local_rag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/coderex7/nano-rag/blob/main/local_rag.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "# Create and run a local RAG pipeline from scratch\n",
    "\n",
    "The goal of this notebook is to build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it run on a local GPU.\n",
    "\n",
    "Specifically, we'd like to be able to open a PDF file, ask questions (queries) of it and have them answered by a Large Language Model (LLM).\n",
    "\n",
    "There are frameworks that replicate this kind of workflow, including [LlamaIndex](https://www.llamaindex.ai/) and [LangChain](https://www.langchain.com/), however, the goal of building from scratch is to be able to inspect and customize all the parts.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "## Requirements and setup\n",
    "\n",
    "* Local NVIDIA GPU (I used a NVIDIA RTX 4070 on a Windows 11 machine) or Google Colab with access to a GPU.\n",
    "* Environment setup (see [setup details on GitHub](https://github.com/CodeRex7/nano-rag/?tab=readme-ov-file#setup)).\n",
    "* Data source (for example, a PDF). \n",
    "* Internet connection (to download the models, but once you have them, it'll run offline)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Google Colab installs (if running in Google Colab)\n",
    "import os\n",
    "\n",
    "if \"COLAB_GPU\" in os.environ:\n",
    "    print(\"[INFO] Running in Google Colab, installing requirements.\")\n",
    "    !pip install -U torch # requires torch 2.1.1+ (for efficient sdpa implementation)\n",
    "    !pip install PyMuPDF # for reading PDFs with Python\n",
    "    !pip install tqdm # for progress bars\n",
    "    !pip install sentence-transformers # for embedding models\n",
    "    !pip install accelerate # for quantization model loading\n",
    "    !pip install bitsandbytes # for quantizing models (less storage space)\n",
    "    !pip install flash-attn --no-build-isolation # for faster attention mechanism = faster LLM inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Document/Text Processing and Embedding Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import PDF Document "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File assets/human-nutrition-text.pdf exists.\n"
     ]
    }
   ],
   "source": [
    "# Download PDF file\n",
    "import os\n",
    "import requests\n",
    "\n",
    "# Get PDF document\n",
    "pdf_path = \"assets/human-nutrition-text.pdf\"\n",
    "\n",
    "# Download PDF if it doesn't already exist\n",
    "if not os.path.exists(pdf_path):\n",
    "  print(\"File doesn't exist, downloading...\")\n",
    "\n",
    "  # The URL of the PDF you want to download\n",
    "  url = \"https://pressbooks.oer.hawaii.edu/humannutrition2/open/download?type=pdf\"\n",
    "\n",
    "  # The local filename to save the downloaded file\n",
    "  filename = pdf_path\n",
    "\n",
    "  # Send a GET request to the URL\n",
    "  response = requests.get(url)\n",
    "\n",
    "  # Check if the request was successful\n",
    "  if response.status_code == 200:\n",
    "      # Open a file in binary write mode and save the content to it\n",
    "      with open(filename, \"wb\") as file:\n",
    "          file.write(response.content)\n",
    "      print(f\"The file has been downloaded and saved as {filename}\")\n",
    "  else:\n",
    "      print(f\"Failed to download the file. Status code: {response.status_code}\")\n",
    "else:\n",
    "  print(f\"File {pdf_path} exists.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "0it [00:00, ?it/s]"
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      "text/plain": [
       "[{'page_number': -41,\n",
       "  'page_char_count': 29,\n",
       "  'page_word_count': 4,\n",
       "  'page_sentence_count_raw': 1,\n",
       "  'page_token_count': 7.25,\n",
       "  'text': 'Human Nutrition: 2020 Edition'},\n",
       " {'page_number': -40,\n",
       "  'page_char_count': 0,\n",
       "  'page_word_count': 1,\n",
       "  'page_sentence_count_raw': 1,\n",
       "  'page_token_count': 0.0,\n",
       "  'text': ''}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Requires !pip install PyMuPDF, see: https://github.com/pymupdf/pymupdf\n",
    "import fitz # (pymupdf, found this is better than pypdf for our use case, note: licence is AGPL-3.0, keep that in mind if you want to use any code commercially)\n",
    "from tqdm.auto import tqdm # for progress bars, requires !pip install tqdm \n",
    "\n",
    "def text_formatter(text: str) -> str:\n",
    "    \"\"\"Performs minor formatting on text.\"\"\"\n",
    "    cleaned_text = text.replace(\"\\n\", \" \").strip() # note: this might be different for each doc (best to experiment)\n",
