diff --git a/README.md b/README.md
index bb3105c..84fabfb 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,2 @@
-# Gradient Works Interview Repo
-
-Hello friends! If you're here, you're probably looking for an engineering job at Gradient Works and I'm excited you're here!
-
-This repo is a collection of exercises that we use to evaluate candidates for engineering roles at Gradient Works. We hope you find them interesting and challenging. We also hope you find them a good representation of the kind of work you'd be doing here.
-
-If you're looking for the Machine learning role, please check out the [machine learning exercises](./machine-learning/README.md).
-
-We're not currently looking to hire someone for a Salesforce engineering role, but if you have a morbid curiosity around that, you can check out the [Salesforce exercises](./salesforce/README.md).
\ No newline at end of file
+## RAG based LLM 
+### model: GPT 3.5
diff --git a/machine-learning/README.md b/machine-learning/README.md
index a772d8b..8b13789 100644
--- a/machine-learning/README.md
+++ b/machine-learning/README.md
@@ -1,46 +1 @@
-# Gradient Works Machine Learning Interview Repo
 
-Hello friends! If you're reading this, you're probably interviewing for a machine learning position at Gradient Works. If not, GET OUT! SHOO! Go on now, git! Just kidding, you can hang out. But you're probably not going to get much out of this repo. Maybe you will, who knows? I'm not your mom.
-
-Anyway, if you're here for the interview, welcome! I'll outline the steps for the whole process here and if you have any questions, send an email to mando@gradient.works and he'll clear things up.
-
-## Step 1: Phone screen
-
-You've probably already done this, but if not, we'll schedule a 30-minute phone call so we can get to know each other. We'll chat about your background and experience, and we'll also talk about Gradient Works, the role and what you're looking for in your next job. If everything is a good fit, we'll move on to the next step.
-
-## Step 2: Technical exercise
-
-We'll send you a technical exercise to complete. It's a Jupyter notebook that you can run in your own environment or in Google Colab, plus a tiny CSV dataset. The exercise will test your ability to explore data, answer questions about that data, and build a small RAG system to answer arbitrary questions about the data.
-
-We expect the exercise to take 1-3 hours, depending on how far you take the RAG system. We're not looking for a perfect solution, but we are looking for a thoughtful approach and a good understanding of the approach (there's more detail in the notebook).
-
-You can find the exercise [here](./exercise.ipynb).
-
-To complete the exercise, you can either:
-
-1. fork the repo and submit a pull request with your solution, or
-2. import the notebook into your own Google Colab environment and share the notebook with mando@gradient.works
-
-If neither of those work, email mando@gradient.works and we'll figure something out.
-
-> [!NOTE]
->
-> If possible, we'd like to invite you into our Slack while you're working on the exercise. This way, you can ask questions and we can get to know each other a bit better. If you're interested, let us know and we'll send you an invite.
-
-> [!NOTE]
->
-> You might be asking yourself, "Doesn't this dummy know that I can just look at the notebook and get started early?". And yeah, I get it. But the whole point of this is for you to demonstrate your abilities and level of understanding. So, if you spend a bunch of time learning all the ins and outs of RAG systems ahead of time, well, I mean that's great for us :).
-
-Once you've completed the exercise, we'll review it and schedule a Zoom interview to discuss your solution.
-
-## Step 3: Zoom interview
-
-We'll schedule a 1.5-hour Zoom interview to discuss your solution to the technical exercise. We'll ask you to walk us through your approach, explain your code, how you might extend the solution, etc. We'll also make sure to leave plenty of time for you to ask us questions about the team, the company, and what we're building and how.
-
-After that, we'll make a decision and let you know as soon as possible.
-
-## Step 4: There's no Step 4
-
-Hopefully this all makes sense and you're excited to get started. Interviewing is hard and we get that. We hope that this process makes sense and is as stress-free as it can be.
-
-As always, feedback is a gift, so if you have any thoughts on how we can improve this process, please let us know.
\ No newline at end of file
diff --git a/machine-learning/exercise.ipynb b/machine-learning/exercise.ipynb
index 1c64854..cd15b06 100644
--- a/machine-learning/exercise.ipynb
+++ b/machine-learning/exercise.ipynb
@@ -1,136 +1,1432 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Welcome to our ML project!\n",
-    "\n",
-    "This is a quick exercise to help demonstrate your familiarity with RAG systems - one might say that this is a place where you can b**RAG** about your skills! 🤣\n",
-    "\n",
-    "In this exercise, you will be asked to build a simple RAG system that answer some provided questions using the dataset provided. We expect this exercise to take 1-3 hours TOPS so use that to temper your approach to building this. We're not looking for reusable or production-level code - we're expressly looking for you to show us that you:\n",
-    "\n",
-    "* can explore an unknown dataset\n",
-    "* can use an LLM (in this case, OpenAI's GPT-3) to build a simple RAG system\n",
-    "\n",
-    "## The Dataset\n",
-    "\n",
-    "You'll find the dataset in `content.csv`. It is a set of content about companies that has been scraped from the web. It contains the following columns:\n",
-    "\n",
-    "* `company_id`: a unique identifier for the company (UUID)\n",
-    "* `company_name`: the name of the company\n",
-    "* `url`: the URL from which the content was scraped\n",
-    "* `chunk`: a chunk of the content that was scraped from the `url`\n",
-    "* `chunk_hash`: a hash of the chunk\n",
-    "* `chunk_id`: a unique identifier for the chunk of content\n",
-    "* `chunk_type`: the type of the chunk of content (e.g. `header`, `footer`)\n",
-    "\n",
-    "\n",
-    "Here's an example:\n",
-    "\n",
-    "|company_id|company_name|url|chunk_type|chunk_hash|chunk|chunk_id|\n",
-    "|---|---|---|---|---|---|---|\n",
-    "|4c1fde18-8a40-4ee7-9c3c-19152c7d1ff8|Aboitiz Group|https://aboitiz.com/about-us/the-aboitiz-way/|head|d312f0c688076be80ee2e4af8a51c2f10cbb993a4a8de779cb4aa5545fe1051f|\"<head>Aboitiz - The Aboitiz Way</head>\"|be36e2f0-cd0b-42eb-b36d-c9403c2428be|\n",
-    "\n",
-    "## Step 1: Explore the dataset\n",
-    "\n",
-    "Here are some questions that we'd like you to answer about the dataset:\n",
-    "\n",
-    "1. How many companies are in the dataset?\n",
-    "2. How many unique URLs are in the dataset?\n",
-    "3. What is the most common chunk type?\n",
-    "4. What is the distribution of chunk types by company?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pandas in ./.venv/lib/python3.11/site-packages (2.2.1)\n",
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-      "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.11/site-packages (from pandas) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.11/site-packages (from pandas) (2024.1)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in ./.venv/lib/python3.11/site-packages (from pandas) (2024.1)\n",
-      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "Collecting matplotlib\n",
-      "  Downloading matplotlib-3.8.3-cp311-cp311-macosx_11_0_arm64.whl.metadata (5.8 kB)\n",
-      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
-      "  Using cached contourpy-1.2.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (5.8 kB)\n",
-      "Collecting cycler>=0.10 (from matplotlib)\n",
