Query_TigerGraph.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGh0fvdnqZZX"
      },
      "source": [
        "# Abhyas Edu Context\n",
        "### Note: Choose Nvidia GPU in colab for faster computation. \n",
        "### You can do that from the top bar at Runtime/change runtime type/ and choose **GPU**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5DJ4OwUdrIl"
      },
      "source": [
        "## ***Important***: Do not forget to add your TigerGraph Graph Studio credentials in the PyTigerGraph section at the end."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyE_zUvpsJQW",
        "outputId": "f25b6eb6-2fdb-4840-b3ab-d6a39dccdbc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Wed Apr 20 14:56:53 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    27W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# this will tell you if you have an Nvidia GPU attached to your machine or not\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nrGO0uxt1c54",
        "outputId": "0a52bf43-925d-4dad-f284-51f559e6c677"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pdfplumber\n",
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            "Building wheels for collected packages: pdfplumber\n",
            "  Building wheel for pdfplumber (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pdfplumber: filename=pdfplumber-0.6.0-py3-none-any.whl size=33688 sha256=6cb9a2efb26033832a12c7c492d56e605a5d282d7dab41575d38bcbf39daf724\n",
            "  Stored in directory: /root/.cache/pip/wheels/58/56/fe/2e93d842ffa9ea97746c1ab253d43502ed61c0689361a0224e\n",
            "Successfully built pdfplumber\n",
            "Installing collected packages: cryptography, Wand, Pillow, pdfminer.six, pdfplumber\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Pillow-9.1.0 Wand-0.6.7 cryptography-36.0.2 pdfminer.six-20211012 pdfplumber-0.6.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.7.0-py3-none-any.whl size=171046 sha256=a6e799fd5bfc669e2bbb537ce7a6dded55c1cb2a0aab2ad5778db411969b3928\n",
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            "Successfully installed clean-text-0.6.0 emoji-1.7.0 ftfy-6.1.1\n",
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            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (6.0)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.8.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.4)\n",
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          ]
        }
      ],
      "source": [
        "# install dependencies\n",
        "# ignore pip dependency warnings/errors (These are Colab specific)\n",
        "\n",
        "!pip install pdfplumber\n",
        "!pip install clean-text\n",
        "!pip install nltk\n",
        "!pip install keybert\n",
        "!pip install unidecode\n",
        "!pip install transformers\n",
        "!pip install sentence-transformers\n",
        "!pip install pyTigerGraph -q\n",
        "!pip install tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "u6s6EbH8qUTC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EejqvTlY1c56",
        "outputId": "9afd147b-a991-4580-e7d1-02c7affab2e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#downloading assets\n",
        "\n",
        "from nltk import download\n",
        "\n",
        "download('stopwords')\n",
        "download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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      ],
      "source": [
        "# keyword extraction\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from keybert import KeyBERT\n",
        "from cleantext import clean\n",
        "\n",
        "stop_words = stopwords.words('english')\n",
        "\n",
        "kw_model = KeyBERT()\n",
        "\n",
        "def extract_keywords(sentence):\n",
        "    lis = kw_model.extract_keywords(sentence, keyphrase_ngram_range=(1, 1), stop_words = stop_words, top_n = 3)\n",
        "    clean_list = []\n",
        "    for each in lis:\n",
        "        clean_each = clean(each[0].strip(), no_numbers=True, replace_with_number=\"\", no_punct=True)\n",
        "        if '' != clean_each:\n",
        "            clean_list.append(clean_each)\n",
        "    return clean_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jr3BZc9X1c57",
        "outputId": "eaf404db-ed09-4e43-9a28-4def0b12cf10"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['war', 'bad']"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# call the method once to download the required all-MiniLM-L6-v2 (distilled BERT base) model assets\n",
        "\n",
        "extract_keywords('war is bad')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSVIsKuQk4--",
        "outputId": "98532c7e-a1d3-429b-873f-c9b93008f07b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['tigergraph', 'graphs', 'data']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extract_keywords('TigerGraph uses Graphs to store data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rrqxlLrs1c58"
      },
      "outputs": [],
      "source": [
        "# embedding extraction\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "def extract_embedding(sentence):\n",
        "    return model.encode(sentence, convert_to_tensor=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "5VucDIxK1c59"
      },
      "outputs": [],
      "source": [
        "# call the method once to download the required all-MiniLM-L6-v2 transfer learning model assets \n",
        "# This will not download again if downloaded at the above key word extraction step\n",
        "# only the first call takes time due to assets being downloaded\n",
        "\n",
        "a = extract_embedding('war is bad')\n",
        "b = extract_embedding('war is never good')\n",
        "c = extract_embedding('this is a sunflower')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "IV2osWl81c59"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import util\n",
        "\n",
        "def similarity_score(embedding1, embedding2):\n",