    "\n",
    "    # Other potential text formatting functions can go here\n",
    "    return cleaned_text\n",
    "\n",
    "# Open PDF and get lines/pages\n",
    "# Note: this only focuses on text, rather than images/figures etc\n",
    "def open_and_read_pdf(pdf_path: str) -> list[dict]:\n",
    "    \"\"\"\n",
    "    Opens a PDF file, reads its text content page by page, and collects statistics.\n",
    "\n",
    "    Parameters:\n",
    "        pdf_path (str): The file path to the PDF document to be opened and read.\n",
    "\n",
    "    Returns:\n",
    "        list[dict]: A list of dictionaries, each containing the page number\n",
    "        (adjusted), character count, word count, sentence count, token count, and the extracted text\n",
    "        for each page.\n",
    "    \"\"\"\n",
    "    doc = fitz.open(pdf_path)  # open a document\n",
    "    pages_and_texts = []\n",
    "    for page_number, page in tqdm(enumerate(doc)):  # iterate the document pages\n",
    "        text = page.get_text()  # get plain text encoded as UTF-8\n",
    "        text = text_formatter(text)\n",
    "        pages_and_texts.append({\"page_number\": page_number - 41,  # adjust page numbers since our PDF starts on page 42\n",
    "                                \"page_char_count\": len(text),\n",
    "                                \"page_word_count\": len(text.split(\" \")),\n",
    "                                \"page_sentence_count_raw\": len(text.split(\". \")),\n",
    "                                \"page_token_count\": len(text) / 4,  # 1 token = ~4 chars, see: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them\n",
    "                                \"text\": text})\n",
    "    return pages_and_texts\n",
    "\n",
    "pages_and_texts = open_and_read_pdf(pdf_path=pdf_path)\n",
    "pages_and_texts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 289,\n",
       "  'page_char_count': 52,\n",
       "  'page_word_count': 14,\n",
       "  'page_sentence_count_raw': 3,\n",
       "  'page_token_count': 13.0,\n",
       "  'text': 'PART V  CHAPTER 5. LIPIDS  Chapter 5. Lipids  |  289'},\n",
       " {'page_number': 443,\n",
       "  'page_char_count': 1822,\n",
       "  'page_word_count': 309,\n",
       "  'page_sentence_count_raw': 17,\n",
       "  'page_token_count': 455.5,\n",
       "  'text': 'Effects of Alcohol Abuse on the Brain  A small amount (up to 10%) of the liver acetaldehyde may  accumulate inside the liver cells. As more alcohol is ingested, this  stimulates the production of acetaldehyde by both the alcohol  dehydrogenase and MEOS systems. As the levels of acetaldehyde  increase inside the liver cells with heavy consumption of alcohol,  some of the acetaldehyde diffuse into the blood circulation. In  circulation, high levels of acetaldehyde cause nausea and vomiting.  Vomiting causes more body dehydration and loss of electrolytes.  If the dehydration becomes severe enough, this can impair brain  function and a person may lose consciousness.  Alcohol can adversely affect nearly every area of the brain. When  BAC rises, the central nervous system is depressed. Alcohol disrupts  the way nerve cells communicate with each other by interfering  with receptors on certain cells. The immediate impact of alcohol  on the brain can be seen in the awkwardly displayed symptoms  of confusion, blurred vision, slurred speech, and other signs of  intoxication. These symptoms will go away once drinking stops,  but abusive alcohol consumption over time can lead to long-lasting  damage to the brain and nervous system. This is because alcohol  and its metabolic byproducts kill brain cells.  Effects of Excessive Alcohol on the Liver  Alcohol stimulates the release of epinephrine from the kidneys.  Epinephrine binds to receptors in the liver cells to stimulate the  release of glucagon from the pancreas. Glucagon and epinephrine  stimulate glycogenolysis in the liver cells. Epinephrine also  stimulates the breakdown of triglycerides and glycerol into free  fatty acids  in adipose tissue and are released into the bloodstream  and travel to the liver.  Health Consequences of Alcohol Abuse  |  443'},\n",
       " {'page_number': 1018,\n",
       "  'page_char_count': 990,\n",
       "  'page_word_count': 162,\n",
       "  'page_sentence_count_raw': 7,\n",
       "  'page_token_count': 247.5,\n",