-      "  Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
-      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
-      "  Using cached fonttools-4.50.0-cp311-cp311-macosx_10_9_universal2.whl.metadata (159 kB)\n",
-      "Collecting kiwisolver>=1.3.1 (from matplotlib)\n",
-      "  Using cached kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl.metadata (6.4 kB)\n",
-      "Requirement already satisfied: numpy<2,>=1.21 in ./.venv/lib/python3.11/site-packages (from matplotlib) (1.26.4)\n",
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-      "Collecting pillow>=8 (from matplotlib)\n",
-      "  Using cached pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (9.7 kB)\n",
-      "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
-      "  Using cached pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.11/site-packages (from matplotlib) (2.9.0.post0)\n",
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-      "Downloading matplotlib-3.8.3-cp311-cp311-macosx_11_0_arm64.whl (7.5 MB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-      "\u001b[?25hUsing cached contourpy-1.2.0-cp311-cp311-macosx_11_0_arm64.whl (243 kB)\n",
-      "Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
-      "Using cached fonttools-4.50.0-cp311-cp311-macosx_10_9_universal2.whl (2.8 MB)\n",
-      "Using cached kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl (66 kB)\n",
-      "Using cached pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl (3.3 MB)\n",
-      "Using cached pyparsing-3.1.2-py3-none-any.whl (103 kB)\n",
-      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
-      "Successfully installed contourpy-1.2.0 cycler-0.12.1 fonttools-4.50.0 kiwisolver-1.4.5 matplotlib-3.8.3 pillow-10.2.0 pyparsing-3.1.2\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*Nicole Mathias*\n",
+        "\n",
+        "Instructions to run the notebook\n",
+        "\n",
+        "\n",
+        "*   Add exercise.ipynb and content.csv to your drive (to specific folder) - my code will add it to a folder automatically."
+      ],
+      "metadata": {
+        "id": "mmjGtnEgU-LP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Vql-4RvAUJVj"
+      },
+      "source": [
+        "# Welcome to our ML project!\n",
+        "\n",
+        "This is a quick exercise to help demonstrate your familiarity with RAG systems - one might say that this is a place where you can b**RAG** about your skills! 🤣\n",
+        "\n",
+        "In this exercise, you will be asked to build a simple RAG system that answer some provided questions using the dataset provided. We expect this exercise to take 1-3 hours TOPS so use that to temper your approach to building this. We're not looking for reusable or production-level code - we're expressly looking for you to show us that you:\n",
+        "\n",
+        "* can explore an unknown dataset\n",
+        "* can use an LLM (in this case, OpenAI's GPT-3) to build a simple RAG system\n",
+        "\n",
+        "## The Dataset\n",
+        "\n",
+        "You'll find the dataset in `content.csv`. It is a set of content about companies that has been scraped from the web. It contains the following columns:\n",
+        "\n",
+        "* `company_id`: a unique identifier for the company (UUID)\n",
+        "* `company_name`: the name of the company\n",
+        "* `url`: the URL from which the content was scraped\n",
+        "* `chunk`: a chunk of the content that was scraped from the `url`\n",
+        "* `chunk_hash`: a hash of the chunk\n",
+        "* `chunk_id`: a unique identifier for the chunk of content\n",
+        "* `chunk_type`: the type of the chunk of content (e.g. `header`, `footer`)\n",
+        "\n",
+        "\n",
+        "Here's an example:\n",
+        "\n",
+        "|company_id|company_name|url|chunk_type|chunk_hash|chunk|chunk_id|\n",
+        "|---|---|---|---|---|---|---|\n",
+        "|4c1fde18-8a40-4ee7-9c3c-19152c7d1ff8|Aboitiz Group|https://aboitiz.com/about-us/the-aboitiz-way/|head|d312f0c688076be80ee2e4af8a51c2f10cbb993a4a8de779cb4aa5545fe1051f|\"<head>Aboitiz - The Aboitiz Way</head>\"|be36e2f0-cd0b-42eb-b36d-c9403c2428be|\n",
+        "\n",
+        "## Step 1: Explore the dataset\n",
+        "\n",
+        "Here are some questions that we'd like you to answer about the dataset:\n",
+        "\n",
+        "1. How many companies are in the dataset?\n",
+        "2. How many unique URLs are in the dataset?\n",
+        "3. What is the most common chunk type?\n",
+        "4. What is the distribution of chunk types by company?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AhUSjSgJUJVl",
+        "outputId": "dfe7807b-4677-413e-d451-9413efd07f38"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
+            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.50.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
+            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.25.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.0)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.16.2)\n",
+            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
+            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.27.0)\n",
+            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai) (2.6.4)\n",
+            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai) (1.3.1)\n",
+            "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.2)\n",
+            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai) (4.10.0)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (1.2.0)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
+            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
+            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (0.6.0)\n",
+            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (2.16.3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install pandas\n",
+        "%pip install matplotlib\n",
+        "%pip install openai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd"
+      ],
+      "metadata": {
+        "id": "SyxR7w6bUZn-"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZnjVN4qjU0mR",
+        "outputId": "ab2d0b19-e5f4-487a-a6a0-3f9cea13d0a8"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "# The below code will move content.csv and exercise.ipynb to a specific folder\n",
+        "# Just upload both the files to your drive (No need to add them to a folder, its all handled below)\n",
+        "folder_path = \"/content/drive/MyDrive/gradient_works_data\"\n",
+        "csv_file_path = \"/content/drive/MyDrive/content.csv\"\n",
+        "py_file_path = \"/content/drive/MyDrive/exercise.ipynb\"\n",
+        "\n",
+        "if not os.path.exists(folder_path):\n",
+        "  os.makedirs(folder_path)\n",
+        "\n",
+        "  shutil.move(py_file_path, folder_path)\n",
+        "  shutil.move(csv_file_path, folder_path)\n",
+        "\n",
+        "\n",
+        "os.chdir(folder_path)"
+      ],
+      "metadata": {
+        "id": "N7v-R5TuU2Tt"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# reading data from csv\n",
+        "df = pd.read_csv('content.csv')"
+      ],
+      "metadata": {
+        "id": "dUryU8cOVovz"
+      },
+      "execution_count": 143,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### PART 1 Questions"
+      ],
+      "metadata": {
+        "id": "luU2ySX_V4mo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1. How many companies are in the dataset?\n",
+        "no_companies = len(df['company_name'].unique())\n",
+        "print(\"No. of Companies:\",no_companies)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q8Ii5lkgV7Cm",
+        "outputId": "0443a6b0-aa42-4c16-9d23-aecf79537c63"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "No. of Companies: 75\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 2. How many unique URLs are in the dataset?\n",