        "    return float(util.pytorch_cos_sim(embedding1, embedding2)[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDMc-Uyk1c5-",
        "outputId": "336b496b-ca87-4e71-c9bf-cfeb5e8d30bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8693468570709229"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "similarity_score(a,b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8F8QMgN21c5-",
        "outputId": "dd96ead4-79e1-4119-8045-baa36abb13a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.09055604040622711"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "similarity_score(a,c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX1ITq0t1c5_",
        "outputId": "c79859c3-f808-46b2-9298-5d89b3b97709"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.06450851261615753"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "similarity_score(b,c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "eIzyJKZ_JgVG"
      },
      "outputs": [],
      "source": [
        "# stemmer\n",
        "\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "\n",
        "englishStemmer=SnowballStemmer(\"english\")\n",
        "\n",
        "def stem(word):\n",
        "    return englishStemmer.stem(word)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5cnKu4b4JlCi",
        "outputId": "0e83a8a9-fcdd-4d9c-9f58-1c9645feb8f4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'have'"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stem('having')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7PSmOQX1c6A"
      },
      "source": [
        "# PyTigerGraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "BoVF449J1c6A"
      },
      "outputs": [],
      "source": [
        "import pyTigerGraph as tg\n",
        "\n",
        "# connection parameters\n",
        "# hostName is the TigerGraph solution URL\n",
        "# Relace these with your credentials\n",
        "hostName = \"https://abhyas-edu-context.i.tgcloud.io\"\n",
        "graphName = \"edu_context\"\n",
        "userName = \"tigergraph\"\n",
        "password = \"thisisours\"\n",
        "\n",
        "# establish the connection to the TigerGraph Solution\n",
        "conn = tg.TigerGraphConnection(host=hostName, username=userName, password=password)\n",
        "\n",
        "# set the name of the graph that we want to connect to\n",
        "conn.graphname = graphName\n",
        "\n",
        "# create a secret\n",
        "secret = conn.createSecret()\n",
        "# use the secret to get a token\n",
        "authToken = conn.getToken(secret)[0]\n",
        "\n",
        "# connect to graph with token\n",
        "conn = tg.TigerGraphConnection(host=hostName, username=userName, password=password, graphname=graphName, apiToken=authToken)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "BuJbWVff1c6B"
      },
      "outputs": [],
      "source": [
        "# result = conn.runInstalledQuery(\"get_edu_context\", params={\"inName\": stem(\"software\")})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
        "id": "YmpnZ5701c6B",
        "jupyter": {
          "outputs_hidden": true
        }
      },
      "outputs": [],
      "source": [
        "# result[0]['RES'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "2nJBw6Ku1c6C"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "def get_edu_context(sentence, top_n=10):\n",
        "    sentence_embedding = extract_embedding(sentence)\n",
        "    key_words = extract_keywords(sentence)\n",
        "    edu_context_res = []\n",
        "    print(\"Key words:\", key_words,\"\\n\")\n",
        "    print(\"Querying TigerGraph...\")\n",
        "    for word in key_words:\n",
        "        this_res = conn.runInstalledQuery(\"get_edu_context\", params={\"inName\": stem(word)})\n",
        "        if this_res[0]['RES'] != [] and this_res[0]['RES'][0]['attributes']['@this_data'] != []:\n",
        "            edu_context_res.append(this_res[0]['RES'][0])\n",
        "    \n",
        "    top_matches = []\n",
        "    print(\"TigerGraph response received.\")\n",
        "    print('Extracting embeddings and calculating similarity...\\n')\n",
        "\n",
        "    for each in edu_context_res:\n",
        "        for sent_data in tqdm(each['attributes']['@this_data']):\n",
        "            this_sim_score = similarity_score(extract_embedding(sent_data['sentence']), sentence_embedding)\n",
        "            grade = sent_data['book'].split('_')[0]\n",
        "            subject = sent_data['book'].split('_')[1]\n",
        "            top_matches.append({'sentence': sent_data['sentence'], 'page': sent_data['page'], 'grade':grade, 'subject':subject, 'similarity':this_sim_score })\n",
        "            \n",
        "    top_matches = [dict(t) for t in {tuple(d.items()) for d in top_matches}]\n",
        "    \n",
        "    return sorted(top_matches, key=lambda x: (x[\"similarity\"], x['grade'], x['subject']), reverse=True)[:top_n]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rzrm9czk8jU4"
      },
      "source": [
        "# Now, let's find ***Edu Context***!!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "0mvVFAR08g23"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "NLMRgixjK3Kz"
      },
      "outputs": [],
      "source": [
        "# comment this cell if you're not using google colab\n",
        "\n",
        "from google.colab import data_table\n",
        "data_table.enable_dataframe_formatter()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "ejthTfaT0-35",
        "outputId": "a35f5414-2685-456c-e0a9-67960177c699"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key words: ['computers', 'humanity', 'important'] \n",
            "\n",
            "Querying TigerGraph...\n",
            "TigerGraph response received.\n",
            "Extracting embeddings and calculating similarity...\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 144/144 [00:01<00:00, 98.33it/s]\n",
            "100%|██████████| 47/47 [00:00<00:00, 99.90it/s] \n",
