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@@ -0,0 +1,8087 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/kerondavid-debug/lab-extractive-question-answering/blob/main/lab_extractive_question_answering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "split-aluminum",
+      "metadata": {
+        "id": "split-aluminum",
+        "papermill": {
+          "duration": 0.048394,
+          "end_time": "2021-04-15T21:06:39.560571",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.512177",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "# LAB | Extractive Question Answering"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e3f44179",
+      "metadata": {
+        "id": "e3f44179"
+      },
+      "source": [
+        "<div style=\"border:1px solid #ccc; border-radius:6px; text-align: center;\">\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "**Run this notebook in [Google Colab](https://colab.research.google.com/) for GPU acceleration.**\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "prospective-turner",
+      "metadata": {
+        "id": "prospective-turner",
+        "papermill": {
+          "duration": 0.045573,
+          "end_time": "2021-04-15T21:06:39.651272",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.605699",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "This notebook demonstrates how Pinecone helps you build an extractive question-answering application. To build an extractive question-answering system, we need three main components:\n",
+        "\n",
+        "- A vector index to store and run semantic search\n",
+        "- A retriever model for embedding context passages\n",
+        "- A reader model to extract answers\n",
+        "\n",
+        "We will use the SQuAD dataset, which consists of **questions** and **context** paragraphs containing question **answers**. We generate embeddings for the context passages using the retriever, index them in the vector database, and query with semantic search to retrieve the top k most relevant contexts containing potential answers to our question. We then use the reader model to extract the answers from the returned contexts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "oC3GG-dWkZJ6",
+      "metadata": {
+        "id": "oC3GG-dWkZJ6"
+      },
+      "source": [
+        "Let's get started by installing the packages needed for notebook to run:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "terminal-export",
+      "metadata": {
+        "id": "terminal-export",
+        "papermill": {
+          "duration": 0.044413,
+          "end_time": "2021-04-15T21:06:39.741951",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.697538",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "# Install Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "expressed-executive",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-04-15T21:06:39.845309Z",
+          "iopub.status.busy": "2021-04-15T21:06:39.842494Z",
+          "iopub.status.idle": "2021-04-15T21:08:22.163939Z",
+          "shell.execute_reply": "2021-04-15T21:08:22.164616Z"
+        },
+        "id": "expressed-executive",
+        "papermill": {
+          "duration": 102.376674,
+          "end_time": "2021-04-15T21:08:22.165052",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.788378",
+          "status": "completed"
+        },
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fdbcc726-2510-41e6-c7fe-e5e7d9ca78bb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m88.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m515.2/515.2 kB\u001b[0m \u001b[31m41.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m427.3/427.3 kB\u001b[0m \u001b[31m34.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m28.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m447.8/447.8 kB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.1/50.1 MB\u001b[0m \u001b[31m54.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m117.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\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",
+            "diffusers 0.39.0 requires huggingface-hub<2.0,>=0.34.0, but you have huggingface-hub 0.26.5 which is incompatible.\n",
+            "gradio 6.20.0 requires huggingface-hub<2.0,>=1.2.0, but you have huggingface-hub 0.26.5 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q \\\n",
+        "    transformers==4.46.3 \\\n",
+        "    datasets==4.5.0 \\\n",
+        "    pinecone==5.4.2 \\\n",
+        "    sentence-transformers==3.3.1 \\\n",
+        "    huggingface_hub==0.26.5\n",
+        "\n",
+        "!rm -rf ~/.cache/huggingface\n",
+        "\n",
+        "\n",
+        "# NOTE: we don't pin torch on purpose.\n",
+        "# Reason: Colab already comes with a torch version that matches its GPU setup (Cuda),\n",
+        "# so forcing a different fixed version could break things later when Colab updates its GPU/software.\n",
+        "#\n",
+        "# For that same reason, we also don't pin transitive dependencies like numpy, huggingface_hub,\n",
+        "# tokenizers, safetensors, or accelerate. Colab preinstalls many packages (jax, cupy,\n",
+        "# diffusers, gradio, etc.) that already require newer versions of these, so forcing\n",
+        "# old pins here creates the same kind of conflict as pinning torch would."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2DCPtl6IhgSz",
+      "metadata": {
+        "id": "2DCPtl6IhgSz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b30128fd-b77a-419c-869b-2991ec3d4a06"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "⚠️ WARNING:\n",
+            "This code cell will restart the kernel to load the newly installed packages...\n",
+            "If you used 'Run all', it will stop here — that's expected.\n",
+            "See the note below on how to continue running the rest of the notebook.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Force-kill the kernel process to restart it, ensuring the newly\n",
+        "# installed/uninstalled package versions are loaded in a fresh process\n",
+        "\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "print(\n",
+        "    \"⚠️ WARNING:\\n\"\n",
+        "    \"This code cell will restart the kernel to load the newly installed packages...\\n\"\n",
+        "    \"If you used 'Run all', it will stop here — that's expected.\\n\"\n",
+        "    \"See the note below on how to continue running the rest of the notebook.\",\n",
+        "    flush=True,\n",
+        ")\n",
+        "sys.stdout.flush()\n",
+        "\n",
+        "os.kill(os.getpid(), 9)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d339487b",
+      "metadata": {