+        "no_url = len(df['url'].unique())\n",
+        "print(\"No. of unique URL's\",no_url)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a5HsVjy7WFDX",
+        "outputId": "f641260c-fbb9-4e19-b4f3-b545f762ef5a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "No. of unique URL's 530\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3. What is the most common chunk type?\n",
+        "most_common_chunk = df['chunk_type'].mode().item()\n",
+        "print(\"Most common chunk:\",most_common_chunk)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3VcXkmR3WJDF",
+        "outputId": "49714849-ec02-4724-e23e-7b93fc1d6b02"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Most common chunk: header\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 4. What is the distribution of chunk types by company?\n",
+        "\n",
+        "# - group by companies and then for each company group get the count of chunk_types\n",
+        "chunk_types = df.groupby(\"company_name\")[\"chunk_type\"].value_counts()\n",
+        "\n",
+        "chunk_grp_count = chunk_types.unstack(fill_value=0)\n",
+        "chunk_grp_count"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "RBhmH1MUWMrn",
+        "outputId": "85a1395b-2e77-4a0f-83a2-3932bf3c8a53"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "chunk_type            footer  head  header  main\n",
+              "company_name                                    \n",
+              "24Hrbookkeeper             0     1       0     1\n",
+              "365games.net              10    11      11    11\n",
+              "4wheeltravels.com          0     2       0     2\n",
+              "579twu.org                 0    11       0    11\n",
+              "66corporation.com          4     4       4     4\n",
+              "...                      ...   ...     ...   ...\n",
+              "bhatiagraphica.com         5     5       7     0\n",
+              "bijvoorbeeldzo            11    11      11    11\n",
+              "bikesandmunchies.com       9     9       9     0\n",
+              "bingotech.net              2     2       1     2\n",
+              "contact@acom.co.id         2     2       1     2\n",
+              "\n",
+              "[75 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-723a8f68-47c0-4d68-9ebe-0d9906755545\" 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>chunk_type</th>\n",
+              "      <th>footer</th>\n",
+              "      <th>head</th>\n",
+              "      <th>header</th>\n",
+              "      <th>main</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>company_name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>24Hrbookkeeper</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>365games.net</th>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4wheeltravels.com</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>579twu.org</th>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>66corporation.com</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bhatiagraphica.com</th>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bijvoorbeeldzo</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bikesandmunchies.com</th>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bingotech.net</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>contact@acom.co.id</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>75 rows × 4 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-723a8f68-47c0-4d68-9ebe-0d9906755545')\"\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",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-723a8f68-47c0-4d68-9ebe-0d9906755545 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-723a8f68-47c0-4d68-9ebe-0d9906755545');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-bf0eb6d7-a9fe-4c60-bf23-5451f0ca3ccb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf0eb6d7-a9fe-4c60-bf23-5451f0ca3ccb')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-bf0eb6d7-a9fe-4c60-bf23-5451f0ca3ccb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "chunk_grp_count",
+              "summary": "{\n  \"name\": \"chunk_grp_count\",\n  \"rows\": 75,\n  \"fields\": [\n    {\n      \"column\": \"company_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 75,\n        \"samples\": [\n          \"66corporation.com\",\n          \"accentronix.co.za\",\n          \"ABBE Technology Solutions Inc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"footer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 18,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          18,\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"head\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          11,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"header\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          1,\n          7,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"main\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 18,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          1,\n          11,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Creating a plot for Q.4\n",
+        "colors = ['teal', 'brown', 'orange', 'red']\n",
+        "\n",
+        "chunk_grp_count.plot(kind='bar', stacked=True, color=colors,\n",
+        "                     xlabel='Company name',\n",
+        "                     ylabel='Count by chunk type',\n",
+        "                     title='Distribution of Chunk Types by Company',\n",
+        "                     figsize=(20,10)\n",
+        "                     )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 810
+        },
+        "id": "o0WBcinLW5oQ",
+        "outputId": "0f0b5c14-01af-48bf-bade-be37952537c2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: title={'center': 'Distribution of Chunk Types by Company'}, xlabel='Company name', ylabel='Count by chunk type'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EapiCF4lUJVl"
+      },
+      "source": [
+        "## Step 2: RAGtime!\n",
+        "\n",
+        "Now that you're a little more familar with the dataset, let's build a simple RAG system that uses OpenAI to help answer some questions about the dataset. To reiterate, we don't expect you to add anything else to the environment to build this system - for example, you don't need to set up a database or anything like that. You can add any libraries you need to the environment, but we'd like you to use OpenAI for any and all tasks that require a language model (we'll send you a key to use).\n",
+        "\n",
+        "That being said, we'd like you to show the specifics of how a RAG implementation works so please avoid using any libraries that provide end-to-end RAG implementations.\n",
+        "\n",
+        "Here is the question that we'd like you to answer via your RAG system:\n",
+        "\n",
+        "1. What does the company Caravan Health do?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Steps for preparing the model:\n",
+        "# 1) Getting data & cleaning it - Cleaning can invloving preprocessing like removing stop-words and other information retrieval techniques.\n",
+        "# 2) Chunking logic - based on token size of the embedding and LLM model\n",
+        "# 3) Embedding the chunks\n",
+        "\n",
+        "# Steps for query\n",
+        "# 1) Getting the query\n",
+        "# 2) If query is too long - chunk it\n",
+        "# 3) Embed the query\n",
+        "\n",
+        "# Similarity and scoring mechanism\n",
+        "# 1) Find the similarity scores (cosine) between query embeddings and the chunk embeddings\n",
+        "# 2) Based on the scores, retrieve top k docs\n",
+        "\n",
+        "# Generating answers\n",
+        "# 1) Use the top k documents as context and provide it to the LLM model"
+      ],
+      "metadata": {
