notebooks/prompt_engineering_querying_facts.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkyQQ1FSt9zg"
      },
      "source": [
        "# Study: Querying facts with GPT-3\n",
        "\n",
        "This study aims to investigate the utilization of GPT-3 for querying the extracted facts from a database. The specific objectives of this investigation include:\n",
        "\n",
        "  - Implementing auxiliary mechanisms to store the extracted facts, utilizing a simple Pandas Dataframe for this purpose.\n",
        "\n",
        "  - Developing prompts that facilitate querying the facts from the database. Remarkably, we will explore how GPT-3 can be leveraged to enhance this querying process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9tOlkdTt9zl"
      },
      "source": [
        "### Setup"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install openai library\n",
        "!pip install openai --quiet"
      ],
      "metadata": {
        "id": "kp8n6GlUw3WQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef773755-5a03-43b8-ca84-4cdf3295d4f9"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tCx0GZA9t9zm"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "import getpass\n",
        "import openai\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "g3_EDLi_t9zq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0397473-15f6-4e1d-94de-6b25bd816e38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter OpenAI API key: ··········\n"
          ]
        }
      ],
      "source": [
        "# get and store openai API key\n",
        "openai.api_key = getpass.getpass('Enter OpenAI API key: ')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hadQGeVt9zr"
      },
      "source": [
        "### Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zLHxhVFRt9zr"
      },
      "outputs": [],
      "source": [
        "def gpt3_complete(\n",
        "    prompt, \n",
        "    engine='text-davinci-003', \n",
        "    temperature=0.1, \n",
        "    max_tokens=200,\n",
        "    top_p=1.0, \n",
        "    frequency_penalty=0, \n",
        "    presence_penalty=0, \n",
        "    stop=None,\n",
        "    echo=False\n",
        "):\n",
        "    \"\"\"\n",
        "    Generates a completion based on the given prompt using the OpenAI GPT-3 model.\n",
        "\n",
        "    Parameters:\n",
        "        prompt (str): The text prompt to generate a completion for.\n",
        "        engine (str): The engine to use for completion. \n",
        "        temperature (float): The temperature parameter controlling the randomness of the generated text. \n",
        "        max_tokens (int): The maximum number of tokens in the generated completion.\n",
        "        top_p (float): The cumulative probability threshold for the top-p sampling algorithm. It controls the diversity of the generated text. \n",
        "        frequency_penalty (float): The frequency penalty to apply during completion. \n",
        "        presence_penalty (float): The presence penalty to apply during completion. \n",
        "        stop (str or list): The stop sequence(s) to use for stopping the generation.\n",
        "        echo (bool): Whether to include the prompt in the generated completion. Default is False.\n",
        "\n",
        "    Returns:\n",
        "        completion (str): The generated completion based on the prompt.\n",
        "    \"\"\"\n",
        "    response = openai.Completion.create(\n",
        "        engine=engine,\n",
        "        prompt=prompt,\n",
        "        temperature=temperature,\n",
        "        max_tokens=max_tokens,\n",
        "        top_p=top_p,\n",
        "        frequency_penalty=frequency_penalty,\n",
        "        presence_penalty=presence_penalty,\n",
        "        stop=stop,\n",
        "        echo=echo\n",
        "    )\n",
        "    \n",
        "    completion = response['choices'][0]['text']\n",
        "    return completion"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_to_examples(\n",
        "    examples, \n",
        "    prompt_func, \n",
        "    temperature=0.1,\n",
        "    frequency_penalty=0,\n",
        "    presence_penalty=0\n",
        "):\n",
        "    \"\"\"\n",
        "    Applies the GPT-3 completion to a list of input examples.\n",
        "\n",
        "    Parameters:\n",
        "        examples (list): A list of input examples to generate completions for.\n",
        "        prompt_func (function): A function that takes an example as input and returns a prompt for GPT-3.\n",
        "        temperature (float): The temperature parameter controlling the randomness of the generated text.\n",
        "        frequency_penalty (float): The frequency penalty to apply during completion. \n",
        "        presence_penalty (float): The presence penalty to apply during completion. \n",
        "\n",
        "    Returns:\n",
        "        results (list): A list of generated completions for each input example.\n",
        "    \"\"\"\n",
        "    results = []\n",
        "    for example in examples:\n",
        "        print(f\">>> INPUT: {example}\")\n",
        "        result = gpt3_complete(prompt_func(example), temperature=temperature, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty)\n",
        "        print(f\">>> OUTPUT:\\n{result}\")\n",
        "        results.append(result)\n",
        "        print(\"========================================\\n\\n\")\n",
        "    return results"
      ],
      "metadata": {
        "id": "JWd-3X1tlkMo"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UH6-n80at9zt"
      },
      "outputs": [],
      "source": [
        "def extract_lines_from_result(result):\n",
        "    \"\"\"\n",
        "    Extracts the lines from the result string.\n",
        "\n",
        "    Parameters:\n",
        "        result (str): The result string to extract lines from.\n",
        "\n",
        "    Returns:\n",
        "        lines (list): A list of extracted lines from the result.\n",
        "    \"\"\"\n",
        "    lines = [line.strip(' -*') for line in result.split('\\n') if len(line) > 0]\n",
        "    return lines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "RKhe2Hb7t9zu"
      },
      "outputs": [],
      "source": [
        "def string_to_tuples(s):\n",
        "    \"\"\"\"\n",
        "    Converts a string that looks like a tuple to an actual Python tuple.\n",
        "\n",
        "    Parameters:\n",
        "        s (str): The string representation of the tuple.\n",
        "\n",
        "    Returns:\n",
        "        tuple_list (list): A list of tuples converted from the string.\n",
        "    \"\"\"\n",