+        "id": "d339487b"
+      },
+      "source": [
+        "<div style=\"border:1px solid #ccc; border-radius:6px; padding:12px;\">\n",
+        "\n",
+        "⚠️ **Do not use \"Run all\" for this whole notebook in one go.** The cell above force-restarts the kernel (`os.kill`) so the newly installed packages load cleanly. Colab's \"Run all\" cannot continue past a kernel restart — it will always stop right after that cell, no matter how many times you retry it.\n",
+        "\n",
+        "Instead, run it in two steps:\n",
+        "1. Run only the two cells above (install + restart). Wait for the kernel to finish restarting (a few seconds).\n",
+        "2. Click on the next cell below, then use **Runtime → Run after** (or manually run the remaining cells one by one / select them and press Shift+Enter). Do not click \"Run all\" again, since it would just re-trigger the restart at the same spot.\n",
+        "\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2a62f4d8",
+      "metadata": {
+        "id": "2a62f4d8"
+      },
+      "source": [
+        "# Setup for Pinecone"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "WDBzNVM4hqTB",
+      "metadata": {
+        "id": "WDBzNVM4hqTB"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "from dotenv import load_dotenv, find_dotenv\n",
+        "_ = load_dotenv(find_dotenv())\n",
+        "\n",
+        "#\n",
+        "# For this notebook, you'll need a Pinecone API key,\n",
+        "# to get one, sign up for free at https://app.pinecone.io/,\n",
+        "# then go to \"API Keys\" and copy the default key (or create a new one).\n",
+        "#\n",
+        "# Once you have your Pinecone API key,\n",
+        "# load it from Colab's \"Secrets\" manager\n",
+        "# (the key icon in the left sidebar).\n",
+        "# Add a secret named PINECONE_API_KEY there and enable notebook access.\n",
+        "#\n",
+        "PINECONE_API_KEY= userdata.get('PINECONE_API_KEY')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "29ad3840",
+      "metadata": {
+        "id": "29ad3840"
+      },
+      "source": [
+        "# Load Dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "hgIieQukgagu",
+      "metadata": {
+        "id": "hgIieQukgagu"
+      },
+      "source": [
+        "Now let's load the SQUAD dataset from the HuggingFace Model Hub. We load the dataset into a pandas dataframe and filter the title, question, and context columns, and we drop any duplicate context passages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "J250IJeh7NIb",
+      "metadata": {
+        "id": "J250IJeh7NIb"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "# load the squad dataset into a pandas dataframe\n",
+        "df = load_dataset(\"rajpurkar/squad\", split=\"train\").to_pandas()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "FcmeNO97dHDO",
+      "metadata": {
+        "id": "FcmeNO97dHDO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "baccccfe-37a3-482a-a8f4-55ef2870cf49"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                          title  \\\n",
+              "0      University_of_Notre_Dame   \n",
+              "5      University_of_Notre_Dame   \n",
+              "10     University_of_Notre_Dame   \n",
+              "15     University_of_Notre_Dame   \n",
+              "20     University_of_Notre_Dame   \n",
+              "...                         ...   \n",
+              "87574                 Kathmandu   \n",
+              "87579                 Kathmandu   \n",
+              "87584                 Kathmandu   \n",
+              "87589                 Kathmandu   \n",
+              "87594                 Kathmandu   \n",
+              "\n",
+              "                                                 context  \n",
+              "0      Architecturally, the school has a Catholic cha...  \n",
+              "5      As at most other universities, Notre Dame's st...  \n",
+              "10     The university is the major seat of the Congre...  \n",
+              "15     The College of Engineering was established in ...  \n",
+              "20     All of Notre Dame's undergraduate students are...  \n",
+              "...                                                  ...  \n",
+              "87574  Institute of Medicine, the central college of ...  \n",
+              "87579  Football and Cricket are the most popular spor...  \n",
+              "87584  The total length of roads in Nepal is recorded...  \n",
+              "87589  The main international airport serving Kathman...  \n",
+              "87594  Kathmandu Metropolitan City (KMC), in order to...  \n",
+              "\n",
+              "[18891 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d41f3060-bbe9-4388-8396-83a171d580e8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>context</th>\n",
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+              "    <tr>\n",
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+              "      <td>University_of_Notre_Dame</td>\n",
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+              "      <td>University_of_Notre_Dame</td>\n",
+              "      <td>The College of Engineering was established in ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>University_of_Notre_Dame</td>\n",
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+              "    <tr>\n",
+              "      <th>...</th>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87579</th>\n",
+              "      <td>Kathmandu</td>\n",
+              "      <td>Football and Cricket are the most popular spor...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87584</th>\n",
+              "      <td>Kathmandu</td>\n",
+              "      <td>The total length of roads in Nepal is recorded...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87589</th>\n",
+              "      <td>Kathmandu</td>\n",
+              "      <td>The main international airport serving Kathman...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87594</th>\n",
+              "      <td>Kathmandu</td>\n",
+              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>18891 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d41f3060-bbe9-4388-8396-83a171d580e8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </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",