+        "id": "7G9DBsM7YXr-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Data Scraping - Retrieving data from HTML tags\n",
+        "\n",
+        "- Using Beautiful soup to parse the html tags and obtain data.\n",
+        "- There are other tools like selenium as well"
+      ],
+      "metadata": {
+        "id": "PWhV_x0XY5eE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install beautifulsoup4"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rwNSb5pvY9Tq",
+        "outputId": "4c742b52-1a36-43ac-cc1b-bfe4953dc2db"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (4.12.3)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4) (2.5)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from bs4 import BeautifulSoup"
+      ],
+      "metadata": {
+        "id": "TZdtoSIXY_bA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# function to parse html tags and extract data\n",
+        "def text_cleaner():\n",
+        "  df['desc'] = ''\n",
+        "\n",
+        "  for idx in range(len(df)):\n",
+        "    chunk_content = df['chunk'][idx]\n",
+        "    company_name = df['company_name'][idx]\n",
+        "\n",
+        "    content = BeautifulSoup(chunk_content,'lxml')\n",
+        "\n",
+        "    text_content = content.get_text(separator = '\\n', strip = True)\n",
+        "    df['desc'][idx] = company_name + \" \" + text_content"
+      ],
+      "metadata": {
+        "id": "SsPDU_MkZjJQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text_cleaner()\n",
+        "print(\"---Html data was cleaned---\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ijUpIeL2atXv",
+        "outputId": "91e35b33-3d24-45f0-a54a-b918594b0274"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "---Html data was cleaned---\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Chunking logic\n",
+        "\n",
+        "\n",
+        "*   Chunking is being performed in order to create list of tokens - that can be fed to the embedding model.\n",
+        "\n",
+        "*   Embedding models have different token sizes and we can develop a chunking logic based on the specific embedding model and LLM, but to keep it simple I will use a token size = 512 (most of the models use 512 size size currently. So this code can be easily mapped to other embedding models if required - I would want to try more models and experiment to make a conclusion)\n",
+        "\n",
+        "* I made the above point because, based on analysis there were chunks in languages other than English as well, so a multi-lingual model would be required for it - so it is safe to use a size of 512\n",
+        "\n",
+        "\n",
+        "\n",
+        "**Additional text preprocessing steps can be performed:**\n",
+        "\n",
+        "\n",
+        "1.   Removal of stop-words (common words like a, the) which will improve query-document matching during retrieval.\n",
+        "\n",
+        "2. Converting all the words to lowercase - might improve the embeddings.\n",
+        "\n",
+        "3.   Using a bag of words model (used in NLP) is a simple way of using embeddings without a pretrained model\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "NIbC_hKGbNg1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re"
+      ],
+      "metadata": {
+        "id": "UgT49-s9bTX2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data splitting logic:\n",
+        "\n",
+        "1.   I am splitting every data chunk sentence-wise. (Splitting it paragraph-wise and then breaking it down would have been better for context: will look into this later)\n",
+        "\n",
+        "2.   After I have my individual sentence - I split the sentence and check if the no. of token <= 512, then I keep it intact\n",
+        "\n",
+        "3. If no. of tokens > 512, then I break the token list into smaller sublist's.\n",
+        "\n",
+        "4. Also, experiments can be performed by changing the chunk size and chunk overlap which will help determine which setting workds best for our given task.\n",
+        "\n",
+        "*The above logic is based on preserving context of the data, which is better than randomly splitting*\n"
+      ],
+      "metadata": {
+        "id": "ukSyogw9e47d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def split_tokens(tokens, max_size):\n",
+        "\n",
+        "  if not tokens:\n",
+        "      return []\n",
+        "\n",
+        "  # If the length of tokens is less than or equal to max_size, return the tokens as a single sublist\n",
+        "  if len(tokens) <= max_size:\n",
+        "    return tokens\n",
+        "\n",
+        "  # Split the tokens into two parts: the first max_size tokens and the rest\n",
+        "  first_part = tokens[:max_size]\n",
+        "  remaining_tokens = tokens[max_size:]\n",
+        "\n",
+        "  # Recursively split the remaining tokens\n",
+        "  remaining_parts = split_tokens(remaining_tokens, max_size)\n",
+        "\n",
+        "  # Combine the first part with the recursively split remaining parts\n",
+        "  return first_part + remaining_parts"
+      ],
+      "metadata": {
+        "id": "78y_PMDQdqJU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**I wanted to come back and implement the overlap context feature below - but the creation of embeddings took more time than I imagined.**\n",
+        "\n",
+        "*Advantage: This feature would be useful.\n",
+        "Remember, I had split the data based on sentences, but from my observation there were sentences which exceeded the max token limit of 512, so I broke the sentence into a sub-sentences.\n",
+        "The problem here is that these broken sentences loose context, and my idea of preserving the context was through overlapping some of the data*"
+      ],
+      "metadata": {
+        "id": "w8y8AIoOzt5N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_chunks(max_size):\n",
+        "  max_size = max_size  # Maximum size for each sublist\n",
+        "\n",
+        "  df['chunked_data'] = ''\n",
+        "\n",
+        "  # overlapping data from previous context - I am keeping it fixed and can experiment with it later (this is the baseline)\n",
+        "  for idx in range(len(df)):\n",
+        "    # data_split = re.split(r'\\.|\\s+', df['desc'][idx]) # split the desc based on '.' or ' '\n",
+        "    data_split = re.split(r'\\.',df['desc'][idx])\n",
+        "\n",
+        "    sentence_chunks = []\n",
+        "    for sent in data_split:\n",
+        "      # calling the function to split and tokenize sentences\n",
+        "      sentence = \" \".join(split_tokens(sent.split(), max_size))\n",
+        "      sentence_chunks.append(sentence)\n",
+        "\n",
+        "    # df['chunked_data'][idx] = split_tokens(data_split, max_size)\n",
+        "    df['chunked_data'][idx] = sentence_chunks\n"
+      ],
+      "metadata": {
+        "id": "4AMdYzHfd5b-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_size = 512\n",
+        "create_chunks(max_size)\n",
+        "\n",
+        "print(\"---Data has been chunked---\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I5rzTMoCgY5_",
+        "outputId": "40129fbc-3d3d-4de6-b801-34bda84dc058"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "---Data has been chunked---\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "saving the dataframe for a backup - this is not a necessary step\n"
+      ],
+      "metadata": {
+        "id": "1DuYyd6F3Fx7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# using pickle - which preserving the data perfectly, even json is another good option\n",
+        "import pickle\n",
+        "with open('df_pickle.pkl', 'wb') as f:\n",
+        "    pickle.dump(df, f)"
+      ],
+      "metadata": {
+        "id": "t-e1AoSSgjvr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " ### Embeddding Logic and getting top k docs\n",
+        "\n",
+        "\n",
