n",
              "      <th>num_mflix_comments</th>\n",
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              "      <th>title</th>\n",
              "      <th>genres</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Young Pauline is left a lot of money when her ...</td>\n",
              "      <td>[USA]</td>\n",
              "      <td>199.0</td>\n",
              "      <td>[Pearl White, Crane Wilbur, Paul Panzer, Edwar...</td>\n",
              "      <td>[English]</td>\n",
              "      <td>movie</td>\n",
              "      <td>[0.00072939653, -0.026834568, 0.013515796, -0....</td>\n",
              "      <td>0</td>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BMzgxOD...</td>\n",
              "      <td>The Perils of Pauline</td>\n",
              "      <td>[Action]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'nominations': 0, 'text': '1 win.', 'wins': 1}</td>\n",
              "      <td>[Louis J. Gasnier, Donald MacKenzie]</td>\n",
              "      <td>None</td>\n",
              "      <td>[Charles W. Goddard (screenplay), Basil Dickey...</td>\n",
              "      <td>{'id': 4465, 'rating': 7.6, 'votes': 744}</td>\n",
              "      <td>Young Pauline is left a lot of money when her ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A penniless young man tries to save an heiress...</td>\n",
              "      <td>[USA]</td>\n",
              "      <td>22.0</td>\n",
              "      <td>[Harold Lloyd, Mildred Davis, 'Snub' Pollard, ...</td>\n",
              "      <td>[English]</td>\n",
              "      <td>movie</td>\n",
              "      <td>[-0.022837115, -0.022941574, 0.014937485, -0.0...</td>\n",
              "      <td>0</td>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BNzE1OW...</td>\n",
              "      <td>From Hand to Mouth</td>\n",
              "      <td>[Comedy, Short, Action]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'nominations': 1, 'text': '1 nomination.', 'w...</td>\n",
              "      <td>[Alfred J. Goulding, Hal Roach]</td>\n",
              "      <td>TV-G</td>\n",
              "      <td>[H.M. Walker (titles)]</td>\n",
              "      <td>{'id': 10146, 'rating': 7.0, 'votes': 639}</td>\n",
              "      <td>As a penniless man worries about how he will m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michael \"Beau\" Geste leaves England in disgrac...</td>\n",
              "      <td>[USA]</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[Ronald Colman, Neil Hamilton, Ralph Forbes, A...</td>\n",
              "      <td>[English]</td>\n",
              "      <td>movie</td>\n",
              "      <td>[0.00023330493, -0.028511643, 0.014653289, -0....</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>Beau Geste</td>\n",
              "      <td>[Action, Adventure, Drama]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'nominations': 0, 'text': '1 win.', 'wins': 1}</td>\n",
              "      <td>[Herbert Brenon]</td>\n",
              "      <td>None</td>\n",
              "      <td>[Herbert Brenon (adaptation), John Russell (ad...</td>\n",
              "      <td>{'id': 16634, 'rating': 6.9, 'votes': 222}</td>\n",
              "      <td>Michael \"Beau\" Geste leaves England in disgrac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Seeking revenge, an athletic young man joins t...</td>\n",
              "      <td>[USA]</td>\n",
              "      <td>88.0</td>\n",
              "      <td>[Billie Dove, Tempe Pigott, Donald Crisp, Sam ...</td>\n",
              "      <td>None</td>\n",
              "      <td>movie</td>\n",
              "      <td>[-0.005927917, -0.033394486, 0.0015323418, -0....</td>\n",
              "      <td>1</td>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BMzU0ND...</td>\n",
              "      <td>The Black Pirate</td>\n",
              "      <td>[Adventure, Action]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'nominations': 0, 'text': '1 win.', 'wins': 1}</td>\n",
              "      <td>[Albert Parker]</td>\n",
              "      <td>None</td>\n",
              "      <td>[Douglas Fairbanks (story), Jack Cunningham (a...</td>\n",
              "      <td>{'id': 16654, 'rating': 7.2, 'votes': 1146}</td>\n",
              "      <td>A nobleman vows to avenge the death of his fat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>An irresponsible young millionaire changes his...</td>\n",
              "      <td>[USA]</td>\n",
              "      <td>58.0</td>\n",
              "      <td>[Harold Lloyd, Jobyna Ralston, Noah Young, Jim...</td>\n",
              "      <td>[English]</td>\n",
              "      <td>movie</td>\n",
              "      <td>[-0.0059373598, -0.026604708, -0.0070914757, -...</td>\n",
              "      <td>0</td>\n",
              "      <td>https://m.media-amazon.com/images/M/MV5BMTcxMT...</td>\n",
              "      <td>For Heaven's Sake</td>\n",
              "      <td>[Action, Comedy, Romance]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>{'nominations': 1, 'text': '1 nomination.', 'w...</td>\n",
              "      <td>[Sam Taylor]</td>\n",
              "      <td>PASSED</td>\n",
              "      <td>[Ted Wilde (story), John Grey (story), Clyde B...</td>\n",
              "      <td>{'id': 16895, 'rating': 7.6, 'votes': 918}</td>\n",
              "      <td>The Uptown Boy, J. Harold Manners (Lloyd) is a...</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset_df",