       "  'text': 'transforming raw ingredients into packaged food, from fresh-baked  goods to frozen dinners. Although there are numerous benefits  to both, preservation and processing also pose some concerns, in  terms of both nutrition and sustainability.  Learning Activities  Technology Note: The second edition of the Human  Nutrition Open Educational Resource (OER) textbook  features interactive learning activities.  These activities are  available in the web-based textbook and not available in the  downloadable versions (EPUB, Digital PDF, Print_PDF, or  Open Document).  Learning activities may be used across various mobile  devices, however, for the best user experience it is strongly  recommended that users complete these activities using a  desktop or laptop computer and in Google Chrome.    An interactive or media element has been  excluded from this version of the text. You can  view it online here:  http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=532    1018  |  The Food System'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random.sample(pages_and_texts, k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get some stats on the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>page_number</th>\n",
       "      <th>page_char_count</th>\n",
       "      <th>page_word_count</th>\n",
       "      <th>page_sentence_count_raw</th>\n",
       "      <th>page_token_count</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-41</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25</td>\n",
       "      <td>Human Nutrition: 2020 Edition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-40</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>2</th>\n",
       "      <td>-39</td>\n",
       "      <td>320</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Human Nutrition: 2020  Edition  UNIVERSITY OF ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-38</td>\n",
       "      <td>212</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>53.00</td>\n",
       "      <td>Human Nutrition: 2020 Edition by University of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-37</td>\n",
       "      <td>797</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>199.25</td>\n",
       "      <td>Contents  Preface  University of Hawai‘i at Mā...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   page_number  page_char_count  page_word_count  page_sentence_count_raw  \\\n",
       "0          -41               29                4                        1   \n",
       "1          -40                0                1                        1   \n",
       "2          -39              320               54                        1   \n",
       "3          -38              212               32                        1   \n",
       "4          -37              797              147                        3   \n",
       "\n",
       "   page_token_count                                               text  \n",
       "0              7.25                      Human Nutrition: 2020 Edition  \n",
       "1              0.00                                                     \n",
       "2             80.00  Human Nutrition: 2020  Edition  UNIVERSITY OF ...  \n",
       "3             53.00  Human Nutrition: 2020 Edition by University of...  \n",
       "4            199.25  Contents  Preface  University of Hawai‘i at Mā...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(pages_and_texts)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>page_number</th>\n",
       "      <th>page_char_count</th>\n",
       "      <th>page_word_count</th>\n",
       "      <th>page_sentence_count_raw</th>\n",
       "      <th>page_token_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1148.00</td>\n",
       "      <td>199.50</td>\n",
       "      <td>10.52</td>\n",
       "      <td>287.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>348.86</td>\n",
       "      <td>560.38</td>\n",
       "      <td>95.83</td>\n",
       "      <td>6.55</td>\n",
       "      <td>140.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-41.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>260.75</td>\n",
       "      <td>762.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>190.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1231.50</td>\n",
       "      <td>216.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>307.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>864.25</td>\n",
       "      <td>1603.50</td>\n",
       "      <td>272.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>400.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1166.00</td>\n",
       "      <td>2308.00</td>\n",
       "      <td>430.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>577.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       page_number  page_char_count  page_word_count  page_sentence_count_raw  \\\n",
       "count      1208.00          1208.00          1208.00                  1208.00   \n",