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+              "      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-d41f3060-bbe9-4388-8396-83a171d580e8 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-d41f3060-bbe9-4388-8396-83a171d580e8');\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=\"id_06aa90e0-0d65-405b-b8d2-076e75e3bdf4\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_06aa90e0-0d65-405b-b8d2-076e75e3bdf4 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18891,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 442,\n        \"samples\": [\n          \"Dominican_Order\",\n          \"Brigham_Young_University\",\n          \"Matter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18891,\n        \"samples\": [\n          \"More commonly, in cases where there are three or more parties, no one party is likely to gain power alone, and parties work with each other to form coalition governments. This has been an emerging trend in the politics of the Republic of Ireland since the 1980s and is almost always the case in Germany on national and state level, and in most constituencies at the communal level. Furthermore, since the forming of the Republic of Iceland there has never been a government not led by a coalition (usually of the Independence Party and one other (often the Social Democratic Alliance). A similar situation exists in the Republic of Ireland; since 1989, no one party has held power on its own. Since then, numerous coalition governments have been formed. These coalitions have been exclusively led by one of either Fianna F\\u00e1il or Fine Gael. Political change is often easier with a coalition government than in one-party or two-party dominant systems.[dubious \\u2013 discuss] If factions in a two-party system are in fundamental disagreement on policy goals, or even principles, they can be slow to make policy changes, which appears to be the case now in the U.S. with power split between Democrats and Republicans. Still coalition governments struggle, sometimes for years, to change policy and often fail altogether, post World War II France and Italy being prime examples. When one party in a two-party system controls all elective branches, however, policy changes can be both swift and significant. Democrats Woodrow Wilson, Franklin Roosevelt and Lyndon Johnson were beneficiaries of such fortuitous circumstances, as were Republicans as far removed in time as Abraham Lincoln and Ronald Reagan. Barack Obama briefly had such an advantage between 2009 and 2011.\",\n          \"There has been some concern over the potential adverse environmental and ecosystem effects caused by the influx of visitors. Some environmentalists and scientists have made a call for stricter regulations for ships and a tourism quota. The primary response by Antarctic Treaty Parties has been to develop, through their Committee for Environmental Protection and in partnership with IAATO, \\\"site use guidelines\\\" setting landing limits and closed or restricted zones on the more frequently visited sites. Antarctic sightseeing flights (which did not land) operated out of Australia and New Zealand until the fatal crash of Air New Zealand Flight 901 in 1979 on Mount Erebus, which killed all 257 aboard. Qantas resumed commercial overflights to Antarctica from Australia in the mid-1990s.\",\n          \"After World War II, the Guam Organic Act of 1950 established Guam as an unincorporated organized territory of the United States, provided for the structure of the island's civilian government, and granted the people U.S. citizenship. The Governor of Guam was federally appointed until 1968, when the Guam Elective Governor Act provided for the office's popular election.:242 Since Guam is not a U.S. state, U.S. citizens residing on Guam are not allowed to vote for president and their congressional representative is a non-voting member.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "# select only title and context column\n",
+        "df = df[[\"title\", \"context\"]]\n",
+        "# drop rows containing duplicate context passages\n",
+        "df = df.drop_duplicates(subset=[\"context\"])\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "57bbcb57",
+      "metadata": {
+        "id": "57bbcb57"
+      },
+      "source": [
+        "# Initialize Pinecone Index"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e24d904c",
+      "metadata": {
+        "id": "e24d904c"
+      },
+      "source": [
+        "The Pinecone index stores vector representations of our context passages which we can retrieve using another vector (query vector). We first need to initialize our connection to Pinecone to create our vector index. For this, we need a free [API key](\"https://app.pinecone.io/\"), and then we initialize the connection like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "092d1e71",
+      "metadata": {
+        "id": "092d1e71"
+      },
+      "outputs": [],
+      "source": [
+        "from pinecone import Pinecone, ServerlessSpec\n",
+        "\n",
+        "spec = ServerlessSpec(\n",
+        "    cloud=\"aws\", region=\"us-east-1\"\n",
+        ")\n",
+        "\n",
+        "# connect to pinecone environment\n",
+        "pc = Pinecone(\n",
+        "    api_key = PINECONE_API_KEY\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58028e12",
+      "metadata": {
+        "id": "58028e12"
+      },
+      "source": [
+        "Now we create a new index called \"question-answering\" — we can name the index anything we want. We specify the metric type as \"cosine\" and dimension as 384 because the retriever we use to generate context embeddings is optimized for cosine similarity and outputs 384-dimension vectors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b3206184",
+      "metadata": {
+        "id": "b3206184"
+      },
+      "outputs": [],
+      "source": [
+        "index_name = \"question-answering\"\n",
+        "\n",
+        "# check if the extractive-question-answering index exists\n",
+        "if index_name not in pc.list_indexes().names():\n",
+        "    # create the index if it does not exist\n",
+        "    pc.create_index(\n",
+        "        name=index_name,\n",
+        "        dimension=384,\n",
+        "        metric=\"cosine\",\n",
+        "        spec=spec)\n",
+        "# connect to extractive-question-answering index we created\n",
+        "index = pc.Index(index_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6e84a3e5",
+      "metadata": {
+        "id": "6e84a3e5"
+      },
+      "source": [
+        "# Initialize Retriever"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "oZzhGS1Lpj0g",
+      "metadata": {
+        "id": "oZzhGS1Lpj0g"
+      },
+      "source": [
+        "Next, we need to initialize our retriever. The retriever will mainly do two things:\n",
+        "\n",
+        "- Generate embeddings for all context passages (context vectors/embeddings)\n",