              "summary": "{\n  \"name\": \"dataset_df\",\n  \"rows\": 1500,\n  \"fields\": [\n    {\n      \"column\": \"plot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1429,\n        \"samples\": [\n          \"A New York City architect becomes a one-man vigilante squad after his wife is murdered by street punks in which he randomly goes out and kills would-be muggers on the mean streets after dark.\",\n          \"As the daring thief Ars\\u00e8ne Lupin (Duris) ransacks the homes of wealthy Parisians, the police, with a secret weapon in their arsenal, attempt to ferret him out.\",\n          \"The spoilt son of a millionaire finds the love of his life, but she will only accept him if he proves himself as a kung-fu master. He enters and wins the \\\"Kung-Fu Scholar\\\" tournament, ...\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"countries\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.09038552453906,\n        \"min\": 6.0,\n        \"max\": 1256.0,\n        \"num_unique_values\": 139,\n        \"samples\": [\n          152.0,\n          127.0,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cast\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"languages\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"series\",\n          \"movie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"plot_embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_mflix_comments\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 0,\n        \"max\": 158,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          117,\n          134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poster\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1368,\n        \"samples\": [\n          \"https://m.media-amazon.com/images/M/MV5BNWE5MzAwMjQtNzI1YS00YjZhLTkxNDItM2JjNjM3ZjI5NzBjXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_SY1000_SX677_AL_.jpg\",\n          \"https://m.media-amazon.com/images/M/MV5BMTgwNjIyNTczMF5BMl5BanBnXkFtZTcwODI5MDkyMQ@@._V1_SY1000_SX677_AL_.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1435,\n        \"samples\": [\n          \"Turbo: A Power Rangers Movie\",\n          \"Neon Genesis Evangelion: Death & Rebirth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metacritic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.861995960390892,\n        \"min\": 9.0,\n        \"max\": 97.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          50.0,\n          97.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"awards\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"directors\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rated\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"TV-MA\",\n          \"TV-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"writers\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imdb\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fullplot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1409,\n        \"samples\": [\n          \"An undercover cop infiltrates a gang of thieves who plan to rob a jewelry store.\",\n          \"Godzilla returns in a brand-new movie that ignores all preceding movies except for the original with a brand new look and a powered up atomic ray. 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            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#discard rows that do not have \"fullplot\" information\n",
        "dataset_df = dataset_df.dropna(subset=[\"fullplot\"])\n",
        "#We will need only 'fullplot' 'title' and 'genres' information\n",
        "main_dataset_df = dataset_df[[\"fullplot\", \"title\", \"genres\"]]"
      ],
      "metadata": {
        "id": "RlVK_FwVR02n"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main_dataset_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iEaqGwQLYkEE",
        "outputId": "a1d9855c-3a74-46f6-f893-8aaf60b21017"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            fullplot                  title  \\\n",
              "0  Young Pauline is left a lot of money when her ...  The Perils of Pauline   \n",
              "1  As a penniless man worries about how he will m...     From Hand to Mouth   \n",
              "2  Michael \"Beau\" Geste leaves England in disgrac...             Beau Geste   \n",
              "3  A nobleman vows to avenge the death of his fat...       The Black Pirate   \n",
              "4  The Uptown Boy, J. Harold Manners (Lloyd) is a...      For Heaven's Sake   \n",
              "\n",
              "                       genres  \n",
              "0                    [Action]  \n",
              "1     [Comedy, Short, Action]  \n",
              "2  [Action, Adventure, Drama]  \n",
              "3         [Adventure, Action]  \n",
              "4   [Action, Comedy, Romance]  "
            ],
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              "<style scoped>\n",
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              "      <td>Young Pauline is left a lot of money when her ...</td>\n",
              "      <td>The Perils of Pauline</td>\n",
              "      <td>[Action]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>As a penniless man worries about how he will m...</td>\n",
              "      <td>From Hand to Mouth</td>\n",
              "      <td>[Comedy, Short, Action]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michael \"Beau\" Geste leaves England in disgrac...</td>\n",
              "      <td>Beau Geste</td>\n",
              "      <td>[Action, Adventure, Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A nobleman vows to avenge the death of his fat...</td>\n",
              "      <td>The Black Pirate</td>\n",
              "      <td>[Adventure, Action]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The Uptown Boy, J. Harold Manners (Lloyd) is a...</td>\n",
              "      <td>For Heaven's Sake</td>\n",
              "      <td>[Action, Comedy, Romance]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a06a3316-4211-4ce6-a91b-23433b141ff0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a06a3316-4211-4ce6-a91b-23433b141ff0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a06a3316-4211-4ce6-a91b-23433b141ff0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f4df0a1e-29a4-46d1-835f-c5cf135a8d11\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4df0a1e-29a4-46d1-835f-c5cf135a8d11')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f4df0a1e-29a4-46d1-835f-c5cf135a8d11 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "main_dataset_df",
              "summary": "{\n  \"name\": \"main_dataset_df\",\n  \"rows\": 1452,\n  \"fields\": [\n    {\n      \"column\": \"fullplot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1409,\n        \"samples\": [\n          \"An undercover cop infiltrates a gang of thieves who plan to rob a jewelry store.\",\n          \"Godzilla returns in a brand-new movie that ignores all preceding movies except for the original with a brand new look and a powered up atomic ray. This time he battles a mysterious UFO that later transforms into a mysterious kaiju dubbed Orga. They meet up for the final showdown in the city of Shinjuku.\",\n          \"Relationships become entangled in an emotional web.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1391,\n        \"samples\": [\n          \"Superhero Movie\",\n          \"Hooper\",\n          \"Sivaji\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import DataFrameLoader\n",