       "mean        562.50          1148.00           199.50                    10.52   \n",
       "std         348.86           560.38            95.83                     6.55   \n",
       "min         -41.00             0.00             1.00                     1.00   \n",
       "25%         260.75           762.00           134.00                     5.00   \n",
       "50%         562.50          1231.50           216.00                    10.00   \n",
       "75%         864.25          1603.50           272.00                    15.00   \n",
       "max        1166.00          2308.00           430.00                    39.00   \n",
       "\n",
       "       page_token_count  \n",
       "count           1208.00  \n",
       "mean             287.00  \n",
       "std              140.10  \n",
       "min                0.00  \n",
       "25%              190.50  \n",
       "50%              307.88  \n",
       "75%              400.88  \n",
       "max              577.00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get stats\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further text processing (splitting pages into sentences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ideal way of processing text before embedding it is still an active area of research.\n",
    "A simple method I've found helpful is to break the text into chunks of sentences.\n",
    "\n",
    "Some options for splitting text into sentences:\n",
    "\n",
    "1. Split into sentences with simple rules (e.g. split on \". \" with `text = text.split(\". \")`, like we did above).\n",
    "2. Split into sentences with a natural language processing (NLP) library such as [spaCy](https://spacy.io/) or [nltk](https://www.nltk.org/).\n",
    "\n",
    "Why split into sentences?\n",
    "\n",
    "* Easier to handle than larger pages of text (especially if pages are densely filled with text).\n",
    "* Can get specific and find out which group of sentences were used to help within a RAG pipeline.\n",
    "\n",
    "> **Resource:** See [spaCy install instructions](https://spacy.io/usage). \n",
    "\n",
    "Let's use spaCy to break our text into sentences since it's likely a bit more robust than just using `text.split(\". \")`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[This is a sentence., This another sentence.]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spacy.lang.en import English # see https://spacy.io/usage for install instructions\n",
    "\n",
    "nlp = English()\n",
    "\n",
    "# Add a sentencizer pipeline, see https://spacy.io/api/sentencizer/ \n",
    "nlp.add_pipe(\"sentencizer\")\n",
    "\n",
    "# Create a document instance as an example\n",
    "doc = nlp(\"This is a sentence. This another sentence.\")\n",
    "assert len(list(doc.sents)) == 2\n",
    "\n",
    "# Access the sentences of the document\n",
    "list(doc.sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/1208 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in tqdm(pages_and_texts):\n",
    "    item[\"sentences\"] = list(nlp(item[\"text\"]).sents)\n",
    "    \n",
    "    # Make sure all sentences are strings\n",
    "    item[\"sentences\"] = [str(sentence) for sentence in item[\"sentences\"]]\n",
    "    \n",
    "    # Count the sentences \n",
    "    item[\"page_sentence_count_spacy\"] = len(item[\"sentences\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 81,\n",
       "  'page_char_count': 86,\n",
       "  'page_word_count': 12,\n",
       "  'page_sentence_count_raw': 1,\n",
       "  'page_token_count': 21.5,\n",
       "  'text': 'http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=84    The Digestive System  |  81',\n",
       "  'sentences': ['http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=84    The Digestive System  |  81'],\n",
       "  'page_sentence_count_spacy': 1}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect an example\n",
    "random.sample(pages_and_texts, k=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>page_number</th>\n",
       "      <th>page_char_count</th>\n",
       "      <th>page_word_count</th>\n",
       "      <th>page_sentence_count_raw</th>\n",
       "      <th>page_token_count</th>\n",
       "      <th>page_sentence_count_spacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1148.00</td>\n",
       "      <td>199.50</td>\n",
       "      <td>10.52</td>\n",
       "      <td>287.00</td>\n",
       "      <td>10.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>348.86</td>\n",
       "      <td>560.38</td>\n",
       "      <td>95.83</td>\n",
       "      <td>6.55</td>\n",
       "      <td>140.10</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-41.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>260.75</td>\n",