        "    return [eval(s.strip()) for s in extract_lines_from_result(s)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "67gFcZt2t9zv"
      },
      "outputs": [],
      "source": [
        "def extract_terms_from_all_results(results):\n",
        "    \"\"\"\n",
        "    Extracts the terms from the result string.\n",
        "\n",
        "    Parameters:\n",
        "        results (list): A list of result strings.\n",
        "\n",
        "    Returns:\n",
        "        terms (list): A list of lists, where each inner list contains the extracted terms from each result string.\n",
        "    \"\"\"\n",
        "    terms = []\n",
        "    for result in results:\n",
        "        terms.append(extract_lines_from_result(result))\n",
        "    return terms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tg9Ra4JYt9zw"
      },
      "source": [
        "### Example Data\n",
        "\n",
        "Some standard data to exercise our solution below. That shall include information input to the system, as well as related *queries*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UTA1k2wSt9zw"
      },
      "outputs": [],
      "source": [
        "example_information_to_save = [\"Flu shot cost = $80\", \n",
        "                               \"things my boss likes: cricket, science and vegetarian food\",\n",
        "                               \"my wife wants a vegetarian food book\",\n",
        "                               \"sales guy email = jp@example.com\",\n",
        "                               \"vanessa's email vanessa@outlook.com, rember to send the ppts she asked\",\n",
        "                               \"+55 11 27670-0987 -> pedro whatsapp\",\n",
        "                               \"need to buy milk, eggs and bread\", \n",
        "                               \"need to sell old video game, chair\",\n",
        "                               \"december receipts for gym: yoga, ballet, ??\",\n",
        "                               \"book with KWG the hardware setup\",\n",
        "                               \"dog day with the foreign visitors\",\n",
        "                               \"event support: we failed :-(\",\n",
        "                               \"floor layout: we forgot various details!!\",\n",
        "                               \"first aid kit in the reception\",\n",
        "                               \"ask the pediatrician about when to start brushing teeth\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-S1VenRlt9zx"
      },
      "outputs": [],
      "source": [
        "example_queries = [\"shopping list\", \n",
        "                   \"what does my boss like?\", \n",
        "                   \"emails\",\n",
        "                   \"questions for the pediatrician\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YayvvWKVt9z7"
      },
      "outputs": [],
      "source": [
        "# following prompt obtained from fact extraction study\n",
        "def extraction_prompt(x, categories=[\"Family\", \"Work\", \"Friends\", \"Shopping\", \n",
        "                                       \"Health\", \"Finance\", \"Travel\", \"Home\", \n",
        "                                       \"Pets\", \"Hobbies\", \"Other\"]):\n",
        "\n",
        "    prompt =\\\n",
        "f\"\"\"\n",
        "You are tasked with extracting pieces of personal information from various inputs, such as phone numbers, email addresses, names, trivia, reminders, etc. Your goal is to extract and categorize these pieces of information into structured tuples.\n",
        "Extract the information as tuples with the following format: (Category, Type, People, Key, Value). \n",
        "Assume everything mentioned refers to the same thing. \n",
        "The extracted information should be categorized according to the allowed categories and types specified below.\n",
        "\n",
        "Constraints:\n",
        "  - Allowed Categories: {', '.join(categories)}\n",
        "  - Allowed Types: \"List\", \"Email\", \"Phone\", \"Address\", \"Document\", \"Pendency\", \"Price\", \"Reminder\", \"Note\", \"Doubt\", \"Wish\", \"Other\"\n",
        "  - People contain the name or description of the people or organizations concerned, or is empty if no person or organization is mentioned.\n",
        "  \n",
        "Example input: \"Mom's phone number is 555-555-5555\"\n",
        "Example output: (\"Family\", \"Phone\", \"mom\", \"mom's number\", \"555-555-5555\")\n",
        "\n",
        "Example input: \"email of the building administration = adm@example.com\"\n",
        "Example output: (\"Work\", \"Email\", \"building administration\", \"email\", \"adm@example.com\")\n",
        "\n",
        "Example input: \"Need to do: lab work, ultrasound, buy aspirin\"\n",
        "Example output: \n",
        "(\"Health\", \"List\", \"Self\", \"to do\", \"lab work\")\n",
        "(\"Health\", \"List\", \"Self\", \"to do\", \"ultrasound\")\n",
        "(\"Shopping\", \"List\", \"Self\", \"aspirin\", \"buy\")\t\n",
        "\n",
        "Example input: event support: we failed :-(\n",
        "Example output: \n",
        "(\"Other\", \"Note\", \"event support\", \"failed\", \"we failed :-(\")\n",
        "\n",
        "Example input: first aid kit in the reception\n",
        "Example output:\n",
        "(\"Home\", \"Note\", \"\", \"first aid kit\", \"reception\")\n",
        "\n",
        "Example input: december receipts for gym: yoga, ballet, ??\n",
        "Example output:\n",
        "(\"Finance\", \"Document\", \"gym\", \"december receipts\", \"yoga\")\n",
        "(\"Finance\", \"Document\", \"gym\", \"december receipts\", \"ballet\")\n",
        "(\"Finance\", \"Document\", \"gym\", \"december receipts\", \"??\")\n",
        "\n",
        "Input: {x}\n",
        "Output: \n",
        "\"\"\"\n",
        "    return prompt "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugeJlyuPt90B"
      },
      "source": [
        "### Populating Database\n",
        "\n",
        "The Pandas dataframe will serve as the primary data structure akin to a database. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWIDtDx-t90e",
        "outputId": "94e203e2-61c6-49ad-d016-3d107bb83c6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Health', 'Price', '', 'flu shot', '$80'),\n",
              " ('Work', 'List', 'boss', 'likes', 'cricket'),\n",
              " ('Work', 'List', 'boss', 'likes', 'science'),\n",
              " ('Work', 'List', 'boss', 'likes', 'vegetarian food'),\n",
              " ('Shopping', 'Wish', 'wife', 'vegetarian food book', 'my wife wants'),\n",
              " ('Work', 'Email', 'sales guy', 'email', 'jp@example.com'),\n",
              " ('Work', 'Email', 'vanessa', \"vanessa's email\", 'vanessa@outlook.com'),\n",
              " ('Work', 'Reminder', '', 'send ppts', 'rember to send the ppts she asked'),\n",