        "\n",
        "#convert DataFrane into Langchain Document format for further processing\n",
        "#\"fullplot\" will be the main content information, \"title\" and \"generes\" will be used as metadata\n",
        "loader = DataFrameLoader(main_dataset_df, page_content_column=\"fullplot\")\n",
        "dataset_docs = loader.load()"
      ],
      "metadata": {
        "id": "DWtkuchnTuBz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#chunking using embedding model\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "#hierarchy of seperators to be used by text splitter\n",
        "MARKDOWN_SEPARATORS = [\n",
        "    \"\\n#{1,6} \",\n",
        "    \"```\\n\",\n",
        "    \"\\n\\\\*\\\\*\\\\*+\\n\",\n",
        "    \"\\n---+\\n\",\n",
        "    \"\\n___+\\n\",\n",
        "    \"\\n\\n\",\n",
        "    \"\\n\",\n",
        "    \" \",\n",
        "    \"\",\n",
        "]\n",
        "\n",
        "#\"thenlper/gte-small\" with 512 dimentional embedding is used as embedding model\n",
        "EMB_MODEL_CKP = \"thenlper/gte-small\"\n",
        "#get enbedding_tokenizer\n",
        "embedding_tokenizer = AutoTokenizer.from_pretrained(EMB_MODEL_CKP)\n",
        "\n",
        "def split_documents(chunk_size, KB, tokenizer=embedding_tokenizer):\n",
        "  \"\"\"\n",
        "    Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.\n",
        "  \"\"\"\n",
        "  text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(\n",
        "        embedding_tokenizer, #tokenizer to be used to determine number of tokens\n",
        "        chunk_size=chunk_size,\n",
        "        chunk_overlap=int(chunk_size / 10),\n",
        "        add_start_index=True, # If `True`, includes chunk's start index in metadata\n",
        "        strip_whitespace=True, # If `True`, strips whitespace from the start and end of every document\n",
        "        separators=MARKDOWN_SEPARATORS, #use seperators for chunking\n",
        "    )\n",
        "\n",
        "  docs_processed = []\n",
        "  for doc in KB:\n",
        "      docs_processed += text_splitter.split_documents([doc])\n",
        "\n",
        "  #remove duplicates\n",
        "  unique_texts = {}\n",
        "  docs_processed_unique = []\n",
        "  for doc in tqdm(docs_processed):\n",
        "    if doc.page_content not in unique_texts:\n",
        "      unique_texts[doc.page_content] = True\n",
        "      docs_processed_unique.append(doc)\n",
        "\n",
        "  return docs_processed_unique"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        "outputId": "a99587ad-1353-4f81-9ce6-e2183deec8f0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"
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            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "#split documents\n",
        "docs_processed_tok = split_documents(512, dataset_docs, EMB_MODEL_CKP)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        "id": "wnfbLN3CZ1C5",
        "outputId": "9dab5d9b-0af5-414b-af61-367e0a40dc0a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2125 [00:00<?, ?it/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize the chunk sizes we would have in tokens from a common model\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lengths = [len(embedding_tokenizer.encode(doc.page_content)) for doc in tqdm(dataset_docs)]\n",
        "fig = pd.Series(lengths).hist()\n",
        "plt.title(\"Distribution of document lengths in the knowledge base (in count of tokens) before\")\n",
        "plt.show()\n",
        "\n",
        "lengths = [len(embedding_tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed_tok)]\n",
        "fig = pd.Series(lengths).hist()\n",
        "plt.title(\"Distribution of document lengths in the knowledge base (in count of tokens) after chunking\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951,
          "referenced_widgets": [
            "a612613feb1445d1b29c99b493097fbd",
            "fcaa90338c774f0986089bd1f71b57b1",
            "4110900bd4f24e79bdc670f31ead7a0e",
            "9773dd827cbf4016b1f29fb86be4eea3",
            "7f04c0b01ca941388aecf62ef0690ab7",
            "e371d278934647d495d3db6a9d59c40a",
            "ac01c9a65f9d4413bf8d40fd0d79f574",
            "2570bf2e3caa430b9ba56c6a61be7b8a",
            "8e8021b1443a48a8ad40a3296b198674",
            "85f24860363b435ba94b6d8e4ae2d229",
            "942b760d6af14029af2154b57598f28f",
            "edbd98368b594ac0ba8b826c1205a330",
            "f338d8f776334b87850e9b62999caf25",
            "a06a8e3b219f4ad18f3f70d6fc9d137b",
            "3778c71f354f4b879e8cc520ed06596d",
            "6fa05751758d4e70b4a2c94fefb51169",
            "77d0f556fd0843c3ac3adc467c7d7531",
            "8d5d89a82a684ecd8aab780354191903",
            "e2d3a66347cf4f32a18575390d2708f1",
            "1e0a53da455f44169cf06fb93decf118",
            "b1a38434347d484584541a286d4fb74e",
            "eb6b6698afde4623b3bc501cfa078409"
          ]
        },
        "id": "qg8AVG5IZ7iH",
        "outputId": "e6433c86-5539-48c0-b85e-a6d6ce2dce16"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1452 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a612613feb1445d1b29c99b493097fbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2057 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edbd98368b594ac0ba8b826c1205a330"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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55vyMMWbv3r1Gkhk0aJDd8B49ehhJZuzYsbZhHTp0MG5ububYsWO2YadPnzYlSpQwd999t23YSy+9ZCSZr7/+OsPyUlNTjTH/v40dP37cbnzauly3bp1tWNq2sGDBAtuwX375xUgyzs7OZtu2bbbhK1euzLDe+vXrZ8qVK2f+/vtvu2V169bN+Pr6msTERLtl16xZ0yQlJdmmmzFjhpFk9u3bZxvWpk0bu23getJvdxMnTjReXl7m119/tZvuueeeM8WKFTMnT540xhhz/PhxI8mULl3anDt3zjbdt99+aySZpUuX2obVqVPHVKhQwZw/f942bP369UaSXVv/+uuvDOs2TZ8+fYwkM2HCBLvhDRo0MKGhobbXQ4cONT4+PubKlStZ/gzSpF92Wt/x6KOP2k334IMPmtKlS193flbrIqvrMzU11VSvXt1ERkbatk9jjElMTDRVqlQxrVq1uuby05azcOFCc/78edO8eXNTpkwZ8+OPP9pNl9U6076T/fv3t5vumWeeMZLM2rVrjTHGxMbGGklm1qxZxhhj4uLijLOzs3nooYdMQECA7X1PPfWUKVWqlK22tG3q6u9Iy5YtTZ06dcylS5dsw1JTU81dd91lqlevbhs2bNgwI8ls377dNiw2Ntb4+vrafZ+jo6ONi4uL6dChg10N48aNM5Jy9F2wEhwcbCSZr776yjYsPj7elCtXzm4fdenSJZOSkmL33uPHjxt3d3e77b19+/amVq1a11xmVvuUzCQnJxsnJyfz9NNPZxiXfj9qzI3tMz788EMjybzxxhsZxqVtD1ndf2W23Vzdxpx+p728vDLdJ2cmL/aB12pDVvd5Vx8znTlzxtSqVcvccsst5vfff7dN88knnxhnZ2ezadMmu2XMmTPHSDI//PCDbVhW17mvr68ZPHhwlmq89dZbTevWra87XWbbbmRkpLnlllvshvXp08d4eXnZDbM6bspOH5u27XTv3j0rZWXpeCM7+/asHiNmZ57Nmze39SlfffWVcXV1NY899liG/ij9stO2q/DwcLvPbfjw4aZYsWImLi7OGJO9/jYrcv0uCm9v72s+LSnt2sFvv/02xzcIu7u7q2/fvlmevnfv3rZflSWpc+fOKleunJYvX56j5WfV8uXLVaxYMdsvemmefvppGWO0YsUKu+Hh4eF2v6LUrVtXPj4++u233667nMDAQHXv3t02zNXVVU899ZQuXLigDRs25KjtkjK0Pf0vZCkpKVq1apU6dOigW265xTa8XLly6tGjhzZv3qyEhARJ0ldffaV69epl+ktGTh8Z6O3tbfc0kdtuu01+fn6qWbOm3a88af+f9lkaY/TVV1+pbdu2Msbo77//tv2LjIxUfHx8htO3ffv2tbsuM+2Xkuutn+xYuHChmjVrppIlS9q1KTw8XCkpKdq4caPd9F27dlXJkiUt23T69Gnt27dPvXv3tvuFvXnz5qpTp0622/fEE0/YvW7WrJld/X5+flk+xX0jyzx79qxtu8qp663PvXv36siRI+rRo4fOnj1rWxcXL15Uy5YttXHjxiz1YfHx8YqIiNAvv/yi9evXW970d706076TI0aMsJsu7ZfS7777TtJ/v5jWqFHDtq388MMPKlasmEaOHKmYmBgdOXJE0n9nGJo2bWr53Tt37pzWrl2rLl266Pz587b6z549q8jISB05ckSnTp2yta1x48Z2vwaWLVtWPXv2tJvnmjVrdOXKFQ0aNMhu+JNPPplh+dn9LmQmKCjIrr/x8fFR79699eOPPyo6OlrSf/uTtBsKU1JSdPbsWdulslf3AX5+fvrzzz8zveRPylmfcrVz587JGGP3fb6enO4zvvrqK5UpUybTzz1te8ju/is7cvs7nRf7QCvZ2eel+fPPP9W8eXMlJydr48aNCg4Oto1buHChatasqRo1athtMy1atJAkrVu3zm5eWVnnfn5+2r59u06fPn3detK+X9dz9dnC+Ph4/f3332revLl+++03xcfHX/f9mclJH5t+27GSneONvNi3Z2ee//vf/9S1a1c9/vjjmjt3bpZvcB4wYIBdLc2aNVNKSopOnDghKXv9bVbk+h2Gac+QttK1a1e9//776t+/v5577jm1bNlSHTt2VOfOnbP8IZUvXz5bN9RUr17d7rWTk5OqVauW588GP3HihIKCguzCivTfpU1p469WqVKlDPMoWbKk/vnnn+sup3r16hk+P6vlZLXtzs7OGU4D33bbbXav//rrLyUmJmYYnrb81NRU/fHHH6pVq5aOHTumTp06Zbst11KhQoUMX35fX19VrFgxwzBJts/yr7/+UlxcnN599129++67mc47NjbW7nX69ZO2Y7/e+smOI0eO6Oeff7Y8TZ7dNqWt+2rVqmWYV7Vq1a55AJOeh4dHhnal3z4HDRqkL7/8Uq1bt1b58uUVERGhLl266L777svyctK7Vo0+Pj55Ml9JtgPrPn36WM4jPj7+ugd4w4YN06VLl/Tjjz+qVq1aOWqPj4+P7TuZfl0GBgbKz8/P7nverFkzW8DYtGmTGjZsqIYNG6pUqVLatGmTAgIC9NNPP9kupcnM0aNHZYzRmDFjNGbMmEyniY2NVfny5XXixIlML8NI3y9YbY+lSpXK8Dlm97uQmWrVqmXoH2699VZJ/117HxgYqNTUVM2YMUOzZs3S8ePH7e7JufqynlGjRmn16tW68847Va1aNUVERKhHjx5q0qSJpJz1KZkx17jMIr2c7jOOHTum22677ZoPGcju/is7cvs7nRf7QCvZ2eel6dWrl1xcXHTo0CEFBgbavefIkSM6dOhQjvt8KeM6nzZtmvr06aOKFSsqNDRU999/v3r37m0XcNIYY7L0g90PP/ygsWPHauvWrRnurYqPj7ftY7MjJ31slSpVsjTv7Bxv5MW+PavzPH78uB5++GE99NBDevvtt3N1Gdnpb7MiVwPDn3/+qfj4+EwPTtIUL15cGzdu1Lp16/Tdd9/p+++/1xdffKEWLVpo1apV171mK20euc3qC5OSkpKlNuUGq+VkZwfi6K61HjJj9Zld77NM+9Xi4Ycftuysrr5+PyvzzA2pqalq1aqVnn322UzHpx3s5Gebrresq/n7+2vv3r1auXKlVqxYoRUrVmjevHnq3bu33Q2IubHcG60xq9vIq6++anlWICv3qLRv316ff/65pkyZoo8//tjyh5Gs1pmVnXvTpk313nvv6bffftOmTZvUrFkzOTk5qWnTptq0aZOCgoKUmppq+9UrM2n1P/PMM4qMjMx0mmv19Tcqu9+FnJo0aZLGjBmjRx99VBMnTlSpUqXk7OysYcOG2f26WbNmTR0+fFjLli3T999/r6+++kqzZs3SSy+9pPHjx+eoT7laqVKl5OTklK2DlJthn5HdPlu6Odqdnzp27KiPP/5YM2bM0OTJk+3Gpaamqk6dOnrjjTcyfW/6H7+y8tl16dJFzZo10zfffKNVq1bp1Vdf1dSpU/X111+rdevWdu/7559/Mvyomt6xY8fUsmVL1ahRQ2+88YYqVqwoNzc3LV++XNOnT8/x1SI56WPz4vgvK59pdo8Rs7qNlytXznbFy65du9SwYcOsNjvfv0e5Ghg++eQTSbLcuaRxdnZWy5Yt1bJlS73xxhuaNGmSXnjhBa1bt07h4eG5/hct01JsGmOMjh49atd5lyxZUnFxcRnee+LECbtUnp22BQcHa/Xq1Tp//rzdrzS//PKLbXxuCA4O1s8//6zU1FS7g5EbWU5wcLBSU1Ntv0SlSXuqQ5qyZcvK09Mzw/C05Ts7O9s6vKpVq2r//v3XXG5a6k2/LnLzFyJJtic/paSkKDw8PNfme6PbbtWqVXXhwoVca1Paus/s6Qzph+XW987NzU1t27ZV27ZtlZqaqkGDBmnu3LkaM2ZMnh5gppcb60L67zKWG1kfHTp0UEREhB555BGVKFHiuk8lspL2nTxy5Ijtl1Ppvxtt4+Li7L7naUEgKipKO3futD3D/+6779bs2bMVFBQkLy8vhYaGWi4vrd9zdXW9bv3BwcEZ+lkpY39x9fZ49S+FZ8+ezXCgnBvfhbSzJFdvC7/++qsk2Z6atWjRIt1777364IMP7N4bFxeX4UEMXl5e6tq1q7p27arLly+rY8eOeuWVVzR69Ogb7lNcXFxUtWpVHT9+PNvvza6qVatq+/btSk5OtnxcY1b3X3nVZ2d3X5vb+0CrNmRnn5fmySefVLVq1fTSSy/J19fX7m9qVK1aVT/99JNatmyZq8c+5cqV06BBgzRo0CDFxsbq9ttv1yuvvGIXGK5cuaI//vhD7dq1u+a8li5dqqSkJC1ZssTuV+30l0tZsaort/pYq3lf73gjO7J6jJhdHh4eWrZsmVq0aKH77rtPGzZsuOaZ6OzITn+bFbl2D8PatWs1ceJEValSJcN1q1c7d+5chmFpyTLtEWJeXl6SMnZAOfXxxx/b3VexaNEinTlzxu6LU7VqVW3btk2XL1+2DVu2bFmGx69mp23333+/UlJS9M4779gNnz59upycnDIk/Zy6//77FR0dbXdn/JUrV/T222/L29tbzZs3z/Y809p29WMYJWV4MkmxYsUUERGhb7/91u4Sr5iYGC1YsEBNmza1nWLu1KmTfvrpJ9vTXK6WlojTOpCrr09OSUmxPMWfU8WKFVOnTp301VdfZdqppH80WVZ5eXnl+HpO6b9fhrZu3aqVK1dmGBcXF2d7zFpWBQUFqXbt2vr444/tnrW9YcMG7du3z25aT09P23Jy6uzZs3avnZ2dbcE8/SMC89qNrovQ0FBVrVpVr732WqbPKc/ONtK7d2+99dZbmjNnjkaNGpWj9tx///2SMn4H036ZvPoJZlWqVFH58uU1ffp0JScn2y6badasmY4dO6ZFixapcePG17wkxd/fX/fcc4/mzp2rM2fOZBh/df3333+/tm3bph07dtiN/+yzz+ze07JlS7m4uGQITen7SCl3vgunT5+2628SEhL08ccfq379+rZLQ4oVK5bhF7mFCxfa7s9Ik37bdnNzU0hIiIwxSk5OzpU+JSwsTLt27brudDeqU6dO+vvvvzP93NM+i6zuv3x8fFSmTJkM95TMmjXrhtro5eWV5b4oL/aBVm3Izj7vamPGjNEzzzyj0aNH223/Xbp00alTp/Tee+9leM+///6b7b81kJKSkqHf8/f3V1BQUIY++ODBg7p06ZLlkx7TpP2SffX3JD4+XvPmzctSm6yOm3Kzj00vK8cb2ZHVY8Sc8PX11cqVK22Pv7360cY3Ijv9bVbk6AzDihUr9Msvv+jKlSuKiYnR2rVrFRUVpeDgYC1ZsuSaf2BmwoQJ2rhxo9q0aaPg4GDFxsZq1qxZqlChgu2ZulWrVpWfn5/mzJmjEiVKyMvLS40aNcrytWvplSpVSk2bNlXfvn0VExOjN998U9WqVbN79Gv//v21aNEi3XffferSpYuOHTumTz/9NMM1/NlpW9u2bXXvvffqhRde0O+//6569epp1apV+vbbbzVs2LBrPiYuOwYMGKC5c+fqkUce0e7du1W5cmUtWrRIP/zwg958880M16BmRf369dW9e3fNmjVL8fHxuuuuu7RmzZpMf6l++eWXbX9bY9CgQXJxcdHcuXOVlJSkadOm2aYbOXKkFi1apIceekiPPvqoQkNDde7cOS1ZskRz5sxRvXr1VKtWLTVu3FijR4/WuXPnVKpUKX3++efZPlDOiilTpmjdunVq1KiRHnvsMYWEhOjcuXPas2ePVq9enWm4vZ7Q0FB98cUXGjFihO644w55e3urbdu2WX7/yJEjtWTJEj3wwAN65JFHFBoaqosXL2rfvn1atGiRfv/992w/fnbSpElq3769mjRpor59++qff/7RO++8o9q1a9t10sWLF1dISIi++OIL3XrrrSpVqpRq165tezRnVvTv31/nzp1TixYtVKFCBZ04cUJvv/226tevb/ereH640XXh7Oys999/X61bt1atWrXUt29flS9fXqdOndK6devk4+OjpUuXZnl+Q4YMUUJCgl544QX5+vra/n5MVtWrV099+vTRu+++q7i4ODVv3lw7duzQRx99pA4dOujee++1m75Zs2b6/PPPVadOHduvwLfffru8vLz066+/XvP+hTQzZ85U06ZNVadOHT322GO65ZZbFBMTo61bt+rPP/+0/a2CZ599Vp988onuu+8+DR061PZY1bRfftMEBARo6NChev3119WuXTvdd999+umnn7RixQqVKVPG7pfI3Pgu3HrrrerXr5927typgIAAffjhh4qJibE70HnggQc0YcIE9e3bV3fddZf27dunzz77LMOvhhEREQoMDFSTJk0UEBCgQ4cO6Z133lGbNm1sfeyN9int27fXJ598ol9//TXXLrnKTO/evfXxxx9rxIgR2rFjh5o1a6aLFy9q9erVGjRokNq3b5+t/Vf//v01ZcoU9e/fXw0bNtTGjRttZ3JyKjQ0VKtXr9Ybb7yhoKAgValSxfJxpXmxD7xWG7K6z0vv1VdfVXx8vAYPHqwSJUro4YcfVq9evfTll1/qiSee0Lp169SkSROlpKTol19+0ZdffqmVK1dm6zKV8+fPq0KFCurcubPq1asnb29vrV69Wjt37tTrr79uN21UVJQ8PT1tjxi2EhERYTtz/Pjjj+vChQt677335O/vn+mPCeld67gpN/vYq2XleCM7snqMmFNlypSxbVPh4eHavHmz3aPPcyI7/W2WZOeRSmmPckr75+bmZgIDA02rVq3MjBkz7B5dlib94+DWrFlj2rdvb4KCgoybm5sJCgoy3bt3z/DovG+//daEhIQYFxcXu8dxXf0YqvSsHm/1v//9z4wePdr4+/ub4sWLmzZt2pgTJ05keP/rr79uypcvb9zd3U2TJk3Mrl27MszzWm1L/1hVY4w5f/68GT58uAkKCjKurq6mevXq5tVXX7V7FJYx/z0uLbPHoFk9yiu9mJgY07dvX1OmTBnj5uZm6tSpk+lj7rLzSLl///3XPPXUU6Z06dLGy8vLtG3b1vzxxx+ZPnpzz549JjIy0nh7extPT09z7733mi1btmSY59mzZ82QIUNM+fLljZubm6lQoYLp06eP3WMIjx07ZsLDw427u7sJCAgwzz//vImKisr0saqZbQtWNWb2GcfExJjBgwebihUrGldXVxMYGGhatmxp3n33Xds0Vz8e82qZPU7wwoULpkePHsbPzy/DY0szk9n6PX/+vBk9erSpVq2acXNzM2XKlDF33XWXee2118zly5ftlv3qq69mWmf69fP555+bGjVqGHd3d1O7dm2zZMkS06lTJ1OjRg276bZs2WJCQ0ONm5ub3Xwye1SeMRm/34sWLTIRERHG39/fuLm5mUqVKpnHH3/cnDlz5pqfQ2btTpt3+kcwWz16Nz2rdZGd9WmMMT/++KPp2LGjKV26tHF3dzfBwcGmS5cuZs2aNddcvtVynn32WSPJvPPOO9muMzk52YwfP95UqVLFuLq6mooVK5rRo0fbPfY0zcyZM40kM3DgQLvh4eHhRlKG9lvVf+zYMdO7d28TGBhoXF1dTfny5c0DDzxgFi1aZDfdzz//bJo3b248PDxM+fLlzcSJE80HH3yQoYYrV66YMWPGmMDAQFO8eHHTokULc+jQIVO6dGnzxBNP2M0zK98FK2n9wMqVK03dunWNu7u7qVGjRob1cenSJfP000+bcuXKmeLFi5smTZqYrVu3Zuj7586da+6++27bdlC1alUzcuRIEx8fbze/rPQpVpKSkkyZMmXMxIkT7YZbPVb1RvYZiYmJ5oUXXrBtS4GBgaZz5852jwrN6v4rMTHR9OvXz/j6+poSJUqYLl262B7vm9Pv9C+//GLuvvtuU7x48Sw9AjIv9oHXakNW9nmZPYo+JSXFdO/e3bi4uJjFixcbY/57BOzUqVNNrVq1jLu7uylZsqQJDQ0148ePt9u+srLOk5KSzMiRI029evVMiRIljJeXl6lXr57tMctXa9SokXn44Yez9FksWbLE1K1b13h4eJjKlSubqVOn2h7Pe/V6s9pXWB03GZO1PtZq27mW6x1vZHdfkJVjxOzMM7NjmKNHj5py5cqZmjVr2mq1eqxq+j9xkNnj57PT316PkzGF9C4jANdUv359lS1bNlcfgQrkRFxcnEqWLKmXX35ZL7zwQkE3p0BNnDhR8+bN05EjR/LtgRsoevbu3avbb79de/bssbzhGIVTTvvbXP87DABuLsnJyRku6Vq/fr1++uknuz9pD+SHf//9N8OwtPsy2B6l4cOH68KFC/r8888LuikoxKZMmaLOnTsTFgq53OxvOcMAFHK///67wsPD9fDDDysoKEi//PKL5syZI19fX+3fv9/uWfNAXps/f77mz5+v+++/X97e3tq8ebP+97//KSIiItMbnAEAOZOb/W2u/+E2ADeXkiVLKjQ0VO+//77++usveXl5qU2bNpoyZQphAfmubt26cnFx0bRp05SQkGC7Me/ll18u6KYBQKGSm/0tZxgAAAAAWOIeBgAAAACWCAwAAAAALHEPA7ItNTVVp0+fVokSJXL1T9kDAIC8Y4zR+fPnFRQUJGdnfjNG1hEYkG2nT59WxYoVC7oZAAAgB/744w9VqFChoJsBB0JgQLaVKFFC0n8djo+PTwG3JueSk5O1atUqRUREyNXVtaCbk2+KYt3UXDRqlopm3dRcNGqWbrzuhIQEVaxY0bYfB7KKwIBsS7sMycfHx+EDg6enp3x8fIrcDqeo1U3NRaNmqWjWTc1Fo2Yp9+rmcmJkFxewAQAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJZcCroBAAAURZWf+y5X5uNezGjanVLtcSuVlOKUK/O08vuUNnk6fwA3J84wAAAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicDgQMaNGycnJye7fzVq1LCNv3TpkgYPHqzSpUvL29tbnTp1UkxMjN08Tp48qTZt2sjT01P+/v4aOXKkrly5kt+lAAAAwEG4FHQDkD21atXS6tWrba9dXP5/FQ4fPlzfffedFi5cKF9fXw0ZMkQdO3bUDz/8IElKSUlRmzZtFBgYqC1btujMmTPq3bu3XF1dNWnSpHyvBQAAADc/AoODcXFxUWBgYIbh8fHx+uCDD7RgwQK1aNFCkjRv3jzVrFlT27ZtU+PGjbVq1SodPHhQq1evVkBAgOrXr6+JEydq1KhRGjdunNzc3PK7HAAAANzkCAwO5siRIwoKCpKHh4fCwsI0efJkVapUSbt371ZycrLCw8Nt09aoUUOVKlXS1q1b1bhxY23dulV16tRRQECAbZrIyEgNHDhQBw4cUIMGDTJdZlJSkpKSkmyvExISJEnJyclKTk7Oo0rzXlrbHbmGnCiKdVNz0eFIdbsXM7kzH2dj99+8dLN8ro60nnPTjdZd1D4v5B4nY0ze9zDIFStWrNCFCxd022236cyZMxo/frxOnTql/fv3a+nSperbt6/dgb0k3Xnnnbr33ns1depUDRgwQCdOnNDKlStt4xMTE+Xl5aXly5erdevWmS533LhxGj9+fIbhCxYskKenZ+4WCQAA8kRiYqJ69Oih+Ph4+fj4FHRz4EA4w+BArj6gr1u3rho1aqTg4GB9+eWXKl68eJ4td/To0RoxYoTtdUJCgipWrKiIiAiH7nCSk5MVFRWlVq1aydXVtaCbk2+KYt3UXDRqlhyr7trjVl5/oixwdzaa2DBVY3Y5KynVKVfmaWX/uMg8nX9WOdJ6zk03WnfaFQJAdhEYHJifn59uvfVWHT16VK1atdLly5cVFxcnPz8/2zQxMTG2ex4CAwO1Y8cOu3mkPUUps/si0ri7u8vd3T3DcFdX10LRUReWOrKrKNZNzUWHI9SdlJK7B/dJqU65Ps/0brbP1BHWc17Iad1F8bNC7uCxqg7swoULOnbsmMqVK6fQ0FC5urpqzZo1tvGHDx/WyZMnFRYWJkkKCwvTvn37FBsba5smKipKPj4+CgkJyff2AwAA4ObHGQYH8swzz6ht27YKDg7W6dOnNXbsWBUrVkzdu3eXr6+v+vXrpxEjRqhUqVLy8fHRk08+qbCwMDVu3FiSFBERoZCQEPXq1UvTpk1TdHS0XnzxRQ0ePDjTMwgAAAAAgcGB/Pnnn+revbvOnj2rsmXLqmnTptq2bZvKli0rSZo+fbqcnZ3VqVMnJSUlKTIyUrNmzbK9v1ixYlq2bJkGDhyosLAweXl5qU+fPpowYUJBlQQAAICbHIHBgXz++efXHO/h4aGZM2dq5syZltMEBwdr+fLlud00AAAAFFLcwwAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGBzYlClT5OTkpGHDhtmGXbp0SYMHD1bp0qXl7e2tTp06KSYmxu59J0+eVJs2beTp6Sl/f3+NHDlSV65cyefWAwAAwBEQGBzUzp07NXfuXNWtW9du+PDhw7V06VItXLhQGzZs0OnTp9WxY0fb+JSUFLVp00aXL1/Wli1b9NFHH2n+/Pl66aWX8rsEAAAAOAACgwO6cOGCevbsqffee08lS5a0DY+Pj9cHH3ygN954Qy1atFBoaKjmzZunLVu2aNu2bZKkVatW6eDBg/r0009Vv359tW7dWhMnTtTMmTN1+fLlgioJAAAANymXgm4Asm/w4MFq06aNwsPD9fLLL9uG7969W8nJyQoPD7cNq1GjhipVqqStW7eqcePG2rp1q+rUqaOAgADbNJGRkRo4cKAOHDigBg0aZFheUlKSkpKSbK8TEhIkScnJyUpOTs6LEvNFWtsduYacKIp1U3PR4Uh1uxczuTMfZ2P337x0s3yujrSec9ON1l3UPi/kHgKDg/n888+1Z88e7dy5M8O46Ohoubm5yc/Pz254QECAoqOjbdNcHRbSxqeNy8zkyZM1fvz4DMNXrVolT0/PnJRxU4mKiiroJhSIolg3NRcdjlD3tDtzd34TG6bm7gwzsXz58jxfRnY4wnrOCzmtOzExMZdbgqKCwOBA/vjjDw0dOlRRUVHy8PDIt+WOHj1aI0aMsL1OSEhQxYoVFRERIR8fn3xrR25LTk5WVFSUWrVqJVdX14JuTr4pinVTc9GoWXKsumuPW5kr83F3NprYMFVjdjkrKdUpV+ZpZf+4yDydf1Y50nrOTTdad9oVAkB2ERgcyO7duxUbG6vbb7/dNiwlJUUbN27UO++8o5UrV+ry5cuKi4uzO8sQExOjwMBASVJgYKB27NhhN9+0pyilTZOeu7u73N3dMwx3dXUtFB11Yakju4pi3dRcdDhC3UkpuXtwn5TqlOvzTO9m+0wdYT3nhZzWXRQ/K+QObnp2IC1bttS+ffu0d+9e27+GDRuqZ8+etv93dXXVmjVrbO85fPiwTp48qbCwMElSWFiY9u3bp9jYWNs0UVFR8vHxUUhISL7XBAAAgJsbZxgcSIkSJVS7dm27YV5eXipdurRteL9+/TRixAiVKlVKPj4+evLJJxUWFqbGjRtLkiIiIhQSEqJevXpp2rRpio6O1osvvqjBgwdnehYBAAAARRuBoZCZPn26nJ2d1alTJyUlJSkyMlKzZs2yjS9WrJiWLVumgQMHKiwsTF5eXurTp48mTJhQgK0GAADAzYrA4ODWr19v99rDw0MzZ87UzJkzLd8THBx80z3pAgAAADcn7mEAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwOZPbs2apbt658fHzk4+OjsLAwrVixwjb+0qVLGjx4sEqXLi1vb2916tRJMTExdvM4efKk2rRpI09PT/n7+2vkyJG6cuVKfpcCAAAAB0FgcCAVKlTQlClTtHv3bu3atUstWrRQ+/btdeDAAUnS8OHDtXTpUi1cuFAbNmzQ6dOn1bFjR9v7U1JS1KZNG12+fFlbtmzRRx99pPnz5+ull14qqJIAAABwk3Mp6AYg69q2bWv3+pVXXtHs2bO1bds2VahQQR988IEWLFigFi1aSJLmzZunmjVratu2bWrcuLFWrVqlgwcPavXq1QoICFD9+vU1ceJEjRo1SuPGjZObm1tBlAUAAICbGIHBQaWkpGjhwoW6ePGiwsLCtHv3biUnJys8PNw2TY0aNVSpUiVt3bpVjRs31tatW1WnTh0FBATYpomMjNTAgQN14MABNWjQINNlJSUlKSkpyfY6ISFBkpScnKzk5OQ8qjDvpbXdkWvIiaJYNzUXHY5Ut3sxkzvzcTZ2/81LN8vn6kjrOTfdaN1F7fNC7iEwOJh9+/YpLCxMly5dkre3t7755huFhIRo7969cnNzk5+fn930AQEBio6OliRFR0fbhYW08WnjrEyePFnjx4/PMHzVqlXy9PS8wYoKXlRUVEE3oUAUxbqpuehwhLqn3Zm785vYMDV3Z5iJ5cuX5/kyssMR1nNeyGndiYmJudwSFBUEBgdz2223ae/evYqPj9eiRYvUp08fbdiwIU+XOXr0aI0YMcL2OiEhQRUrVlRERIR8fHzydNl5KTk5WVFRUWrVqpVcXV0Lujn5pijWTc1Fo2bJsequPW5lrszH3dloYsNUjdnlrKRUp1yZp5X94yLzdP5Z5UjrOTfdaN1pVwgA2UVgcDBubm6qVq2aJCk0NFQ7d+7UjBkz1LVrV12+fFlxcXF2ZxliYmIUGBgoSQoMDNSOHTvs5pf2FKW0aTLj7u4ud3f3DMNdXV0LRUddWOrIrqJYNzUXHY5Qd1JK7h7cJ6U65fo807vZPlNHWM95Iad1F8XPCrmDpyQ5uNTUVCUlJSk0NFSurq5as2aNbdzhw4d18uRJhYWFSZLCwsK0b98+xcbG2qaJioqSj4+PQkJC8r3tAAAAuPlxhsGBjB49Wq1bt1alSpV0/vx5LViwQOvXr9fKlSvl6+urfv36acSIESpVqpR8fHz05JNPKiwsTI0bN5YkRUREKCQkRL169dK0adMUHR2tF198UYMHD870DAIAAABAYHAgsbGx6t27t86cOSNfX1/VrVtXK1euVKtWrSRJ06dPl7Ozszp16qSkpCRFRkZq1qxZtvcXK1ZMy5Yt08CBAxUWFiYvLy/16dNHEyZMKKiSAAAAcJMjMDiQDz744JrjPTw8NHPmTM2cOdNymuDg4JvuKRcAAAC4eXEPAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACy5FHQDAKAwq/zcdwWyXPdiRtPulGqPW6mkFKcCaUN++n1Km4JuAgAUWpxhAAAAAGCJwAAAAADAEpckAXAYOb28p6hdngMAQG7iDAMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicAAAAAAwBKBwYFMnjxZd9xxh0qUKCF/f3916NBBhw8ftpvm0qVLGjx4sEqXLi1vb2916tRJMTExdtOcPHlSbdq0kaenp/z9/TVy5EhduXIlP0sBAACAgyAwOJANGzZo8ODB2rZtm6KiopScnKyIiAhdvHjRNs3w4cO1dOlSLVy4UBs2bNDp06fVsWNH2/iUlBS1adNGly9f1pYtW/TRRx9p/vz5eumllwqiJAAAANzk+EvPDuT777+3ez1//nz5+/tr9+7duvvuuxUfH68PPvhACxYsUIsWLSRJ8+bNU82aNbVt2zY1btxYq1at0sGDB7V69WoFBASofv36mjhxokaNGqVx48bJzc2tIEoDAADATYrA4MDi4+MlSaVKlZIk7d69W8nJyQoPD7dNU6NGDVWqVElbt25V48aNtXXrVtWpU0cBAQG2aSIjIzVw4EAdOHBADRo0yLCcpKQkJSUl2V4nJCRIkpKTk5WcnJwnteWHtLY7cg054ch1uxczOXufs7H7