+        "- Generate embeddings for our questions (query vector/embedding)\n",
+        "\n",
+        "The retriever will generate embeddings in a way that the questions and context passages containing answers to our questions are nearby in the vector space. We can use cosine similarity to calculate the similarity between the query and context embeddings to find the context passages that contain potential answers to our question.\n",
+        "\n",
+        "We will use a SentenceTransformer model named ``multi-qa-MiniLM-L6-cos-v1`` designed for semantic search and trained on 215M (question, answer) pairs from diverse sources as our retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "31a85bb3",
+      "metadata": {
+        "id": "31a85bb3",
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+          ]
+        },
+        "outputId": "3b3288f6-9cf0-4f71-f878-806b453f6ccc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0it [00:00, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2cbc4b7125444afb85d205a8961a8b6d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+          "data": {
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+            ],
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+            ],
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+        },
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+              "tokenizer_config.json:   0%|          | 0.00/383 [00:00<?, ?B/s]"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt: 0.00B [00:00, ?B/s]"
+            ],
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+            }
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+        {
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+          "data": {
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+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7af1d750947745e1b28bfc8c7e17aa15"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cba05365535b4363901195d9490c47b3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SentenceTransformer(\n",
+              "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel \n",
+              "  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
+              "  (2): Normalize()\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from sentence_transformers import SentenceTransformer\n",
+        "\n",
+        "# set device to GPU if available\n",
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "# load the retriever model from huggingface model hub\n",
+        "retriever = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1', device=device) #use the 'multi-qa-MiniLM-L6-cos-v1' model from HuggingFace to build the retriever\n",
+        "\n",
+        "retriever"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8aaad0a2",
+      "metadata": {
+        "id": "8aaad0a2"
+      },
+      "source": [
+        "# Generate Embeddings and Upsert"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Hgy7AagJtO_p",
+      "metadata": {
+        "id": "Hgy7AagJtO_p"
+      },
+      "source": [
+        "Next, we need to generate embeddings for the context passages. We will do this in batches to help us more quickly generate embeddings and upload them to the Pinecone index. When passing the documents to Pinecone, we need an id (a unique value), context embedding, and metadata for each document representing context passages in the dataset. The metadata is a dictionary containing data relevant to our embeddings, such as the article title, context passage, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a17824ef",
+      "metadata": {
+        "id": "a17824ef",
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118,
+          "referenced_widgets": [
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+          ]
+        },
+        "outputId": "8d582d6c-8c8d-43e9-b9e7-bfe27c6f9f68"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/296 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9e8e6496700f42139b29be60f8c8f967"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'dimension': 384,\n",
+              " 'index_fullness': 0.0,\n",
+              " 'namespaces': {'': {'vector_count': 18891}},\n",
+              " 'total_vector_count': 18891}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "\n",
+        "# we will use batches of 64\n",
+        "batch_size = 64\n",
+        "\n",
+        "for i in tqdm(range(0, len(df), batch_size)):\n",
+        "    # find end of batch\n",
+        "    i_end = min(i+batch_size, len(df))\n",
+        "    # extract batch\n",
+        "    batch = df.iloc[i:i_end]\n",
+        "    # generate embeddings for batch\n",
+        "    emb = retriever.encode(batch[\"context\"].tolist()).tolist()\n",
+        "    # get metadata\n",
+        "    meta = batch.to_dict(orient=\"records\")\n",
+        "    # create unique IDs\n",
+        "    ids = [str(x) for x in range(i, i_end)]\n",
+        "    # add all to upsert list\n",
+        "    to_upsert = list(zip(ids, emb, meta))\n",
+        "    # upsert/insert these records to pinecone\n",
+        "    _ = index.upsert(vectors=to_upsert)\n",
+        "\n",
+        "# check that we have all vectors in index\n",
+        "index.describe_index_stats()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "YFyBYafuJ0y0",
+      "metadata": {
+        "id": "YFyBYafuJ0y0"
+      },
+      "source": [
+        "# Initialize Reader"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "HgdiLCz5ynOk",
+      "metadata": {
+        "id": "HgdiLCz5ynOk"
+      },
+      "source": [
+        "We use the `deepset/electra-base-squad2` model from the HuggingFace model hub as our reader model. We load this model into a \"question-answering\" pipeline from HuggingFace transformers and feed it our questions and context passages individually. The model gives a prediction for each context we pass through the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "hg9XTDkIJzH_",
+      "metadata": {
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+        },
+        "outputId": "47592f17-25aa-4c7d-de8e-08ad5b5d0a97"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/635 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1962a2ae302d40459ee5f54d1cd8d7ab"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "22e5100ccbb8473aa94828aa538e4e1d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/200 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ac4b4a467a3a425aad82d365b4f4ae42"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "09a7a37df7c543ce81dff2f9c48e978d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4c7e6a17239c4c5cac6e795e99289fdd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<transformers.pipelines.question_answering.QuestionAnsweringPipeline at 0x79d5ea07c620>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "model_name = 'deepset/electra-base-squad2'\n",