              " ('Friends', 'Phone', 'Pedro', 'WhatsApp', '+55 11 27670-0987'),\n",
              " ('Shopping', 'List', 'Self', 'milk', 'buy'),\n",
              " ('Shopping', 'List', 'Self', 'eggs', 'buy'),\n",
              " ('Shopping', 'List', 'Self', 'bread', 'buy'),\n",
              " ('Shopping', 'List', 'Self', 'sell', 'old video game'),\n",
              " ('Shopping', 'List', 'Self', 'sell', 'chair'),\n",
              " ('Finance', 'Document', 'gym', 'december receipts', 'yoga'),\n",
              " ('Finance', 'Document', 'gym', 'december receipts', 'ballet'),\n",
              " ('Finance', 'Document', 'gym', 'december receipts', '??'),\n",
              " ('Other', 'Document', 'KWG', 'hardware setup', 'book'),\n",
              " ('Pets', 'Reminder', 'foreign visitors', 'dog day', ''),\n",
              " ('Other', 'Note', 'event support', 'failed', 'we failed :-('),\n",
              " ('Home', 'Note', '', 'floor layout', 'we forgot various details!!'),\n",
              " ('Home', 'Note', '', 'first aid kit', 'reception'),\n",
              " ('Health', 'Doubt', 'pediatrician', 'brushing teeth', 'when to start')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "input_tuples = [] # list to save the tuples\n",
        "for input in example_information_to_save:\n",
        "    for tuples in string_to_tuples(gpt3_complete(extraction_prompt(input))):\n",
        "        input_tuples.append(tuples)\n",
        "        \n",
        "input_tuples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "0fpdNuk9t90g",
        "outputId": "73b36811-ea69-4b00-e923-0b009ad43c36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Category      Type            People                   Key  \\\n",
              "0     Health     Price                                flu shot   \n",
              "1       Work      List              boss                 likes   \n",
              "2       Work      List              boss                 likes   \n",
              "3       Work      List              boss                 likes   \n",
              "4   Shopping      Wish              wife  vegetarian food book   \n",
              "5       Work     Email         sales guy                 email   \n",
              "6       Work     Email           vanessa       vanessa's email   \n",
              "7       Work  Reminder                               send ppts   \n",
              "8    Friends     Phone             Pedro              WhatsApp   \n",
              "9   Shopping      List              Self                  milk   \n",
              "10  Shopping      List              Self                  eggs   \n",
              "11  Shopping      List              Self                 bread   \n",
              "12  Shopping      List              Self                  sell   \n",
              "13  Shopping      List              Self                  sell   \n",
              "14   Finance  Document               gym     december receipts   \n",
              "15   Finance  Document               gym     december receipts   \n",
              "16   Finance  Document               gym     december receipts   \n",
              "17     Other  Document               KWG        hardware setup   \n",
              "18      Pets  Reminder  foreign visitors               dog day   \n",
              "19     Other      Note     event support                failed   \n",
              "20      Home      Note                            floor layout   \n",
              "21      Home      Note                           first aid kit   \n",
              "22    Health     Doubt      pediatrician        brushing teeth   \n",
              "\n",
              "                                Value  \n",
              "0                                 $80  \n",
              "1                             cricket  \n",
              "2                             science  \n",
              "3                     vegetarian food  \n",
              "4                       my wife wants  \n",
              "5                      jp@example.com  \n",
              "6                 vanessa@outlook.com  \n",
              "7   rember to send the ppts she asked  \n",
              "8                   +55 11 27670-0987  \n",
              "9                                 buy  \n",
              "10                                buy  \n",
              "11                                buy  \n",
              "12                     old video game  \n",
              "13                              chair  \n",
              "14                               yoga  \n",
              "15                             ballet  \n",
              "16                                 ??  \n",
              "17                               book  \n",
              "18                                     \n",
              "19                      we failed :-(  \n",
              "20        we forgot various details!!  \n",
              "21                          reception  \n",
              "22                      when to start  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3733c4e4-3203-4999-8bf0-2c9cb3e45047\">\n",
              "    <div 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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Type</th>\n",
              "      <th>People</th>\n",
              "      <th>Key</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Health</td>\n",
              "      <td>Price</td>\n",
              "      <td></td>\n",
              "      <td>flu shot</td>\n",
              "      <td>$80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>cricket</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>science</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>vegetarian food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>Wish</td>\n",
              "      <td>wife</td>\n",
              "      <td>vegetarian food book</td>\n",
              "      <td>my wife wants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Work</td>\n",
              "      <td>Email</td>\n",
              "      <td>sales guy</td>\n",
              "      <td>email</td>\n",
              "      <td>jp@example.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Work</td>\n",
              "      <td>Email</td>\n",
              "      <td>vanessa</td>\n",
              "      <td>vanessa's email</td>\n",
              "      <td>vanessa@outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Work</td>\n",
              "      <td>Reminder</td>\n",
              "      <td></td>\n",
              "      <td>send ppts</td>\n",
              "      <td>rember to send the ppts she asked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Friends</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Pedro</td>\n",