b1FQ1GpOv107wvad0+05w3zycV3fLJ+rI63n3HSjdRe1zwu5x8kYUzT2JoVMamqq2rVrp7i4OG3evFmStGDBAvXt29fu4F6S7rzzTt17772aOnWqBgwYoBMnTmjlypW28YmJifLy8tLy5cvVunXrDMsaN26cxo8fn2H4ggUL5OnpmcuVAQCAvJCYmKgePXooPj5ePj4+Bd0cOBDOMDiowYMHa//+/bawkJdGjx6tESNG2F4nJCSoYsWKioiIcOgOJzk5WVFRUWrVqpVcXV0Lujn5xpHrrj1u5fUnyoS7s9HEhqkas8tZSalOudyqm1NRq3n/uEhJjrV953R7Ti8/13Xa51zQHGk956YbrTvtCgEguwgMDmjIkCFatmyZNm7cqAoVKtiGBwYG6vLly4qLi5Ofn59teExMjAIDA23T7Nixw25+aU9RSpsmPXd3d7m7u2cY7urqWig66sJSR3Y5Yt1JKTd2MJSU6nTD83A0RaXm9NuyI2zfub1e8mNd32yfqSOs57yQ07qL4meF3MFTkhyIMUZDhgzRN998o7Vr16pKlSp240NDQ+Xq6qo1a9bYhh0+fFgnT55UWFiYJCksLEz79u1TbGysbZqoqCj5+PgoJCQkfwoBAACAw+AMgwMZPHiwFixYoG+//VYlSpRQdHS0JMnX11fFixeXr6+v+vXrpxEjRqhUqVLy8fHRk08+qbCwMDVu3FiSFBERoZCQEPXq1UvTpk1TdHS0XnzxRQ0ePDjTswgAAAAo2ggMDmT27NmSpHvuucdu+Lx58/TII49IkqZPny5nZ2d16tRJSUlJioyM1KxZs2zTFitWTMuWLdPAgQMVFhYmLy8v9enTRxMmTMivMgAg11V+7jtJ/z15aNqd/90fUBQuxQKA/EBgcCBZeaCVh4eHZs6cqZkzZ1pOExwcrOXLl+dm0+CAOKACAABZwT0MAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABL/B0GAACQJWl/IK+gZecP9P0+pU0+tQoovDjDAAAAAMASgQEAAACAJS5JAnLBzXKaPivSTuUDAABkBWcYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDAAAAAEsEBgAAAACWCAwAAAAALBEYAAAAAFgiMAAAAACwRGBwMBs3blTbtm0VFBQkJycnLV682G68MUYvvfSSypUrp+LFiys8PFxHjhyxm+bcuXPq2bOnfHx85Ofnp379+unChQv5WAUAAAAcBYHBwVy8eFH16tXTzJkzMx0/bdo0vfXWW5ozZ462b98uLy8vRUZG6tKlS7ZpevbsqQMHDigqKkrLli3Txo0bNWDAgPwqAQAAAA7EpaAbgOxp3bq1Wrdunek4Y4zefPNNvfjii2rfvr0k6eOPP1ZAQIAWL16sbt266dChQ/r++++1c+dONWzYUJL09ttv6/7779drr72moKCgfKsFAAAANz8CQyFy/PhxRUdHKzw83DbM19dXjRo10tatW9WtWzdt3bpVfn5+trAgSeHh4XJ2dtb27dv14IMPZphvUlKSkpKSbK8TEhIkScnJyUpOTs7DivJWWttzowb3YuaG55Ff3J2N3X+LAmouOopi3dR8bY68n0rvRvdbhemzQP4iMBQi0dHRkqSAgAC74QEBAbZx0dHR8vf3txvv4uKiUqVK2aZJb/LkyRo/fnyG4atWrZKnp2duNL1ARUVF3fA8pt2ZCw3JZxMbphZ0E/IdNRcdRbFuas7c8uXL86El+Sun+63ExMRcbgmKCgIDrmv06NEaMWKE7XVCQoIqVqyoiIgI+fj4FGDLbkxycrKioqLUqlUrubq63tC8ao9bmUutynvuzkYTG6ZqzC5nJaU6FXRz8gU1F42apaJZNzVfu+b94yLzqVV570b3W2lXCADZRWAoRAIDAyVJMTExKleunG14TEyM6tevb5smNjbW7n1XrlzRuXPnbO9Pz93dXe7u7hmGu7q63vCB9s0gN+pISnG8nXRSqpNDtvtGUHPRURTrpubMFYb9VHo53W8Vxs8C+YPAUIhUqVJFgYGBWrNmjS0gJCQkaPv27Ro4cKAkKSwsTHFxcdq9e7dCQ0MlSWvXrlVqaqoaNWpUUE23U/m57/JlOe7FjKbd+d/ZgaK2kwUAAMgqAoODuXDhgo4ePWp7ffz4ce3du1elSpVSpUqVNGzYML388suqXr26qlSpojFjxigoKEgdOnSQJNWsWVP33XefHnvsMc2ZM0fJyckaMmSIunXrxhOSAAAAkAGBwcHs2rVL9957r+112r0Fffr00fz58/Xss8/q4sWLGjBggOLi4tS0aVN9//338vDwsL3ns88+05AhQ9SyZUs5OzurU6dOeuutt/K9FgAAANz8CAwO5p577pEx1o+Rc3Jy0oQJEzRhwgTLaUqVKqUFCxbkRfMAAABQyPCXngEAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCAwAAAABLBAYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCWXgm4AAABAXqn83HcF3YQc+X1Km4JuAmDDGQYAAAAAlggMAAAAACwRGAAAAABYIjAAAAAAsERgAAAAAGCJwAAAAADAEoEBAAAAgCUCQxE1c+ZMVa5cWR4eHmrUqJF27NhR0E0CAADATYjAUAR98cUXGjFihMaOHas9e/aoXr16ioyMVGxsbEE3DQAAADcZAkMR9MYbb+ixxx5T3759FRISojlz5sjT01MffvhhQTcNAAAANxmXgm4A8tfly5e1e/dujR492jbM2dlZ4eHh2rp1a6bvSUpKUlJSku11fHy8JOncuXNKTk7O9Ta6XLmY6/PMdDmpRomJqXJJdlZKqlO+LPNmUBTrpuaiUbNUNOum5sJZ89mzZzMMS05OVmJios6ePStXV9dsz/P8+fOSJGPMDbcPRQuBoYj5+++/lZKSooCAALvhAQEB+uWXXzJ9z+TJkzV+/PgMw6tUqZInbcxPPQq6AQWkKNZNzUVHUaybmgufMq/n3bzPnz8vX1/fvFsACh0CA65r9OjRGjFihO11amqqzp07p9KlS8vJyXF/2UlISFDFihX1xx9/yMfHp6Cbk2+KYt3UXDRqlopm3dRcNGqWbrxuY4zOnz+voKCgPGgdCjMCQxFTpkwZFStWTDExMXbDY2JiFBgYmOl73N3d5e7ubjfMz88vr5qY73x8fIrUDidNUaybmouOolg3NRcdN1I3ZxaQE9z0XMS4ubkpNDRUa9assQ1LTU3VmjVrFBYWVoAtAwAAwM2IMwxF0IgRI9SnTx81bNhQd955p958801dvHhRffv2LeimAQAA4CZDYCiCunbtqr/++ksvvfSSoqOjVb9+fX3//fcZboQu7Nzd3TV27NgMl1sVdkWxbmouOopi3dRcdBTVulHwnAzP1gIAAABggXsYAAAAAFgiMAAAAACwRGAAAAAAYInAAAAAAMASgQEAAACAJQIDCrXJkyfrjjvuUIkSJeTv768OHTro8OHDdtNcunRJgwcPVunSpeXt7a1OnTpl+EvYjm7KlClycnLSsGHDbMMKY92nTp3Sww8/rNKlS6t48eKqU6eOdu3aZRtvjNFLL72kcuXKqXjx4goPD9eRI0cKsMU3JiUlRWPGjFGVKlVUvHhxVa1aVRMnTtTVD78rDDVv3LhRbdu2VVBQkJycnLR48WK78Vmp8dy5c+rZs6d8fHzk5+enfv366cKFC/lYRfZcq+bk5GSNGjVKderUkZeXl4KCgtS7d2+dPn3abh6OVrN0/XV9tSeeeEJOTk5688037YY7Wt1ZqfnQoUNq166dfH195eXlpTvuuEMnT560jS+M/TluLgQGFGobNmzQ4MGDtW3bNkVFRSk5OVkRERG6ePGibZrhw4dr6dKlWrhwoTZs2KDTp0+rY8eOBdjq3LVz507NnTtXdevWtRte2Or+559/1KRJE7m6umrFihU6ePCgXn/9dZUsWdI2zbRp0/TWW29pzpw52r59u7y8vBQZGalLly4VYMtzburUqZo9e7beeecdHTp0SFOnTtW0adP09ttv26YpDDVfvHhR9erV08yZMzMdn5Uae/bsqQMHDigqKkrLli3Txo0bNWDAgPwqIduuVXNiYqL27NmjMWPGaM+ePfr66691+PBhtWvXzm46R6tZuv66TvPNN99o27ZtCgoKyjDO0eq+Xs3Hjh1T06ZNVaNGDa1fv14///yzxowZIw8PD9s0ha0/x03IAEVIbGyskWQ2bNhgjDEmLi7OuLq6moULF9qmOXTokJFktm7dWlDNzDXnz5831atXN1FRUaZ58+Zm6NChxpjCWfeoUaNM06ZNLcenpqaawMBA8+qrr9qGxcXFGXd3d/O///0vP5qY69q0aWMeffRRu2EdO3Y0PXv2NMYUzpolmW+++cb2Ois1Hjx40EgyO3futE2zYsUK4+TkZE6dOpVvbc+p9DVnZseOHUaSOXHihDHG8Ws2xrruP//805QvX97s37/fBAcHm+nTp9vGOXrdmdXctWtX8/DDD1u+pzD257j5cIYBRUp8fLwkqVSpUpKk3bt3Kzk5WeHh4bZpatSooUqVKmnr1q0F0sbcNHjwYLVp08auPqlw1r1kyRI1bNhQDz30kPz9/dWgQQO99957tvHHjx9XdHS0Xc2+vr5q1KiRw9Z81113ac2aNfr1118lST/99JM2b96s1q1bSyqcNaeXlRq3bt0qPz8/NWzY0DZNeHi4nJ2dtX379nxvc16Ij4+Xk5OT/Pz8JBXemlNTU9WrVy+NHDlStWrVyjC+sNWdmpqq7777TrfeeqsiIyPl7++vRo0a2V22VBj7c9x8CAwoMlJTUzVs2DA1adJEtWvXliRFR0fLzc3NtpNNExAQoOjo6AJoZe75/PPPtWfPHk2ePDnDuMJY92+//abZs2erevXqWrlypQYOHKinnnpKH330kSTZ6goICLB7nyPX/Nxzz6lbt26qUaOGXF1d1aBBAw0bNkw9e/aUVDhrTi8rNUZHR8vf399uvIuLi0qVKlUoPodLly5p1KhR6t69u3x8fCQV3pqnTp0qFxcXPfXUU5mOL2x1x8bG6sKFC5oyZYruu+8+rVq1Sg8++KA6duyoDRs2SCqc/TluPi4F3QAgvwwePFj79+/X5s2bC7opee6PP/7Q0KFDFRUVZXeda2GWmpqqhg0batKkSZKkBg0aaP/+/ZozZ4769OlTwK3LG19++aU+++wzLViwQLVq1dLevXs1bNgwBQUFFdqaYS85OVldunSRMUazZ88u6Obkqd27d2vGjBnas2ePnJycCro5+SI1NVWS1L59ew0fPlySVL9+fW3ZskVz5sxR8+bNC7J5KEI4w4AiYciQIVq2bJnWrVunChUq2IYHBgbq8uXLiouLs5s+JiZGgYGB+dzK3LN7927Fxsbq9ttvl4uLi1xcXLRhwwa99dZbcnFxUUBAQKGru1y5cgoJCbEbVrNmTduTRNLqSv/kEEeueeTIkbazDHXq1FGvXr00fPhw21mlwlhzelmpMTAwULGxsXbjr1y5onPnzjn055AWFk6cOKGoqCjb2QWpcNa8adMmxcbGqlKlSrZ+7cSJE3r66adVuXJlSYWv7jJlysjFxeW6fVth689x8yEwoFAzxmjIkCH65ptvtHbtWlWpUsVufGhoqFxdXbVmzRrbsMOHD+vkyZMKCwvL7+bmmpYtW2rfvn3au3ev7V/Dhg3Vs2dP2/8XtrqbNGmS4ZG5v/76q4KDgyVJVapUUWBgoF3NCQkJ2r59u8PWnJiYKGdn+268WLFitl8lC2PN6WWlxrCwMMXFxWn37t22adauXavU1FQ1atQo39ucG9LCwpEjR7R69WqVLl3abnxhrLlXr176+eef7fq1oKAgjRw5UitXrpRU+Op2c3PTHXfccc2+rbDux3CTKei7roG8NHDgQOPr62vWr19vzpw5Y/uXmJhom+aJJ54wlSpVMmvXrjW7du0yYWFhJiwsrABbnTeufkqSMYWv7h07dhgXFxfzyiuvmCNHjpjPPvvMeHp6mk8//dQ2zZQpU4yfn5/59ttvzc8//2zat29vqlSpYv79998CbHnO9enTx5QvX94sW7bMHD9+3Hz99demTJky5tlnn7VNUxhqPn/+vPnxxx/Njz/+aCSZN954w/z444+2JwJlpcb77rvPNGjQwGzfvt1s3rzZVK9e3XTv3r2gSrqua9V8+fJl065dO1OhQgWzd+9eu74tKSnJNg9Hq9mY66/r9NI/JckYx6v7ejV//fXXxtXV1bz77rvmyJEj5u233zbFihUzmzZtss2jsPXnuPkQGFCoScr037x582zT/Pvvv2bQoEGmZMmSxtPT0zz44IPmzJkzBdfoPJI+MBTGupcuXWpq165t3N3dTY0aNcy7775rNz41NdWMGTPGBAQEGHd3d9OyZUtz+PDhAmrtjUtISDBDhw41lSpVMh4eHuaWW24xL7zwgt1BY2Goed26dZl+j/v06WOMyVqNZ8+eNd27dzfe3t7Gx8fH9O3b15w/f74Aqsmaa9V8/Phxy75t3bp1tnk4Ws3GXH9dp5dZYHC0urNS8wcffGCqVatmPDw8TL169czixYvt5lEY+3PcXJyMuepPggIAAADAVbiHAQAAAIAlAgMAAAAASwQGAAAAAJYIDAAAAAAsERgAAAAAWCIwAAAAALBEYAAAAABgicAAAAAAwBKBAQAAAIAlAgMAAAAASwQGAAAAAJb+DwOMaoFDIzqfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building Vector Database using FAISS\n",