+        "# load the reader model into a question-answering pipeline\n",
+        "\n",
+        "device_id = 0 if torch.cuda.is_available() else -1\n",
+        "reader = pipeline(tokenizer=model_name, model=model_name, task='question-answering', device=device_id)\n",
+        "\n",
+        "reader"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e14d89d6",
+      "metadata": {
+        "id": "e14d89d6"
+      },
+      "source": [
+        "Now all the components we need are ready. Let's write some helper functions to execute our queries. The `get_context` function retrieves the context embeddings containing answers to our question from the Pinecone index, and the `extract_answer` function extracts the answers from these context passages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "lyYaY3QEQiHZ",
+      "metadata": {
+        "id": "lyYaY3QEQiHZ"
+      },
+      "outputs": [],
+      "source": [
+        "# gets context passages from the pinecone index\n",
+        "def get_context(question, top_k):\n",
+        "    # generate embeddings for the question\n",
+        "    xq = retriever.encode(question).tolist()\n",
+        "    # search pinecone index for context passage with the answer\n",
+        "    xc = index.query(vector=xq, top_k=top_k, include_metadata=True)\n",
+        "    # extract the context passage from pinecone search result\n",
+        "    c = [x['metadata']['context'] for x in xc['matches']]\n",
+        "    return c\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "Dc9VYOiUQA7B",
+      "metadata": {
+        "id": "Dc9VYOiUQA7B"
+      },
+      "outputs": [],
+      "source": [
+        "from pprint import pprint\n",
+        "\n",
+        "# extracts answer from the context passage\n",
+        "def extract_answer(question, context):\n",
+        "    results = []\n",
+        "    for c in context:\n",
+        "        # feed the reader the question and contexts to extract answers\n",
+        "        answer = reader(question=question, context=c)\n",
+        "        # add the context to answer dict for printing both together\n",
+        "        answer[\"context\"] = c\n",
+        "        results.append(answer)\n",
+        "    # sort the result based on the score from reader model\n",
+        "    sorted_result = pprint(sorted(results, key=lambda x: x['score'], reverse=True))\n",
+        "    return sorted_result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5E3a3dkJ5ZQD",
+      "metadata": {
+        "id": "5E3a3dkJ5ZQD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a8ac692e-4992-4576-bdf3-eb9aa5b18e79"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Egypt was producing 691,000 bbl/d of oil and 2,141.05 Tcf of natural gas (in 2013), which makes Egypt as the largest oil producer not member of the Organization of the Petroleum Exporting Countries (OPEC) and the second-largest dry natural gas producer in Africa. In 2013, Egypt was the largest consumer of oil and natural gas in Africa, as more than 20% of total oil consumption and more than 40% of total dry natural gas consumption in Africa. Also, Egypt possesses the largest oil refinery capacity in Africa 726,000 bbl/d (in 2012). Egypt is currently planning to build its first nuclear power plant in El Dabaa city, northern Egypt.']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "question = \"How much oil is Egypt producing in a day?\"\n",
+        "context = get_context(question, top_k = 1)\n",
+        "context"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "heKNVbWQ_LtC",
+      "metadata": {
+        "id": "heKNVbWQ_LtC"
+      },
+      "source": [
+        "As we can see, the retiever is working fine and gets us the context passage that contains the answer to our question. Now let's use the reader to extract the exact answer from the context passage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "DQ4GWdbMSjPl",
+      "metadata": {
+        "id": "DQ4GWdbMSjPl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0f9f68b1-a9cc-44ef-c963-4b284cb634df"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': '691,000 bbl/d',\n",
+            "  'context': 'Egypt was producing 691,000 bbl/d of oil and 2,141.05 Tcf of '\n",
+            "             'natural gas (in 2013), which makes Egypt as the largest oil '\n",
+            "             'producer not member of the Organization of the Petroleum '\n",
+            "             'Exporting Countries (OPEC) and the second-largest dry natural '\n",
+            "             'gas producer in Africa. In 2013, Egypt was the largest consumer '\n",
+            "             'of oil and natural gas in Africa, as more than 20% of total oil '\n",
+            "             'consumption and more than 40% of total dry natural gas '\n",
+            "             'consumption in Africa. Also, Egypt possesses the largest oil '\n",
+            "             'refinery capacity in Africa 726,000 bbl/d (in 2012). Egypt is '\n",
+            "             'currently planning to build its first nuclear power plant in El '\n",
+            "             'Dabaa city, northern Egypt.',\n",
+            "  'end': 33,\n",
+            "  'score': 0.9999852180480957,\n",
+            "  'start': 20}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fMD_ABuDAyhN",
+      "metadata": {
+        "id": "fMD_ABuDAyhN"
+      },
+      "source": [
+        "The reader model predicted with 99% accuracy the correct answer *691,000 bbl/d* as seen from the context passage. Let's run few more queries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "_4NRgV4mGWoj",
+      "metadata": {
+        "id": "_4NRgV4mGWoj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a886a302-2453-4a4b-e234-269d5870c845"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': 'Hurley and Chen',\n",
+            "  'context': 'According to a story that has often been repeated in the media, '\n",
+            "             'Hurley and Chen developed the idea for YouTube during the early '\n",
+            "             'months of 2005, after they had experienced difficulty sharing '\n",
+            "             \"videos that had been shot at a dinner party at Chen's apartment \"\n",
+            "             'in San Francisco. Karim did not attend the party and denied that '\n",
+            "             'it had occurred, but Chen commented that the idea that YouTube '\n",
+            "             'was founded after a dinner party \"was probably very strengthened '\n",