              "      <td>WhatsApp</td>\n",
              "      <td>+55 11 27670-0987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>milk</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>eggs</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>bread</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>sell</td>\n",
              "      <td>old video game</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>sell</td>\n",
              "      <td>chair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>yoga</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>ballet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>??</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Other</td>\n",
              "      <td>Document</td>\n",
              "      <td>KWG</td>\n",
              "      <td>hardware setup</td>\n",
              "      <td>book</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Pets</td>\n",
              "      <td>Reminder</td>\n",
              "      <td>foreign visitors</td>\n",
              "      <td>dog day</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Other</td>\n",
              "      <td>Note</td>\n",
              "      <td>event support</td>\n",
              "      <td>failed</td>\n",
              "      <td>we failed :-(</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Home</td>\n",
              "      <td>Note</td>\n",
              "      <td></td>\n",
              "      <td>floor layout</td>\n",
              "      <td>we forgot various details!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Home</td>\n",
              "      <td>Note</td>\n",
              "      <td></td>\n",
              "      <td>first aid kit</td>\n",
              "      <td>reception</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Health</td>\n",
              "      <td>Doubt</td>\n",
              "      <td>pediatrician</td>\n",
              "      <td>brushing teeth</td>\n",
              "      <td>when to start</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3733c4e4-3203-4999-8bf0-2c9cb3e45047')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-3733c4e4-3203-4999-8bf0-2c9cb3e45047 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-3733c4e4-3203-4999-8bf0-2c9cb3e45047');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "database = pd.DataFrame(input_tuples, columns=[\"Category\", \"Type\", \"People\", \"Key\", \"Value\"])\n",
        "database"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prompt Engineering: Querying Information"
      ],
      "metadata": {
        "id": "fh8yV2psjphA"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5X6haMGt90h"
      },
      "source": [
        "#### Prompt 1\n",
        "\n",
        "The current approach proposed is a simplistic solution that requires examining each row of the Pandas dataframe. While it may achieve the desired outcome, it appears to be computationally expensive and inefficient."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "jKbleOvit90h"
      },
      "outputs": [],
      "source": [
        "def querying_prompt_1(query, example):\n",
        "\n",
        "    prompt = \\\n",
        "f\"\"\"\n",
        "Determine whether the query \"{query}\" is related to the tuple \"{example}\".\n",
        "Answer (yes/no):\n",
        "\"\"\"\n",
        "\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CXo1gJjt90i",
        "outputId": "669a454b-f387-4f4f-a7c0-e3de04ffaf76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INPUT QUERY: shopping list\n",
            "\n",
            "INPUT ROW: ('Health', 'Price', '', 'flu shot', '$80')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'cricket')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'science')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'vegetarian food')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'Wish', 'wife', 'vegetarian food book', 'my wife wants')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Work', 'Email', 'sales guy', 'email', 'jp@example.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'vanessa', \"vanessa's email\", 'vanessa@outlook.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Reminder', '', 'send ppts', 'rember to send the ppts she asked')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Friends', 'Phone', 'Pedro', 'WhatsApp', '+55 11 27670-0987')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'milk', 'buy')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'eggs', 'buy')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'bread', 'buy')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'old video game')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'chair')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'yoga')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'ballet')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', '??')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Document', 'KWG', 'hardware setup', 'book')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Pets', 'Reminder', 'foreign visitors', 'dog day', '')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Note', 'event support', 'failed', 'we failed :-(')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'floor layout', 'we forgot various details!!')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'first aid kit', 'reception')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Health', 'Doubt', 'pediatrician', 'brushing teeth', 'when to start')\n",
            "OUTPUT: No\n",
            "====================================================================\n",
            "INPUT QUERY: what does my boss like?\n",
            "\n",
            "INPUT ROW: ('Health', 'Price', '', 'flu shot', '$80')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'cricket')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'science')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'vegetarian food')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'Wish', 'wife', 'vegetarian food book', 'my wife wants')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'sales guy', 'email', 'jp@example.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'vanessa', \"vanessa's email\", 'vanessa@outlook.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Reminder', '', 'send ppts', 'rember to send the ppts she asked')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Friends', 'Phone', 'Pedro', 'WhatsApp', '+55 11 27670-0987')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'milk', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'eggs', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'bread', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'old video game')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'chair')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'yoga')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'ballet')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', '??')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Document', 'KWG', 'hardware setup', 'book')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Pets', 'Reminder', 'foreign visitors', 'dog day', '')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Note', 'event support', 'failed', 'we failed :-(')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'floor layout', 'we forgot various details!!')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'first aid kit', 'reception')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Health', 'Doubt', 'pediatrician', 'brushing teeth', 'when to start')\n",