+        "*   Saved the API auth key as a secret key named as auth_key\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "0DqJYcZBhyD1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install openai"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ubNkFAWUh1_v",
+        "outputId": "29b10621-5fa2-4fbb-fce0-451a19c55ff6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.16.2)\n",
+            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
+            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.27.0)\n",
+            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai) (2.6.4)\n",
+            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai) (1.3.1)\n",
+            "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.2)\n",
+            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai) (4.10.0)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (1.2.0)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
+            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
+            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (0.6.0)\n",
+            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (2.16.3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import openai"
+      ],
+      "metadata": {
+        "id": "79L8lz5QiTPs"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import userdata\n",
+        "API_AUTH = userdata.get('auth_key')"
+      ],
+      "metadata": {
+        "id": "fFB7WlSXh6PX"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "openai.api_key = API_AUTH"
+      ],
+      "metadata": {
+        "id": "5kri4LtCiP9J"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# getting embeddings for the chunked data and saving them\n",
+        "def get_embedding(text_to_embed):\n",
+        "  # Embed the list of text\n",
+        "  response = openai.embeddings.create(\n",
+        "    model = \"text-embedding-3-small\",\n",
+        "    input = text_to_embed\n",
+        "  )\n",
+        "\n",
+        "  # embeddings = []\n",
+        "\n",
+        "  # for i in range(embed_len):\n",
+        "  #   embeddings.append(response.data[i].embedding)\n",
+        "\n",
+        "  return response.data[0].embedding"
+      ],
+      "metadata": {
+        "id": "jFGloqWNiSBs"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['embeddings'] = ''\n",
+        "for idx in range(len(df)):\n",
+        "  embeddings_list = []\n",
+        "\n",
+        "  for chunk in df['chunked_data'][idx]:\n",
+        "    embeddings_list.append(get_embedding(chunk))\n",
+        "\n",
+        "  df['embeddings'][idx] = embeddings_list\n"
+      ],
+      "metadata": {
+        "id": "pHs4_Kb7tJl_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Since I am not creating a vector DB to store the embeddings, I will try to simulate it using simple file storage.**\n",
+        "\n",
+        "* Created a pickle file which contains the embeddings and the source information.\n",
+        "\n",
+        "\n",
+        "*   My vector DB would contain - (chunk_id-unique attribute, embeddings)\n",
+        "\n",
+        "\n",
+        "\n",
+        "*Advantage: Embeddings need to be created only once (only if we do not change the embedding model) - so I can store the current embeddings and keep adding new ones as more data is added to the system*"
+      ],
+      "metadata": {
+        "id": "41t9_M17xvpO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Query Embeddings**"
+      ],
+      "metadata": {
+        "id": "ptiW30pR4eqc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Improvement:**\n",
+        "* Before Deep Learning models, the same tasks were performed using information retrieval systems (used in search engines as well)\n",
+        "\n",
+        "* I propose a similar system - We can match the keywords of the query with the documents - there is a whole different technique of using bag of words as embeddings, word normalizations, creating dictionaries, scoring and retrieving the top k documents. I have worked on this task in the past and it works well (but not perfectly, nothing is) and is used in elastic search.\n",
+        "\n",
+        "* Suggestion: We can use both embeddings and this information retrieval technique and then check which model has retrieved better documents and based on that use our LLM to prepare an answer."
+      ],
+      "metadata": {
+        "id": "L9YJl3KF-L0p"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "query = \"What does the company Caravan Health do?\"\n",
+        "query_embedding = get_embedding(query)\n",
+        "# query_embedding"
+      ],
+      "metadata": {
+        "id": "vIt8pRXq-F_p"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Retrieving top k documents based on similarity scores**\n",
+        "\n",
+        "* There are various scoring mechanisms which we can experiment with.\n",
+        "* I have used cosine score, because it is quick ( uses dot product in calculations) and relatively good.\n",
+        "\n",
+        "**Improvement**: I would also re-rank the documents, because I know there were some chunks which did not have any data (i.e the html parser did not produce any data) - So I just added the company name as data for such cases. But at this point these documents are not so useful and it would rather make sense re-rank the documents and get better data."
+      ],
+      "metadata": {
+        "id": "NWgc1oHc-47Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install scikit-learn"
+      ],
+      "metadata": {
+        "id": "i3qBG66pp558"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity"
+      ],
+      "metadata": {
+        "id": "l2NRw3L-ppEr"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# generating scores using cosine distance\n",
+        "def get_score(query, document):\n",
+        "  similarity = cosine_similarity(np.array(query).reshape(-1,1), np.array(document).reshape(-1,1))\n",
+        "\n",
+        "  similarity = np.array(similarity)\n",
+        "  cosine_scores = np.mean(similarity)\n",
+        "\n",
+        "  return cosine_scores"
+      ],
+      "metadata": {
+        "id": "2SYjnsg7p335"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def retrieve_top_docs(query_embedding, k):\n",
+        "  score_dict = {}\n",
+        "\n",
+        "  for idx in range(len(df)):\n",
+        "  score_dict[df['chunk_id'][idx]] = get_score(query_embedding,df['embeddings'][idx])\n",
+        "\n",
+        "  sorted_cosine = dict(sorted(score_dict.items(), key=lambda item: item[1], reverse=True))\n",
+        "  top_k = dict(list(sorted_cosine.items())[:k])\n",
+        "\n",
+        "  return score_dict"
+      ],
+      "metadata": {
+        "id": "wrnYKKHR_Hc8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "All cosine scores are stored in the below pickle file along with thier chunk_id -\n",
+        "* I am directly retrieving it from a saved file, because I accidentally overwrote my embeddings file earlier and then had a series of issues --> creating embeddings again is very costly, so I left it as is for now"
+      ],
+      "metadata": {
+        "id": "7IbeavTx3Ijn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import pickle\n",
+        "with open('all_scores.pkl', 'rb') as f:\n",
+        "  all_scores = pd.DataFrame(pickle.load(f))"
+      ],
+      "metadata": {
+        "id": "XmneToTayN5S"
+      },
+      "execution_count": 144,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with open('df_pickle.pkl', 'rb') as f:\n",
+        "  df = pd.DataFrame(pickle.load(f))"
+      ],
+      "metadata": {
+        "id": "P5iFlSUa1Bri"
+      },
+      "execution_count": 145,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_context(k, all_scores, df):\n",
+        "  context_data = ''\n",
+        "  top_scores = all_scores.nlargest(k, 'score')\n",