        "**Nearest Neighbour Search**\n",
        " - GTE embeddings work well with `cosine similarity`\n",
        " - for other methods (if vectors are not normalized) try:\n",
        "  - `Dot product`\n",
        "  - `Euclidean Distance`"
      ],
      "metadata": {
        "id": "0fMYqJJRasoa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import FAISS\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_community.vectorstores.utils import DistanceStrategy\n",
        "\n",
        "\n",
        "embedding_model = HuggingFaceEmbeddings(\n",
        "    model_name = EMB_MODEL_CKP,\n",
        "    multi_process = True,\n",
        "    model_kwargs={\"device\": \"cuda\"},\n",
        "    encode_kwargs={\"normalize_embeddings\": True},  # set True for cosine similarity\n",
        ")\n",
        "\n",
        "#create FAISS indices for approximate nearest neighbour search\n",
        "KNOWLEDGE_VECTOR_DATABASE = FAISS.from_documents(\n",
        "    dataset_docs, embedding_model, distance_strategy=DistanceStrategy.COSINE\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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        },
        "id": "e4fqNhIwaNka",
        "outputId": "bafccaa2-ac50-46b5-9ad8-fe1bcf8d1a1c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9fba20cdea74acb9b7e80645ec4041c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/68.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a19f5c1c6004b42bc0e302a491c80ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/57.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50ebd010aeb844278c1e8d82d409bcb1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/583 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/66.7M [00:00<?, ?B/s]"
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        {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_search_result(query, vector_db):\n",
        "  \"\"\"\n",
        "  given a \"query\" search for top k \"fullplots\" embedded in vector database\n",
        "  \"\"\"\n",
        "  #get top k documents similar to \"query\"\n",
        "  retrieved_docs = vector_db.similarity_search(query=user_query, k=3)\n",
        "  search_result = \"\"\n",
        "  for result in retrieved_docs:\n",
        "      retrieved_plot = result.page_content if result.page_content else \"N/A\"\n",
        "      retrieved_title = result.metadata['title'] if result.metadata['title'] else \"N/A\"\n",
        "      retrieved_genre = result.metadata['genres'] if result.metadata['genres'] else \"N/A\"\n",
        "      search_result += f\"Title: {retrieved_title}, Plot: {retrieved_plot} , Genres: {retrieved_genre}\\n\" #add Title, Plot and Genres to the context string\n",
        "\n",
        "  return search_result"
      ],
      "metadata": {
        "id": "SfEESgMLdvO-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Gemma instructction tuned model"
      ],
      "metadata": {
        "id": "g7Z4m6YsfBE4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "bZK-Pj8-fjvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from transformers import pipeline\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\")\n",
        "# CPU Enabled uncomment below 👇🏽\n",
        "# model = AutoModelForCausalLM.from_pretrained(\"google/gemma-2b-it\")\n",
        "# GPU Enabled use below 👇🏽\n",
        "model = AutoModelForCausalLM.from_pretrained(\"google/gemma-2b-it\", device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "RLTJO_8ofAg4",
        "outputId": "6d788e48-feb1-455c-d599-7f592dfad745"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conduct query with retrieval of sources\n",
        "user_query = \"What is a good romance movie to watch and why?\"\n",
        "retrieved_results = get_search_result(user_query, KNOWLEDGE_VECTOR_DATABASE)\n",
        "combined_information = f\"Answer the following query using the provided context. Please provide the movie title in the begining.\\n <CONTEXT>:\\n{retrieved_results}. \\n <QUERY>: {user_query}\"\n",
        "\n",
        "#chat template for gemma model conversation\n",
        "chat = [\n",
        "    { \"role\": \"user\", \"content\": combined_information },\n",
        "]\n",
        "prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)"
      ],
      "metadata": {
        "id": "Dv9q6ohfgbBD"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Moving tensors to GPU\n",
        "input_ids = tokenizer(combined_information, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "pipe = pipeline(model=model,\n",
        "                tokenizer = tokenizer,\n",
        "                task=\"text-generation\",\n",
        "                return_full_text=False,\n",
        "                max_new_tokens=500,\n",
        "                do_sample=True,\n",
        "                temperature=2.1,\n",
        "                top_k=50,\n",
        "                top_p=1,\n",
        "                #repetition_penalty=1.1,\n",
        "                num_return_sequences=1,\n",
        "                #add_special_tokens=True,\n",
        "                )\n",
        "print(f\"Query: {user_query}\\n\")\n",
        "print(pipe(prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "id": "vaBBWPbSe4oR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d67e283-8f22-4527-c7d7-13330d1f756d"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: What is a good romance movie to watch and why?\n",
            "\n",
            "The movie 'Titanic' is a popular romantic epic, that follows the romance of lovers Isabella Paradis(Catherine Zeta Jones) and James Cameron (Leonardo DiCaprio). They relive a romantic experience together decades after. The beautiful music by Celine Dion and the stunning visuals combined to be memorable making it  such an extraordinary.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_information"
      ],
      "metadata": {