+            "             'by marketing ideas around creating a story that was very '\n",
+            "             'digestible\".',\n",
+            "  'end': 79,\n",
+            "  'score': 0.9999276399612427,\n",
+            "  'start': 64}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"What are the first names of the men that invented youtube?\"\n",
+        "context = get_context(question, top_k=1)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "juXlctWgJgMF",
+      "metadata": {
+        "id": "juXlctWgJgMF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "87bac2f6-87c9-43a7-916b-db1dcdaebde9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': 'his theories of special relativity and general relativity',\n",
+            "  'context': 'Albert Einstein is known for his theories of special relativity '\n",
+            "             'and general relativity. He also made important contributions to '\n",
+            "             'statistical mechanics, especially his mathematical treatment of '\n",
+            "             'Brownian motion, his resolution of the paradox of specific '\n",
+            "             'heats, and his connection of fluctuations and dissipation. '\n",
+            "             'Despite his reservations about its interpretation, Einstein also '\n",
+            "             'made contributions to quantum mechanics and, indirectly, quantum '\n",
+            "             'field theory, primarily through his theoretical studies of the '\n",
+            "             'photon.',\n",
+            "  'end': 86,\n",
+            "  'score': 0.9500371217727661,\n",
+            "  'start': 29}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"What is Albert Eistein famous for?\"\n",
+        "context = get_context(question, top_k=1)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "OhCgeny_BVno",
+      "metadata": {
+        "id": "OhCgeny_BVno"
+      },
+      "source": [
+        "Let's run another question. This time for top 3 context passages from the retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "iXACn71xmett",
+      "metadata": {
+        "id": "iXACn71xmett",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3b54e179-c3d1-47fe-9f2c-e657c8bbcdf1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': 'Armstrong',\n",
+            "  'context': 'The trip to the Moon took just over three days. After achieving '\n",
+            "             'orbit, Armstrong and Aldrin transferred into the Lunar Module, '\n",
+            "             'named Eagle, and after a landing gear inspection by Collins '\n",
+            "             'remaining in the Command/Service Module Columbia, began their '\n",
+            "             'descent. After overcoming several computer overload alarms '\n",
+            "             'caused by an antenna switch left in the wrong position, and a '\n",
+            "             'slight downrange error, Armstrong took over manual flight '\n",
+            "             'control at about 180 meters (590 ft), and guided the Lunar '\n",
+            "             'Module to a safe landing spot at 20:18:04 UTC, July 20, 1969 '\n",
+            "             '(3:17:04 pm CDT). The first humans on the Moon would wait '\n",
+            "             'another six hours before they ventured out of their craft. At '\n",
+            "             '02:56 UTC, July 21 (9:56 pm CDT July 20), Armstrong became the '\n",
+            "             'first human to set foot on the Moon.',\n",
+            "  'end': 80,\n",
+            "  'score': 0.9998037815093994,\n",
+            "  'start': 71},\n",
+            " {'answer': 'Aldrin',\n",
+            "  'context': 'The first step was witnessed by at least one-fifth of the '\n",
+            "             'population of Earth, or about 723 million people. His first '\n",
+            "             \"words when he stepped off the LM's landing footpad were, \"\n",
+            "             '\"That\\'s one small step for [a] man, one giant leap for '\n",
+            "             'mankind.\" Aldrin joined him on the surface almost 20 minutes '\n",
+            "             'later. Altogether, they spent just under two and one-quarter '\n",
+            "             'hours outside their craft. The next day, they performed the '\n",
+            "             'first launch from another celestial body, and rendezvoused back '\n",
+            "             'with Columbia.',\n",
+            "  'end': 246,\n",
+            "  'score': 0.695867121219635,\n",
+            "  'start': 240},\n",
+            " {'answer': 'Frank Borman',\n",
+            "  'context': 'On December 21, 1968, Frank Borman, James Lovell, and William '\n",
+            "             'Anders became the first humans to ride the Saturn V rocket into '\n",
+            "             'space on Apollo 8. They also became the first to leave low-Earth '\n",
+            "             'orbit and go to another celestial body, and entered lunar orbit '\n",
+            "             'on December 24. They made ten orbits in twenty hours, and '\n",
+            "             'transmitted one of the most watched TV broadcasts in history, '\n",
+            "             'with their Christmas Eve program from lunar orbit, that '\n",
+            "             'concluded with a reading from the biblical Book of Genesis. Two '\n",
+            "             'and a half hours after the broadcast, they fired their engine to '\n",
+            "             'perform the first trans-Earth injection to leave lunar orbit and '\n",
+            "             'return to the Earth. Apollo 8 safely landed in the Pacific ocean '\n",
+            "             \"on December 27, in NASA's first dawn splashdown and recovery.\",\n",
+            "  'end': 34,\n",
+            "  'score': 0.49246710538864136,\n",
+            "  'start': 22}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"Who was the first person to step foot on the moon?\"\n",
+        "context = get_context(question, top_k=3)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c9oCWHy0tPpV",
+      "metadata": {
+        "id": "c9oCWHy0tPpV"
+      },
+      "source": [
+        "The result looks pretty good."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d83f9f55-b099-4280-a12e-1d0192f4f5aa",
+      "metadata": {
+        "id": "d83f9f55-b099-4280-a12e-1d0192f4f5aa"
+      },
+      "outputs": [],
+      "source": [
+        "pc.delete_index(index_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "27fecadc-6976-43b2-90e1-788a8633ecc7",
+      "metadata": {