+        "  top_scores_id = top_scores[\"chunk_id\"]\n",
+        "\n",
+        "  for i in range(k):\n",
+        "    id = top_10_scores.iloc[i]['chunk_id']\n",
+        "    context_data += df.loc[df['chunk_id'] == id, 'desc'].values\n",
+        "\n",
+        "  return context_data[0]"
+      ],
+      "metadata": {
+        "id": "rZtkEgQt0vSv"
+      },
+      "execution_count": 158,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Generating a response"
+      ],
+      "metadata": {
+        "id": "1P26TN7VNdo1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "context = get_context(10, all_scores, df)\n",
+        "query = \"What does the company Caravan Health do?\"\n",
+        "\n",
+        "\n",
+        "\n",
+        "response = openai.chat.completions.create(\n",
+        "    messages = [\n",
+        "        {\"role\": \"system\", \"content\": \"You will answer questions like a chat system. If you do not understand a question and if it doesnt match any context that is given to you, simply tell the user that you cannot answer it.\"},\n",
+        "        {\"role\": \"user\", \"content\": query + \" \" + context}\n",
+        "    ],\n",
+        "    model = \"gpt-3.5-turbo-0125\",\n",
+        "    temperature = 0.0\n",
+        ")\n",
+        "\n",
+        "# print(response.choices[0].message.content)"
+      ],
+      "metadata": {
+        "id": "s0Vi5PgWNiPT"
+      },
+      "execution_count": 159,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "response.choices[0].message.content\n"
+      ],
+      "metadata": {
+        "id": "2BIh1TOjRpRl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 52
+        },
+        "outputId": "1ffb2c6d-1047-427d-ec88-9ab0090bd563"
+      },
+      "execution_count": 160,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'Caravan Health is a company that partners with healthcare providers to help them transition to value-based care and improve patient outcomes. They offer solutions and support to help healthcare organizations succeed in accountable care and population health management.'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 160
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**How would I scale this RAG based LLM?**\n",
+        "* I would use a cloud provider - Amazon\n",
+        "* Move the entire codebase to Sagemaker and deploy my model\n",
+        "* Create a vector database and store embeddings and their source (unique identifier) - vector DB: PineCone\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5OKZ2rTW6COD"
+      }
+    }
+  ],
+  "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": "3.11.7"
+    },
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "luU2ySX_V4mo"
+      ]
     }
-   ],
-   "source": [
-    "%pip install pandas\n",
-    "%pip install matplotlib\n",
-    "%pip install openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2: RAGtime!\n",
-    "\n",
-    "Now that you're a little more familar with the dataset, let's build a simple RAG system that uses OpenAI to help answer some questions about the dataset. To reiterate, we don't expect you to add anything else to the environment to build this system - for example, you don't need to set up a database or anything like that. You can add any libraries you need to the environment, but we'd like you to use OpenAI for any and all tasks that require a language model (we'll send you a key to use).\n",
-    "\n",
-    "That being said, we'd like you to show the specifics of how a RAG implementation works so please avoid using any libraries that provide end-to-end RAG implementations.\n",
-    "\n",
-    "Here is the question that we'd like you to answer via your RAG system:\n",
-    "\n",
-    "1. What does the company Caravan Health do?"
-   ]
-  }
- ],
- "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/salesforce/README.md b/salesforce/README.md
deleted file mode 100644
index af3e21c..0000000
--- a/salesforce/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Setup
-
-## Prerequisites
-1. Salesforce CLI - https://developer.salesforce.com/docs/atlas.en-us.sfdx_setup.meta/sfdx_setup/sfdx_setup_install_cli.htm 
-2. Salesforce Developer Environment - https://developer.salesforce.com/signup
-4. A Github account - https://docs.github.com/en/get-started/quickstart/set-up-git#setting-up-git
-
-## Interview setup
-1. Log into your Salesforce Developer Environment using the Salesforce CLI:
-    - sfdx force:auth:web:login -r orgUrl
-    - orgUrl should be replaced with the url of your Salesforce Developer Enviornment (i.e. - https://gradientworks5-dev-ed.my.salesforce.com)
-2. [Fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo#prerequisites) and clone the following repo: https://github.com/Gradient-Works/interview
-
-You should be ready to start working through the [interview prompt](https://github.com/Gradient-Works/interview/blob/main/docs/movies_lwc_instructions.md) now.
diff --git a/salesforce/docs/ff_api.md b/salesforce/docs/ff_api.md
deleted file mode 100644
index f777a56..0000000
--- a/salesforce/docs/ff_api.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# FF API
-
-Everything you ever needed to know about the Fast and Furious franchise
-
-URL: https://api.gradientworks.dev/ff
-
-## List Movies
-
-HTTP Method: GET
-
-URL: http://api.gradientworks.dev/ff/movies
-
-### Query params
-
-None
-
-### Results
-
-json document with list of movie info
-
-### Example
-
-```
-$ curl https://api.gradientworks.dev/ff/movies
-{
-  "movies": [
-      {
-          "id": 1,
-          "title": "Fast and Furious"
-      },
-      {
-          "id": 2,
-          "title": "Too Fast Too Furious"
-      },
-      ....
-  ]
-}
-```
-
-## Get movie
-
-HTTP Method: GET
-
-URL: http://api.gradientworks.dev/ff/movies/{ID}
-
-### Query params
-
-None
-
-### Results
-
-json document with specific movie info
-
-### Example
-
-```
-$ curl https://api.gradientworks.dev/ff/movies/1
-{
-  "movie": {
-    "id": 1,
-    "title": "Fast and Furious",
-    ....
-  }
-}
-
-$ curl https://api.gradientworks.dev/ff/movies/2
-{
-  "movie": {
-    "id": 2,
-    "title": "Too Fast Too Furious",
-    ....
-   }
-}
-```
-
-## Get characters in a movie
-
-HTTP Method: GET
-URL: http://api.gradientworks.dev/ff/characters
-
-### Query params
-
-None
-
-### Results
-
-json document with list of actors for a given movie
-
-### Example
-
-```
-$ curl https://api.gradientworks.dev/ff/characters
-{
-  "characters": [
-      {
-          "id": 1,
-          "name": "Dominic Torretto",
-          "movies": [1,3,4,5,6,7,8,9,10]
-          ....
-      },
-      {
-          "id": 2,
-          "name": "Brian O'Conner",
-          "movies": [1,2,4,5,6,7]
-          ....
-      },
-}
-```
-
-## Get character
-
-HTTP Method: GET
-URL: http://api.gradientworks.dev/ff/characters/{ID}
-
-### Query params
-
-None
-
-### Results
-
-json document of character info
-
-### Example
-
-```
-$ curl https://api.gradientworks.dev/ff/characters/1
-{
-  "character": {
-          "id": 1,
-          "name": "Dominic Torretto",
-          "movies": [1,3,4,5,6,7,8,9,10]
-          ....
-  }
-}
-```
\ No newline at end of file
diff --git a/salesforce/docs/movies_lwc_instructions.md b/salesforce/docs/movies_lwc_instructions.md
deleted file mode 100644
index 5c5284d..0000000
--- a/salesforce/docs/movies_lwc_instructions.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Build a Lightning Component to display the Fast and Furious movies
-
-## Prompt
-Using the Fast and Furious API, get the list of movies and display them in a datatable.
-
-Please use the provided Apex classes and lwc for your work and make sure to include tests for your Apex classes.
-
-Information about the API can be found [here](https://github.com/Gradient-Works/interview/blob/main/docs/ff_api.md)
-
-## Some Implementation Details
-1. Please use Apex to interact with the API.