            "100%|██████████| 45/45 [00:00<00:00, 94.03it/s]\n"
          ]
        },
        {
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              "                                            sentence  page grade subject  \\\n",
              "0   the manner, in which computers have revolutio...    12    11  python   \n",
              "1   may it be the field of education and research...    12    11  python   \n",
              "2   the computers that we use today belong to thi...    20    11  python   \n",
              "3   but the computers of today have evolved over ...    12    11  python   \n",
              "4   in simple terms the computers of this generat...    21    11  python   \n",
              "5   computers are very versatile as they do lot o...    21    11  python   \n",
              "6   the digital computers are used in industrial,...    26    11  python   \n",
              "7   let us first have a look at the functional co...    22    11  python   \n",
              "8   today no organization can function without a ...    12    11  python   \n",
              "9   we use objects to capture data, which then ca...   112    11  python   \n",
              "\n",
              "   similarity  \n",
              "0    0.701781  \n",
              "1    0.618278  \n",
              "2    0.599892  \n",
              "3    0.595380  \n",
              "4    0.589639  \n",
              "5    0.552957  \n",
              "6    0.537364  \n",
              "7    0.518935  \n",
              "8    0.517997  \n",
              "9    0.513228  "
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result = get_edu_context('computers are really important for the humanity')\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "O-1ENbhYJugA",
        "outputId": "cf66545c-2d84-4eb3-8959-f017ee6e395f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key words: ['photosynthesis', 'earth', 'life']\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:00<00:00, 98.50it/s]\n",
            "100%|██████████| 155/155 [00:01<00:00, 96.04it/s]\n",
            "100%|██████████| 76/76 [00:00<00:00, 97.42it/s]\n"
          ]
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            "text/plain": [
              "                                             sentence  page grade  subject  \\\n",
              "0    in the absence of photosynthesis, life would ...    17     7  science   \n",
              "1                                      photosynthesis    98     6  science   \n",
              "2    n chlorophyll, water, carbon dioxide and sunl...    22     7  science   \n",
              "3    of all organisms is produced during light ene...    17     7  science   \n",
              "4    carbon and energy requirements of the autotro...   105    10  science   \n",
              "5    absence of photosynthesis! in the absence of ...    16     7  science   \n",
              "6    n oxygen released in photosynthesis is utilis...    22     7  science   \n",
              "7    n green plants synthesise food for themselves...    22     7  science   \n",
              "8        farmers would need process of photosynthesis   214     9  science   \n",
              "9    where do plants get each of the raw materials...   111    10  science   \n",
              "10   among multi-cellular organisms, whether they ...   162    10  science   \n",
              "11   think of the moon, which is result of life on...   199     9  science   \n",
              "12        another process all life-forms on the earth   199     9  science   \n",
              "13                    since process of photosynthesis    18     7  science   \n",
              "14   that plants can do photosynthesis is also a c...   161    10  science   \n",
              "15   which organisms can make organic compounds li...   267    10  science   \n",
              "16                         dioxide for photosynthesis    89     8  science   \n",
              "17   n complex chemical substances such as carbohy...    22     7  science   \n",
              "18   whether herbivores or carnivores, oxygen carb...   225     7  science   \n",
              "19         n oxygen is produced during photosynthesis    22     7  science   \n",
              "\n",
              "    similarity  \n",
              "0     0.740098  \n",
              "1     0.689277  \n",
              "2     0.654601  \n",
              "3     0.649301  \n",
              "4     0.640807  \n",
              "5     0.633923  \n",
              "6     0.607748  \n",
              "7     0.595069  \n",
              "8     0.593553  \n",
              "9     0.584987  \n",
              "10    0.575405  \n",
              "11    0.571198  \n",
              "12    0.567891  \n",
              "13    0.567005  \n",
              "14    0.565648  \n",
              "15    0.564615  \n",
              "16    0.559892  \n",
              "17    0.559020  \n",
              "18    0.555468  \n",
              "19    0.541382  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result = get_edu_context('all life on the earth depend on photosynthesis for food and energy', top_n=20)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "COzBwOiu9n3-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ryk6ujoncnVp"
      },
      "source": [
        "## Try something on your own!\n",
        "#### Tip: Go to Data/ncert-science folder, open any one of the textbooks (pages \"11\" to \"end-5\") and search for related topics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "WNupOmHmcuj9",
        "outputId": "cdccd21e-fb6d-494c-9dff-e074f946ae39"
      },
      "outputs": [],
      "source": [
        "# type something here\n",
        "your_query = \"gravitational force\"\n",
        "result = get_edu_context(your_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsRlMXZi9pQV"
      },
      "source": [
        "# *The End... And The Begining... :)*\n"
      ]
    }
  ],
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