            "OUTPUT: No\n",
            "====================================================================\n",
            "INPUT QUERY: emails\n",
            "\n",
            "INPUT ROW: ('Health', 'Price', '', 'flu shot', '$80')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'cricket')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'science')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'vegetarian food')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'Wish', 'wife', 'vegetarian food book', 'my wife wants')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'sales guy', 'email', 'jp@example.com')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Work', 'Email', 'vanessa', \"vanessa's email\", 'vanessa@outlook.com')\n",
            "OUTPUT: Yes\n",
            "INPUT ROW: ('Work', 'Reminder', '', 'send ppts', 'rember to send the ppts she asked')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Friends', 'Phone', 'Pedro', 'WhatsApp', '+55 11 27670-0987')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'milk', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'eggs', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'bread', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'old video game')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'chair')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'yoga')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'ballet')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', '??')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Document', 'KWG', 'hardware setup', 'book')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Pets', 'Reminder', 'foreign visitors', 'dog day', '')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Note', 'event support', 'failed', 'we failed :-(')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'floor layout', 'we forgot various details!!')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'first aid kit', 'reception')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Health', 'Doubt', 'pediatrician', 'brushing teeth', 'when to start')\n",
            "OUTPUT: No\n",
            "====================================================================\n",
            "INPUT QUERY: questions for the pediatrician\n",
            "\n",
            "INPUT ROW: ('Health', 'Price', '', 'flu shot', '$80')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'cricket')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'science')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'List', 'boss', 'likes', 'vegetarian food')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'Wish', 'wife', 'vegetarian food book', 'my wife wants')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'sales guy', 'email', 'jp@example.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Email', 'vanessa', \"vanessa's email\", 'vanessa@outlook.com')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Work', 'Reminder', '', 'send ppts', 'rember to send the ppts she asked')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Friends', 'Phone', 'Pedro', 'WhatsApp', '+55 11 27670-0987')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'milk', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'eggs', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'bread', 'buy')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'old video game')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Shopping', 'List', 'Self', 'sell', 'chair')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'yoga')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', 'ballet')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Finance', 'Document', 'gym', 'december receipts', '??')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Document', 'KWG', 'hardware setup', 'book')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Pets', 'Reminder', 'foreign visitors', 'dog day', '')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Other', 'Note', 'event support', 'failed', 'we failed :-(')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'floor layout', 'we forgot various details!!')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Home', 'Note', '', 'first aid kit', 'reception')\n",
            "OUTPUT: No\n",
            "INPUT ROW: ('Health', 'Doubt', 'pediatrician', 'brushing teeth', 'when to start')\n",
            "OUTPUT: No\n",
            "====================================================================\n"
          ]
        }
      ],
      "source": [
        "for query in example_queries:\n",
        "    print(f\"INPUT QUERY: {query}\\n\")\n",
        "    for i, row in database.iterrows():\n",
        "        row_string = f\"{tuple(row.values)}\"\n",
        "        print(f\"INPUT ROW: {row_string}\")\n",
        "        print(f\"OUTPUT: {gpt3_complete(querying_prompt_1(query, row_string))}\")\n",
        "    \n",
        "    print(f\"====================================================================\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fajg6tryt90i"
      },
      "source": [
        "#### Prompt 2\n",
        "\n",
        "\n",
        "An alternative approach would involve extracting key terms from the query and utilizing them for searching within the dataframe. By identifying and extracting essential terms or keywords from the query, we can narrow down the search scope and potentially improve the efficiency of the search process. This strategy aims to enhance the relevance and accuracy of the search results by focusing on the specific terms that are most relevant to the query."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "kAf5aluTt90j"
      },
      "outputs": [],
      "source": [
        "def querying_prompt_2(query):\n",
        "\n",
        "    prompt = \\\n",
        "f\"\"\"\n",
        "Extract the main entities (one per line, without bullets) in the following sentence: \"{query}\"\n",
        "\"\"\"\n",
        "\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsxCiRGEt90j",