+        "id": "27fecadc-6976-43b2-90e1-788a8633ecc7"
+      },
+      "source": [
+        "### Add a few more questions. What did you observe?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "question = \"Who was the first president of Uganda?\"\n",
+        "context = get_context(question, top_k=3)\n",
+        "extract_answer(question, context)"
+      ],
+      "metadata": {
+        "id": "1ps6WF5BU9Ud",
+        "outputId": "b4df63db-2e6b-4b41-b5c7-976215d4d310",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "1ps6WF5BU9Ud",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': 'Kumba Ialá',\n",
+            "  'context': 'The country was controlled by a revolutionary council until '\n",
+            "             '1984. The first multi-party elections were held in 1994. An army '\n",
+            "             'uprising in May 1998 led to the Guinea-Bissau Civil War and the '\n",
+            "             \"president's ousting in June 1999. Elections were held again in \"\n",
+            "             '2000, and Kumba Ialá was elected president.',\n",
+            "  'end': 272,\n",
+            "  'score': 5.36900079950442e-10,\n",
+            "  'start': 262},\n",
+            " {'answer': 'Taylor',\n",
+            "  'context': 'The subsequent 2005 elections were internationally regarded as '\n",
+            "             'the most free and fair in Liberian history. Ellen Johnson '\n",
+            "             'Sirleaf, a Harvard-trained economist and former Minister of '\n",
+            "             'Finance, was elected as the first female president in Africa. '\n",
+            "             'Upon her inauguration, Sirleaf requested the extradition of '\n",
+            "             'Taylor from Nigeria and transferred him to the SCSL for trial in '\n",
+            "             'The Hague. In 2006, the government established a Truth and '\n",
+            "             'Reconciliation Commission to address the causes and crimes of '\n",
+            "             'the civil war.',\n",
+            "  'end': 309,\n",
+            "  'score': 3.2985595591755734e-12,\n",
+            "  'start': 303},\n",
+            " {'answer': \"Congo's first elected president (1992–1997\",\n",
+            "  'context': \"Pascal Lissouba, who became Congo's first elected president \"\n",
+            "             '(1992–1997) during the period of multi-party democracy, '\n",
+            "             'attempted to implement economic reforms with IMF backing to '\n",
+            "             'liberalise the economy. In June 1996 the IMF approved a '\n",
+            "             'three-year SDR69.5m (US$100m) enhanced structural adjustment '\n",
+            "             'facility (ESAF) and was on the verge of announcing a renewed '\n",
+            "             'annual agreement when civil war broke out in Congo in mid-1997.',\n",
+            "  'end': 70,\n",
+            "  'score': 1.6490456190235842e-13,\n",
+            "  'start': 28}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "question = \"What country records the most deaths due to gun violence yearly?\"\n",
+        "context = get_context(question, top_k=3)\n",
+        "extract_answer(question, context)"
+      ],
+      "metadata": {
+        "id": "zP3l9MLIVF21",
+        "outputId": "bfcf3c8a-bb05-424a-c6bd-0311026d1ebb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "zP3l9MLIVF21",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': '857',\n",
+            "  'context': 'The number of shootings in the city has declined significantly '\n",
+            "             'in the last 10 years. Shooting incidents peaked in 2006 when '\n",
+            "             '1,857 shootings were recorded. That number has dropped 44 '\n",
+            "             'percent to 1,047 shootings in 2014. Similarly, major crimes in '\n",
+            "             'the city has decreased gradually in the last ten years since its '\n",
+            "             'peak in 2006 when 85,498 major crimes were reported. In the past '\n",
+            "             'three years, the number of reported major crimes fell 11 percent '\n",
+            "             'to a total of 68,815. Violent crimes, which include homicide, '\n",
+            "             'rape, aggravated assault, and robbery, decreased 14 percent in '\n",
+            "             'the past three years with a reported 15,771 occurrences in 2014. '\n",
+            "             'Based on the rate of violent crimes per 1,000 residents in '\n",
+            "             'American cities with 25,000 people or more, Philadelphia was '\n",
+            "             'ranked as the 54th most dangerous city in 2015.',\n",
+            "  'end': 129,\n",
+            "  'score': 0.00010442490747664124,\n",
+            "  'start': 126},\n",
+            " {'answer': 'India',\n",
+            "  'context': 'In 2007, the country with the highest estimated incidence rate '\n",
+            "             'of TB was Swaziland, with 1,200 cases per 100,000 people. India '\n",
+            "             'had the largest total incidence, with an estimated 2.0 million '\n",
+            "             'new cases. In developed countries, tuberculosis is less common '\n",
+            "             'and is found mainly in urban areas. Rates per 100,000 people in '\n",
+            "             'different areas of the world were: globally 178, Africa 332, the '\n",
+            "             'Americas 36, Eastern Mediterranean 173, Europe 63, Southeast '\n",
+            "             'Asia 278, and Western Pacific 139 in 2010. In Canada and '\n",
+            "             'Australia, tuberculosis is many times more common among the '\n",
+            "             'aboriginal peoples, especially in remote areas. In the United '\n",
+            "             'States Native Americans have a fivefold greater mortality from '\n",
+            "             'TB, and racial and ethnic minorities accounted for 84% of all '\n",
+            "             'reported TB cases.',\n",
+            "  'end': 126,\n",
+            "  'score': 9.818465756140232e-14,\n",
+            "  'start': 121},\n",
+            " {'answer': '2012',\n",
+            "  'context': 'The top three single agent/disease killers are HIV/AIDS, TB and '\n",
+            "             'malaria. While the number of deaths due to nearly every disease '\n",
+            "             'have decreased, deaths due to HIV/AIDS have increased fourfold. '\n",
+            "             'Childhood diseases include pertussis, poliomyelitis, diphtheria, '\n",