        "id": "Zy5wp5SWorix",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "38275d02-7d8c-4065-f7ba-9dfde4cd3766"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Answer the following query using the provided context. Please provide the movie title in the begining.\\n <CONTEXT>:\\nTitle: Titanic, Plot: The plot focuses on the romances of two couples upon the doomed ship's maiden voyage. Isabella Paradine (Catherine Zeta-Jones) is a wealthy woman mourning the loss of her aunt, who reignites a romance with former flame Wynn Park (Peter Gallagher). Meanwhile, a charming ne'er-do-well named Jamie Perse (Mike Doyle) steals a ticket for the ship, and falls for a sweet innocent Irish girl on board. But their romance is threatened by the villainous Simon Doonan (Tim Curry), who has discovered about the ticket and makes Jamie his unwilling accomplice, as well as having sinister plans for the girl. , Genres: ['Action', 'Drama', 'History']\\nTitle: Shut Up and Kiss Me!, Plot: Ryan and Pete are 27-year old best friends in Miami, born on the same day and each searching for the perfect woman. Ryan is a rookie stockbroker living with his psychic Mom. Pete is a slick surfer dude yet to find commitment. Each meets the women of their dreams on the same day. Ryan knocks heads in an elevator with the gorgeous Jessica, passing out before getting her number. Pete falls for the insatiable Tiara, but Tiara's uncle is mob boss Vincent Bublione, charged with her protection. This high-energy romantic comedy asks to what extent will you go for true love? , Genres: ['Action', 'Comedy', 'Romance']\\nTitle: Romeo Must Die, Plot: In this modern day Romeo and Juliet, kung fu action star Jet Li plays Romeo to hip-hop singer, Aaliyah Haughton's Juliet. Li is an ex-cop investigating the murder of his brother, who had ties with the Chinese mafia in America. Aaliyah plays the daughter of the American mob boss. Neither side approves of their romance, so, obviously, kung fu action ensues, with a soundtrack by Aaliyah. , Genres: ['Action', 'Crime', 'Thriller']\\n. \\n <QUERY>: What is a good romance movie to watch and why?\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conduct query with retrieval of sources\n",
        "user_query = \"What pirate adventure movie mentions Jack Sparrow and what is the plot?\"\n",
        "retrieved_results = get_search_result(user_query, KNOWLEDGE_VECTOR_DATABASE)\n",
        "combined_information = f\"Answer the following query using the provided context. Please provide the movie title in the begining.\\n <CONTEXT>:\\n{retrieved_results}. \\n <QUERY>: {user_query}\"\n",
        "\n",
        "#chat template for gemma model conversation\n",
        "chat = [\n",
        "    { \"role\": \"user\", \"content\": combined_information },\n",
        "]\n",
        "prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)"
      ],
      "metadata": {
        "id": "n7kNCn-o9oJc"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Moving tensors to GPU\n",
        "input_ids = tokenizer(combined_information, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "pipe = pipeline(model=model,\n",
        "                tokenizer = tokenizer,\n",
        "                task=\"text-generation\",\n",
        "                return_full_text=False,\n",
        "                max_new_tokens=500,\n",
        "                do_sample=True,\n",
        "                temperature=2.1,\n",
        "                #top_k=50,\n",
        "                #top_p=1,\n",
        "                #repetition_penalty=1.1,\n",
        "                num_return_sequences=1,\n",
        "                #add_special_tokens=True,\n",
        "                )\n",
        "print(f\"Query: {user_query}\\n\")\n",
        "print(pipe(prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmsHiF7X_7R1",
        "outputId": "af2b1f9e-0d61-42b6-a136-1b3d8b4d4ecd"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query: What pirate adventure movie mentions Jack Sparrow and what is the plot?\n",
            "\n",
            "**Title: Pirates of the Caribbean: The Curse of the Black Pearl, Plot:**\n",
            "This blockbuster follows the epic voyage of Captain Jack Sparrow and Will Turner as they tirelessly search for Elizabeth Swann captured by Barbossa, leader of an insidious cursed crew who needs souls or lives are permanently eradicated with each generation. Along the way, pirates clash in bloody fight choreographed battles reminiscent of \"Dead Man's Chest\". His relentless pursuit culminates the fateful death of The Beast with the Kraken\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retrieved_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "IbcvQ9mU_-Vb",
        "outputId": "4912a1ab-f143-4d3f-e161-db222798e888"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Title: Pirates of the Caribbean: The Curse of the Black Pearl, Plot: This swash-buckling tale follows the quest of Captain Jack Sparrow, a savvy pirate, and Will Turner, a resourceful blacksmith, as they search for Elizabeth Swann. Elizabeth, the daughter of the governor and the love of Will\\'s life, has been kidnapped by the feared Captain Barbossa. Little do they know, but the fierce and clever Barbossa has been cursed. He, along with his large crew, are under an ancient curse, doomed for eternity to neither live, nor die. That is, unless a blood sacrifice is made. , Genres: [\\'Action\\', \\'Adventure\\', \\'Fantasy\\']\\nTitle: Pirates of the Caribbean: Dead Man\\'s Chest, Plot: Once again we\\'re plunged into the world of sword fights and \"savvy\" pirates. Captain Jack Sparrow is reminded he owes a debt to Davy Jones, who captains the flying Dutchman, a ghostly ship, with a crew from hell. Facing the \"locker\" Jack must find the heart of Davy Jones but to save himself he must get the help of quick-witted Will Turner and Elizabeth Swan. If that\\'s not complicated enough, Will and Elizabeth are sentenced to hang, unless will can get Lord Cutler Beckett Jack\\'s compass, Will is forced to join another crazy adventure with Jack. , Genres: [\\'Action\\', \\'Adventure\\', \\'Fantasy\\']\\nTitle: The Black Swan, Plot: When notorious pirate Henry Morgan is made governor of Jamaica, he enlists the help of some of his former partners in ridding the Carribean of Buccaneers. When one of them apparently abducts the previous governor\\'s pretty daughter and joins up with the rebels, things are set for a fight. , Genres: [\\'Action\\', \\'Adventure\\', \\'Drama\\']\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M45okn4OACH6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}