        "outputId": "6ce1bbc5-aba8-4e68-c5b0-95dfa3791b67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> INPUT: shopping list\n",
            ">>> OUTPUT:\n",
            "\n",
            "Shopping \n",
            "List\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: what does my boss like?\n",
            ">>> OUTPUT:\n",
            "\n",
            "Boss\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: emails\n",
            ">>> OUTPUT:\n",
            "\n",
            "Email\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: questions for the pediatrician\n",
            ">>> OUTPUT:\n",
            "\n",
            "Pediatrician \n",
            "Questions\n",
            "========================================\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "results = apply_to_examples(example_queries, querying_prompt_2, temperature=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExuxF9QPt90k",
        "outputId": "b73dc232-42d2-474f-d331-5b77810c3000"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Shopping', 'List'], ['Boss'], ['Email'], ['Pediatrician', 'Questions']]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "examples_terms = extract_terms_from_all_results(results)\n",
        "examples_terms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpV2s1_ot90l"
      },
      "source": [
        "#### Prompt 3\n",
        "\n",
        "The current observations indicate that the entity extraction process is functioning effectively. However, there is a consideration regarding the need for synonym expansion. To address this, we propose performing data augmentation as a means to complement the previous prompt.\n",
        "\n",
        "This approach aims to broaden the search scope and improve the comprehensiveness of the results by considering synonymous terms and expanding the coverage of relevant information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "epzWU7JJt90m"
      },
      "outputs": [],
      "source": [
        "def querying_prompt_3(term):\n",
        "\n",
        "    prompt = \\\n",
        "f\"\"\"\n",
        "List some synonyms to the following term: \"{term}\"\n",
        "Synonyms (one synonym per line):\n",
        "\"\"\"\n",
        "\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbkmrFszt90m",
        "outputId": "e704f317-c27d-4637-a9b2-71da98a10073"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> INPUT: Shopping\n",
            ">>> OUTPUT:\n",
            "\n",
            "Purchasing\n",
            "Acquiring\n",
            "Buying\n",
            "Procuring\n",
            "Obtaining\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: List\n",
            ">>> OUTPUT:\n",
            "\n",
            "Inventory\n",
            "Register\n",
            "Catalogue\n",
            "Index\n",
            "Roll\n",
            "Table\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: Boss\n",
            ">>> OUTPUT:\n",
            "\n",
            "Supervisor\n",
            "Manager\n",
            "Chief\n",
            "Leader\n",
            "Head\n",
            "Director\n",
            "Foreman\n",
            "Governor\n",
            "Principal\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: Email\n",
            ">>> OUTPUT:\n",
            "\n",
            "Message\n",
            "Correspondence\n",
            "Communication\n",
            "Post\n",
            "Dispatch\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: Pediatrician\n",
            ">>> OUTPUT:\n",
            "\n",
            "Child Doctor\n",
            "Paediatrician\n",
            "Infant Specialist\n",
            "Kids Physician\n",
            "Youngster Practitioner\n",
            "========================================\n",
            "\n",
            "\n",
            ">>> INPUT: Questions\n",
            ">>> OUTPUT:\n",
            "\n",
            "Inquiries\n",
            "Queries\n",
            "Interrogations\n",
            "Enquiries\n",
            "Examinations\n",
            "Pose\n",
            "Quizzes\n",
            "========================================\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['\\nPurchasing\\nAcquiring\\nBuying\\nProcuring\\nObtaining',\n",
              "  '\\nInventory\\nRegister\\nCatalogue\\nIndex\\nRoll\\nTable'],\n",
              " ['\\nSupervisor\\nManager\\nChief\\nLeader\\nHead\\nDirector\\nForeman\\nGovernor\\nPrincipal'],\n",
              " ['\\nMessage\\nCorrespondence\\nCommunication\\nPost\\nDispatch'],\n",
              " ['\\nChild Doctor\\nPaediatrician\\nInfant Specialist\\nKids Physician\\nYoungster Practitioner',\n",
              "  '\\nInquiries\\nQueries\\nInterrogations\\nEnquiries\\nExaminations\\nPose\\nQuizzes']]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "augmentation_raw_results = []\n",
        "for terms in examples_terms:\n",
        "    augmentation_raw_result = apply_to_examples(terms, querying_prompt_3)\n",
        "    augmentation_raw_results.append(augmentation_raw_result)\n",
        "\n",
        "augmentation_raw_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwG5FREyt90n",
        "outputId": "3b5f4d99-6b50-422f-91ec-2079132d07ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Purchasing',\n",
              "  'Acquiring',\n",
              "  'Buying',\n",
              "  'Procuring',\n",
              "  'Obtaining',\n",
              "  'Inventory',\n",
              "  'Register',\n",
              "  'Catalogue',\n",
              "  'Index',\n",
              "  'Roll',\n",
              "  'Table'],\n",
              " ['Supervisor',\n",
              "  'Manager',\n",
              "  'Chief',\n",
              "  'Leader',\n",
              "  'Head',\n",
              "  'Director',\n",
              "  'Foreman',\n",
              "  'Governor',\n",
              "  'Principal'],\n",
              " ['Message', 'Correspondence', 'Communication', 'Post', 'Dispatch'],\n",
              " ['Child Doctor',\n",
              "  'Paediatrician',\n",
              "  'Infant Specialist',\n",
              "  'Kids Physician',\n",
              "  'Youngster Practitioner',\n",
              "  'Inquiries',\n",
              "  'Queries',\n",
              "  'Interrogations',\n",
              "  'Enquiries',\n",
              "  'Examinations',\n",
              "  'Pose',\n",
              "  'Quizzes']]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "example_augmentations = []\n",
        "for raw_result in augmentation_raw_results:\n",
        "    example_augmentation = extract_terms_from_all_results(raw_result)\n",
        "    flat_example_augmentation = [item for sublist in example_augmentation for item in sublist]\n",