+            "             'measles and tetanus. Children also make up a large percentage of '\n",
+            "             'lower respiratory and diarrheal deaths. In 2012, approximately '\n",
+            "             '3.1 million people have died due to lower respiratory '\n",
+            "             'infections, making it the number 4 leading cause of death in the '\n",
+            "             'world.',\n",
+            "  'end': 369,\n",
+            "  'score': 9.12405188294857e-14,\n",
+            "  'start': 365}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "question = \"Who has won the most English premier league trophies since inception?\"\n",
+        "context = get_context(question, top_k=3)\n",
+        "extract_answer(question, context)"
+      ],
+      "metadata": {
+        "id": "avUC2uo_VG-4",
+        "outputId": "a54ee5bf-d5d7-423e-dded-0b7eb434f239",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "avUC2uo_VG-4",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': 'Arsenal, Chelsea, Crystal Palace, Tottenham Hotspur, and West Ham '\n",
+            "            'United',\n",
+            "  'context': \"London's most popular sport is football and it has fourteen \"\n",
+            "             'League football clubs, including five in the Premier League: '\n",
+            "             'Arsenal, Chelsea, Crystal Palace, Tottenham Hotspur, and West '\n",
+            "             'Ham United. Among other professional teams based in London '\n",
+            "             'include Fulham, Queens Park Rangers, Millwall and Charlton '\n",
+            "             'Athletic. In May 2012, Chelsea became the first London club to '\n",
+            "             'win the UEFA Champions League. Aside from Arsenal, Chelsea and '\n",
+            "             'Tottenham, none of the other London clubs have ever won the '\n",
+            "             'national league title.',\n",
+            "  'end': 193,\n",
+            "  'score': 0.6641287803649902,\n",
+            "  'start': 121},\n",
+            " {'answer': 'Liverpool',\n",
+            "  'context': 'Seven clubs have won the FA Cup as part of a League and Cup '\n",
+            "             'double, namely Preston North End (1889), Aston Villa (1897), '\n",
+            "             'Tottenham Hotspur (1961), Arsenal (1971, 1998, 2002), Liverpool '\n",
+            "             '(1986), Manchester United (1994, 1996, 1999) and Chelsea (2010). '\n",
+            "             'In 1993, Arsenal became the first side to win both the FA Cup '\n",
+            "             'and the League Cup in the same season when they beat Sheffield '\n",
+            "             'Wednesday 2–1 in both finals. Liverpool (in 2001) and Chelsea '\n",
+            "             '(in 2007) have since repeated this feat. In 2012, Chelsea '\n",
+            "             'accomplished a different cup double consisting of the FA Cup and '\n",
+            "             'the 2012 Champions League. In 1998–99, Manchester United added '\n",
+            "             'the 1999 Champions League title to their league and cup double '\n",
+            "             'to complete a unique Treble. Two years later, in 2000–01, '\n",
+            "             'Liverpool won the FA Cup, League Cup and UEFA Cup to complete a '\n",
+            "             'cup treble. An English Treble has never been achieved.',\n",
+            "  'end': 414,\n",
+            "  'score': 4.2066394598805346e-06,\n",
+            "  'start': 405},\n",
+            " {'answer': '12',\n",
+            "  'context': \"Arsenal's tally of 13 League Championships is the third highest \"\n",
+            "             'in English football, after Manchester United (20) and Liverpool '\n",
+            "             '(18), and they were the first club to reach 8 League '\n",
+            "             'Championships. They hold the highest number of FA Cup trophies, '\n",
+            "             '12. The club is one of only six clubs to have won the FA Cup '\n",
+            "             'twice in succession, in 2002 and 2003, and 2014 and 2015. '\n",
+            "             'Arsenal have achieved three League and FA Cup \"Doubles\" (in '\n",
+            "             '1971, 1998 and 2002), a feat only previously achieved by '\n",
+            "             'Manchester United (in 1994, 1996 and 1999). They were the first '\n",
+            "             'side in English football to complete the FA Cup and League Cup '\n",
+            "             'double, in 1993. Arsenal were also the first London club to '\n",
+            "             'reach the final of the UEFA Champions League, in 2006, losing '\n",
+            "             'the final 2–1 to Barcelona.',\n",
+            "  'end': 247,\n",
+            "  'score': 1.2422140116541414e-06,\n",
+            "  'start': 245}]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Retrieval quality is not good for my questions here — likely because the SQuAD dataset my index was built from just doesn’t contain many passages relevant to these specific questions (Uganda, gun violence stats), so it’s returning “closest available” rather than “actually relevant.”"
+      ],
+      "metadata": {
+        "id": "iq8b9QKoXpV1"
+      },
+      "id": "iq8b9QKoXpV1"
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "machine_shape": "hm",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "environment": {
+      "name": "tf2-gpu.2-3.m65",
+      "type": "gcloud",
+      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m65"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.8"
+    },
+    "papermill": {
+      "default_parameters": {},
+      "duration": 333.240754,
+      "end_time": "2021-04-15T21:12:11.363566",
+      "environment_variables": {},
+      "exception": null,
+      "input_path": "/notebooks/question_answering/question_answering.ipynb",
+      "output_path": "/notebooks/tmp/question_answering/question_answering.ipynb",
+      "parameters": {},
+      "start_time": "2021-04-15T21:06:38.122812",
+      "version": "2.3.3"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "5fe10bf018ef3e697f9035d60bf60847932a12bface18908407fd371fe880db9"
+      }
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "version_major": 2,
+        "version_minor": 0,
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+              "_view_module": "@jupyter-widgets/controls",
+              "_view_module_version": "1.5.0",
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+              "children": [
+                "IPY_MODEL_b56a83e7a20a42308d474e4475026bc5",
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