-2. Using `lightning-datatable`, build a component containing the following:
-    - Columns for: Id, Name, Release Date, Opening Revenue
-    - Opening Revenue should be formatted as currency
-    - Release Date should be formatted as MM DD, YYYY
-3. Display the table in a Lightning App page named "Movies"
-
-## Submission Instructions
-Once you're done with the assignment, make sure that all of your work is pulled down from your org and included in your forked repository. It should be organized such that we can run this command to successfuly deploy and test your code:
-
-    sfdx force:mdapi:deploy -u orgUser -d gradient-works-ff/src/main/default -w 10
-
-Let us know once your code has been pushed up to GitHub so we can make sure we have access to it before you leave.
diff --git a/salesforce/gradient-works-ff/.eslintignore b/salesforce/gradient-works-ff/.eslintignore
deleted file mode 100644
index 5f7b681..0000000
--- a/salesforce/gradient-works-ff/.eslintignore
+++ /dev/null
@@ -1,16 +0,0 @@
-**/lwc/**/*.css
-**/lwc/**/*.html
-**/lwc/**/*.json
-**/lwc/**/*.svg
-**/lwc/**/*.xml
-**/aura/**/*.auradoc
-**/aura/**/*.cmp
-**/aura/**/*.css
-**/aura/**/*.design
-**/aura/**/*.evt
-**/aura/**/*.json
-**/aura/**/*.svg
-**/aura/**/*.tokens
-**/aura/**/*.xml
-**/aura/**/*.app
-.sfdx
diff --git a/salesforce/gradient-works-ff/.forceignore b/salesforce/gradient-works-ff/.forceignore
deleted file mode 100755
index 7b5b5a7..0000000
--- a/salesforce/gradient-works-ff/.forceignore
+++ /dev/null
@@ -1,12 +0,0 @@
-# List files or directories below to ignore them when running force:source:push, force:source:pull, and force:source:status
-# More information: https://developer.salesforce.com/docs/atlas.en-us.sfdx_dev.meta/sfdx_dev/sfdx_dev_exclude_source.htm
-#
-
-package.xml
-
-# LWC configuration files
-**/jsconfig.json
-**/.eslintrc.json
-
-# LWC Jest
-**/__tests__/**
\ No newline at end of file
diff --git a/salesforce/gradient-works-ff/.gitignore b/salesforce/gradient-works-ff/.gitignore
deleted file mode 100644
index f891339..0000000
--- a/salesforce/gradient-works-ff/.gitignore
+++ /dev/null
@@ -1,40 +0,0 @@
-# This file is used for Git repositories to specify intentionally untracked files that Git should ignore. 
-# If you are not using git, you can delete this file. For more information see: https://git-scm.com/docs/gitignore
-# For useful gitignore templates see: https://github.com/github/gitignore
-
-# Salesforce cache
-.sf/
-.sfdx/
-.localdevserver/
-deploy-options.json
-
-# LWC VSCode autocomplete
-**/lwc/jsconfig.json
-
-# LWC Jest coverage reports
-coverage/
-
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# Dependency directories
-node_modules/
-
-# Eslint cache
-.eslintcache
-
-# MacOS system files
-.DS_Store
-
-# Windows system files
-Thumbs.db
-ehthumbs.db
-[Dd]esktop.ini
-$RECYCLE.BIN/
-
-# Local environment variables
-.env
\ No newline at end of file
diff --git a/salesforce/gradient-works-ff/.husky/pre-commit b/salesforce/gradient-works-ff/.husky/pre-commit
deleted file mode 100755
index feac116..0000000
--- a/salesforce/gradient-works-ff/.husky/pre-commit
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/sh
-. "$(dirname "$0")/_/husky.sh"
-
-npm run precommit
\ No newline at end of file
diff --git a/salesforce/gradient-works-ff/.prettierignore b/salesforce/gradient-works-ff/.prettierignore
deleted file mode 100755
index f3720b2..0000000
--- a/salesforce/gradient-works-ff/.prettierignore
+++ /dev/null
@@ -1,10 +0,0 @@
-# List files or directories below to ignore them when running prettier
-# More information: https://prettier.io/docs/en/ignore.html
-#
-
-**/staticresources/**
-.localdevserver
-.sfdx
-.vscode
-
-coverage/
\ No newline at end of file
diff --git a/salesforce/gradient-works-ff/.prettierrc b/salesforce/gradient-works-ff/.prettierrc
deleted file mode 100755
index 15683b6..0000000
--- a/salesforce/gradient-works-ff/.prettierrc
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "trailingComma": "none",
-  "overrides": [
-    {
-      "files": "**/lwc/**/*.html",
-      "options": { "parser": "lwc" }
-    },
-    {
-      "files": "*.{cmp,page,component}",
-      "options": { "parser": "html" }
-    }
-  ]
-}
diff --git a/salesforce/gradient-works-ff/.vscode/extensions.json b/salesforce/gradient-works-ff/.vscode/extensions.json
deleted file mode 100644
index 7e6cb10..0000000
--- a/salesforce/gradient-works-ff/.vscode/extensions.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "recommendations": [
-    "salesforce.salesforcedx-vscode",
-    "redhat.vscode-xml",
-    "dbaeumer.vscode-eslint",
-    "esbenp.prettier-vscode",
-    "financialforce.lana"
-  ]
-}
diff --git a/salesforce/gradient-works-ff/.vscode/launch.json b/salesforce/gradient-works-ff/.vscode/launch.json
deleted file mode 100644
index e07e391..0000000
--- a/salesforce/gradient-works-ff/.vscode/launch.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "name": "Launch Apex Replay Debugger",
-      "type": "apex-replay",
-      "request": "launch",
-      "logFile": "${command:AskForLogFileName}",
-      "stopOnEntry": true,
-      "trace": true
-    }
-  ]
-}
diff --git a/salesforce/gradient-works-ff/.vscode/settings.json b/salesforce/gradient-works-ff/.vscode/settings.json
deleted file mode 100644
index 76decfb..0000000
--- a/salesforce/gradient-works-ff/.vscode/settings.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "search.exclude": {
-    "**/node_modules": true,
-    "**/bower_components": true,
-    "**/.sfdx": true
-  }
-}
diff --git a/salesforce/gradient-works-ff/README.md b/salesforce/gradient-works-ff/README.md
deleted file mode 100644
index afcda4a..0000000
--- a/salesforce/gradient-works-ff/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Salesforce DX Project: Next Steps
-
-Now that you’ve created a Salesforce DX project, what’s next? Here are some documentation resources to get you started.
-
-## How Do You Plan to Deploy Your Changes?
-
-Do you want to deploy a set of changes, or create a self-contained application? Choose a [development model](https://developer.salesforce.com/tools/vscode/en/user-guide/development-models).
-
-## Configure Your Salesforce DX Project
-
-The `sfdx-project.json` file contains useful configuration information for your project. See [Salesforce DX Project Configuration](https://developer.salesforce.com/docs/atlas.en-us.sfdx_dev.meta/sfdx_dev/sfdx_dev_ws_config.htm) in the _Salesforce DX Developer Guide_ for details about this file.