        "    example_augmentations.append(flat_example_augmentation)\n",
        "\n",
        "example_augmentations    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZFfZdMQt90p"
      },
      "source": [
        "### Querying Demo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "kM1Isx_wt90t"
      },
      "outputs": [],
      "source": [
        "def search_dataframe(df, original_terms, augmented_terms):\n",
        "    \"\"\"\n",
        "    Searches the database for the specified terms.\n",
        "    \n",
        "    Parameters:\n",
        "        df (pandas.DataFrame): The dataframe to search.\n",
        "        original_terms (list): The original search terms.\n",
        "        augmented_terms (list): The additional search terms.\n",
        "\n",
        "    Returns:\n",
        "        df_results (pandas.DataFrame): The dataframe containing the search results.\n",
        "    \"\"\"\n",
        "    all_terms = original_terms + augmented_terms\n",
        "    df = df.fillna(\"\")\n",
        "\n",
        "    df_results = None\n",
        "    for column in df.columns:\n",
        "        df_result = df[df[column].str.contains(\"|\".join(all_terms), case=False).fillna(False)]\n",
        "        if df_results is None:\n",
        "            df_results = df_result\n",
        "        else:\n",
        "            df_results = pd.concat([df_results, df_result])\n",
        "            \n",
        "    return df_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "ucAGzbjAt90t",
        "outputId": "5f35f54c-2208-4568-d5c9-72ea218fb618"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Category      Type            People                   Key  \\\n",
              "0     Health     Price                                flu shot   \n",
              "1       Work      List              boss                 likes   \n",
              "2       Work      List              boss                 likes   \n",
              "3       Work      List              boss                 likes   \n",
              "4   Shopping      Wish              wife  vegetarian food book   \n",
              "5       Work     Email         sales guy                 email   \n",
              "6       Work     Email           vanessa       vanessa's email   \n",
              "7       Work  Reminder                               send ppts   \n",
              "8    Friends     Phone             Pedro              WhatsApp   \n",
              "9   Shopping      List              Self                  milk   \n",
              "10  Shopping      List              Self                  eggs   \n",
              "11  Shopping      List              Self                 bread   \n",
              "12  Shopping      List              Self                  sell   \n",
              "13  Shopping      List              Self                  sell   \n",
              "14   Finance  Document               gym     december receipts   \n",
              "15   Finance  Document               gym     december receipts   \n",
              "16   Finance  Document               gym     december receipts   \n",
              "17     Other  Document               KWG        hardware setup   \n",
              "18      Pets  Reminder  foreign visitors               dog day   \n",
              "19     Other      Note     event support                failed   \n",
              "20      Home      Note                            floor layout   \n",
              "21      Home      Note                           first aid kit   \n",
              "22    Health     Doubt      pediatrician        brushing teeth   \n",
              "\n",
              "                                Value  \n",
              "0                                 $80  \n",
              "1                             cricket  \n",
              "2                             science  \n",
              "3                     vegetarian food  \n",
              "4                       my wife wants  \n",
              "5                      jp@example.com  \n",
              "6                 vanessa@outlook.com  \n",
              "7   rember to send the ppts she asked  \n",
              "8                   +55 11 27670-0987  \n",
              "9                                 buy  \n",
              "10                                buy  \n",
              "11                                buy  \n",
              "12                     old video game  \n",
              "13                              chair  \n",
              "14                               yoga  \n",
              "15                             ballet  \n",
              "16                                 ??  \n",
              "17                               book  \n",
              "18                                     \n",
              "19                      we failed :-(  \n",
              "20        we forgot various details!!  \n",
              "21                          reception  \n",
              "22                      when to start  "
            ],
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              "\n",
              "  <div id=\"df-710f967a-b641-41a3-a145-bd9dc8249e54\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
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              "      <th>People</th>\n",
              "      <th>Key</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Health</td>\n",
              "      <td>Price</td>\n",
              "      <td></td>\n",
              "      <td>flu shot</td>\n",
              "      <td>$80</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>cricket</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>science</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Work</td>\n",
              "      <td>List</td>\n",
              "      <td>boss</td>\n",
              "      <td>likes</td>\n",
              "      <td>vegetarian food</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>vegetarian food book</td>\n",
              "      <td>my wife wants</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Work</td>\n",
              "      <td>Email</td>\n",
              "      <td>sales guy</td>\n",
              "      <td>email</td>\n",
              "      <td>jp@example.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Work</td>\n",
              "      <td>Email</td>\n",
              "      <td>vanessa</td>\n",
              "      <td>vanessa's email</td>\n",
              "      <td>vanessa@outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Work</td>\n",
              "      <td>Reminder</td>\n",
              "      <td></td>\n",
              "      <td>send ppts</td>\n",
              "      <td>rember to send the ppts she asked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Friends</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Pedro</td>\n",
              "      <td>WhatsApp</td>\n",
              "      <td>+55 11 27670-0987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>milk</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>eggs</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>bread</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>sell</td>\n",
              "      <td>old video game</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shopping</td>\n",
              "      <td>List</td>\n",
              "      <td>Self</td>\n",
              "      <td>sell</td>\n",
              "      <td>chair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>yoga</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>ballet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Finance</td>\n",
              "      <td>Document</td>\n",
              "      <td>gym</td>\n",
              "      <td>december receipts</td>\n",
              "      <td>??</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Other</td>\n",
              "      <td>Document</td>\n",
              "      <td>KWG</td>\n",
              "      <td>hardware setup</td>\n",
              "      <td>book</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Pets</td>\n",
              "      <td>Reminder</td>\n",
              "      <td>foreign visitors</td>\n",
              "      <td>dog day</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Other</td>\n",
              "      <td>Note</td>\n",
              "      <td>event support</td>\n",
              "      <td>failed</td>\n",
              "      <td>we failed :-(</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Home</td>\n",
              "      <td>Note</td>\n",
              "      <td></td>\n",
              "      <td>floor layout</td>\n",
              "      <td>we forgot various details!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Home</td>\n",
              "      <td>Note</td>\n",
              "      <td></td>\n",
              "      <td>first aid kit</td>\n",
              "      <td>reception</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Health</td>\n",
              "      <td>Doubt</td>\n",
              "      <td>pediatrician</td>\n",
              "      <td>brushing teeth</td>\n",
              "      <td>when to start</td>\n",
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              "  </tbody>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-710f967a-b641-41a3-a145-bd9dc8249e54 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-710f967a-b641-41a3-a145-bd9dc8249e54');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "database # database created initially"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKeJUnjzt90u",
        "outputId": "3d2ed90d-fa7f-49cb-887c-c98843ed542c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search terms: ['Shopping', 'List']\n",
            "Augmented terms: ['Purchasing', 'Acquiring', 'Buying', 'Procuring', 'Obtaining', 'Inventory', 'Register', 'Catalogue', 'Index', 'Roll', 'Table']\n",
            "    Category  Type People                   Key            Value\n",
            "4   Shopping  Wish   wife  vegetarian food book    my wife wants\n",
            "9   Shopping  List   Self                  milk              buy\n",
            "10  Shopping  List   Self                  eggs              buy\n",
            "11  Shopping  List   Self                 bread              buy\n",
            "12  Shopping  List   Self                  sell   old video game\n",
            "13  Shopping  List   Self                  sell            chair\n",
            "1       Work  List   boss                 likes          cricket\n",
            "2       Work  List   boss                 likes          science\n",
            "3       Work  List   boss                 likes  vegetarian food\n",
            "9   Shopping  List   Self                  milk              buy\n",
            "10  Shopping  List   Self                  eggs              buy\n",
            "11  Shopping  List   Self                 bread              buy\n",
            "12  Shopping  List   Self                  sell   old video game\n",
            "13  Shopping  List   Self                  sell            chair\n",
            "====================\n",
            "Search terms: ['Boss']\n",
            "Augmented terms: ['Supervisor', 'Manager', 'Chief', 'Leader', 'Head', 'Director', 'Foreman', 'Governor', 'Principal']\n",
            "  Category  Type People    Key            Value\n",
            "1     Work  List   boss  likes          cricket\n",
            "2     Work  List   boss  likes          science\n",
            "3     Work  List   boss  likes  vegetarian food\n",
            "====================\n",
            "Search terms: ['Email']\n",
            "Augmented terms: ['Message', 'Correspondence', 'Communication', 'Post', 'Dispatch']\n",
            "  Category   Type     People              Key                Value\n",
            "5     Work  Email  sales guy            email       jp@example.com\n",
            "6     Work  Email    vanessa  vanessa's email  vanessa@outlook.com\n",
            "5     Work  Email  sales guy            email       jp@example.com\n",
            "6     Work  Email    vanessa  vanessa's email  vanessa@outlook.com\n",
            "====================\n",
            "Search terms: ['Pediatrician', 'Questions']\n",
            "Augmented terms: ['Child Doctor', 'Paediatrician', 'Infant Specialist', 'Kids Physician', 'Youngster Practitioner', 'Inquiries', 'Queries', 'Interrogations', 'Enquiries', 'Examinations', 'Pose', 'Quizzes']\n",
            "   Category   Type        People             Key          Value\n",
            "22   Health  Doubt  pediatrician  brushing teeth  when to start\n",
            "====================\n"
          ]
        }
      ],
      "source": [
        "for i, original_terms in enumerate(examples_terms):\n",
        "    augmented_terms = example_augmentations[i]\n",
        "    print(f\"Search terms: {original_terms}\")\n",
        "    print(f\"Augmented terms: {augmented_terms}\")\n",
        "    print(search_dataframe(database, original_terms, augmented_terms))\n",
        "    print(f\"====================\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "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": "3.8.5"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "33a43c2ecf7833757cc12407d902cce5b65eac163e939f096c9930e07c500142"
      }
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
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  "nbformat_minor": 0
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