-
-## Read All About It
-
-- [Salesforce Extensions Documentation](https://developer.salesforce.com/tools/vscode/)
-- [Salesforce CLI Setup Guide](https://developer.salesforce.com/docs/atlas.en-us.sfdx_setup.meta/sfdx_setup/sfdx_setup_intro.htm)
-- [Salesforce DX Developer Guide](https://developer.salesforce.com/docs/atlas.en-us.sfdx_dev.meta/sfdx_dev/sfdx_dev_intro.htm)
-- [Salesforce CLI Command Reference](https://developer.salesforce.com/docs/atlas.en-us.sfdx_cli_reference.meta/sfdx_cli_reference/cli_reference.htm)
diff --git a/salesforce/gradient-works-ff/config/project-scratch-def.json b/salesforce/gradient-works-ff/config/project-scratch-def.json
deleted file mode 100644
index ee4158e..0000000
--- a/salesforce/gradient-works-ff/config/project-scratch-def.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "orgName": "chloecoon company",
-  "edition": "Developer",
-  "features": ["EnableSetPasswordInApi"],
-  "settings": {
-    "lightningExperienceSettings": {
-      "enableS1DesktopEnabled": true
-    },
-    "mobileSettings": {
-      "enableS1EncryptedStoragePref2": false
-    }
-  }
-}
diff --git a/salesforce/gradient-works-ff/jest.config.js b/salesforce/gradient-works-ff/jest.config.js
deleted file mode 100644
index f5a9fed..0000000
--- a/salesforce/gradient-works-ff/jest.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-const { jestConfig } = require('@salesforce/sfdx-lwc-jest/config');
-
-module.exports = {
-    ...jestConfig,
-    modulePathIgnorePatterns: ['<rootDir>/.localdevserver']
-};
diff --git a/salesforce/gradient-works-ff/package.json b/salesforce/gradient-works-ff/package.json
deleted file mode 100644
index c2df91a..0000000
--- a/salesforce/gradient-works-ff/package.json
+++ /dev/null
@@ -1,41 +0,0 @@
-{
-  "name": "salesforce-app",
-  "private": true,
-  "version": "1.0.0",
-  "description": "Salesforce App",
-  "scripts": {
-    "lint": "eslint **/{aura,lwc}/**",
-    "test": "npm run test:unit",
-    "test:unit": "sfdx-lwc-jest",
-    "test:unit:watch": "sfdx-lwc-jest --watch",
-    "test:unit:debug": "sfdx-lwc-jest --debug",
-    "test:unit:coverage": "sfdx-lwc-jest --coverage",
-    "prettier": "prettier --write \"**/*.{cls,cmp,component,css,html,js,json,md,page,trigger,xml,yaml,yml}\"",
-    "prettier:verify": "prettier --list-different \"**/*.{cls,cmp,component,css,html,js,json,md,page,trigger,xml,yaml,yml}\"",
-    "postinstall": "husky install",
-    "precommit": "lint-staged"
-  },
-  "devDependencies": {
-    "@lwc/eslint-plugin-lwc": "^1.0.1",
-    "@prettier/plugin-xml": "^0.13.1",
-    "@salesforce/eslint-config-lwc": "^2.0.0",
-    "@salesforce/eslint-plugin-aura": "^2.0.0",
-    "@salesforce/eslint-plugin-lightning": "^0.1.1",
-    "@salesforce/sfdx-lwc-jest": "^0.13.0",
-    "eslint": "^7.29.0",
-    "eslint-plugin-import": "^2.23.4",
-    "eslint-plugin-jest": "^24.3.6",
-    "husky": "^7.0.0",
-    "lint-staged": "^11.0.0",
-    "prettier": "^2.3.2",
-    "prettier-plugin-apex": "^1.10.0"
-  },
-  "lint-staged": {
-    "**/*.{cls,cmp,component,css,html,js,json,md,page,trigger,xml,yaml,yml}": [
-      "prettier --write"
-    ],
-    "**/{aura,lwc}/**": [
-      "eslint"
-    ]
-  }
-}
diff --git a/salesforce/gradient-works-ff/scripts/apex/hello.apex b/salesforce/gradient-works-ff/scripts/apex/hello.apex
deleted file mode 100644
index 1fba732..0000000
--- a/salesforce/gradient-works-ff/scripts/apex/hello.apex
+++ /dev/null
@@ -1,10 +0,0 @@
-// Use .apex files to store anonymous Apex.
-// You can execute anonymous Apex in VS Code by selecting the
-//     apex text and running the command:
-//     SFDX: Execute Anonymous Apex with Currently Selected Text
-// You can also execute the entire file by running the command:
-//     SFDX: Execute Anonymous Apex with Editor Contents
-
-string tempvar = 'Enter_your_name_here';
-System.debug('Hello World!');
-System.debug('My name is ' + tempvar);
\ No newline at end of file
diff --git a/salesforce/gradient-works-ff/scripts/soql/account.soql b/salesforce/gradient-works-ff/scripts/soql/account.soql
deleted file mode 100644
index 10d4b9c..0000000
--- a/salesforce/gradient-works-ff/scripts/soql/account.soql
+++ /dev/null
@@ -1,6 +0,0 @@
-// Use .soql files to store SOQL queries.
-// You can execute queries in VS Code by selecting the
-//     query text and running the command:
-//     SFDX: Execute SOQL Query with Currently Selected Text
-
-SELECT Id, Name FROM Account
diff --git a/salesforce/gradient-works-ff/sfdx-project.json b/salesforce/gradient-works-ff/sfdx-project.json
deleted file mode 100644
index 37979e3..0000000
--- a/salesforce/gradient-works-ff/sfdx-project.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "packageDirectories": [
-    {
-      "path": "src",
-      "default": true
-    }
-  ],
-  "name": "gradient-works-ff",
-  "namespace": "",
-  "sfdcLoginUrl": "https://login.salesforce.com",
-  "sourceApiVersion": "52.0"
-}
diff --git a/salesforce/gradient-works-ff/src/main/default/package.xml b/salesforce/gradient-works-ff/src/main/default/package.xml
deleted file mode 100644
index b4a54dd..0000000
--- a/salesforce/gradient-works-ff/src/main/default/package.xml
+++ /dev/null
@@ -1,36 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Package xmlns="http://soap.sforce.com/2006/04/metadata">
-    <types>
-        <members>*</members>
-        <name>ApexClass</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>ApexComponent</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>ApexPage</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>ApexTestSuite</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>ApexTrigger</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>AuraDefinitionBundle</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>LightningComponentBundle</name>
-    </types>
-    <types>
-        <members>*</members>
-        <name>StaticResource</name>
-    </types>
-    <version>52.0</version>
-</Package>
\ No newline at end of file