       "      <td>762.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>190.50</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1231.50</td>\n",
       "      <td>216.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>307.88</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>864.25</td>\n",
       "      <td>1603.50</td>\n",
       "      <td>272.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>400.88</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1166.00</td>\n",
       "      <td>2308.00</td>\n",
       "      <td>430.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>577.00</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       page_number  page_char_count  page_word_count  page_sentence_count_raw  \\\n",
       "count      1208.00          1208.00          1208.00                  1208.00   \n",
       "mean        562.50          1148.00           199.50                    10.52   \n",
       "std         348.86           560.38            95.83                     6.55   \n",
       "min         -41.00             0.00             1.00                     1.00   \n",
       "25%         260.75           762.00           134.00                     5.00   \n",
       "50%         562.50          1231.50           216.00                    10.00   \n",
       "75%         864.25          1603.50           272.00                    15.00   \n",
       "max        1166.00          2308.00           430.00                    39.00   \n",
       "\n",
       "       page_token_count  page_sentence_count_spacy  \n",
       "count           1208.00                    1208.00  \n",
       "mean             287.00                      10.32  \n",
       "std              140.10                       6.30  \n",
       "min                0.00                       0.00  \n",
       "25%              190.50                       5.00  \n",
       "50%              307.88                      10.00  \n",
       "75%              400.88                      15.00  \n",
       "max              577.00                      28.00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(pages_and_texts)\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunking our sentences together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83457a6a6b0740e4baa162ee3191a759",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1208 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define split size to turn groups of sentences into chunks\n",
    "num_sentence_chunk_size = 10 \n",
    "\n",
    "# Create a function that recursively splits a list into desired sizes\n",
    "def split_list(input_list: list, \n",
    "               slice_size: int) -> list[list[str]]:\n",
    "    \"\"\"\n",
    "    Splits the input_list into sublists of size slice_size (or as close as possible).\n",
    "\n",
    "    For example, a list of 17 sentences would be split into two lists of [[10], [7]]\n",
    "    \"\"\"\n",
    "    return [input_list[i:i + slice_size] for i in range(0, len(input_list), slice_size)]\n",
    "\n",
    "# Loop through pages and texts and split sentences into chunks\n",
    "for item in tqdm(pages_and_texts):\n",
    "    item[\"sentence_chunks\"] = split_list(input_list=item[\"sentences\"],\n",
    "                                         slice_size=num_sentence_chunk_size)\n",
    "    item[\"num_chunks\"] = len(item[\"sentence_chunks\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 549,\n",
       "  'page_char_count': 753,\n",
       "  'page_word_count': 124,\n",
       "  'page_sentence_count_raw': 5,\n",
       "  'page_token_count': 188.25,\n",
       "  'text': 'Learning Activities  Technology Note: The second edition of the Human  Nutrition Open Educational Resource (OER) textbook  features interactive learning activities.  These activities are  available in the web-based textbook and not available in the  downloadable versions (EPUB, Digital PDF, Print_PDF, or  Open Document).  Learning activities may be used across various mobile  devices, however, for the best user experience it is strongly  recommended that users complete these activities using a  desktop or laptop computer and in Google Chrome.    An interactive or media element has been  excluded from this version of the text. You can  view it online here:  http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=331    Fat-Soluble Vitamins  |  549',\n",
       "  'sentences': ['Learning Activities  Technology Note: The second edition of the Human  Nutrition Open Educational Resource (OER) textbook  features interactive learning activities.',\n",
       "   ' These activities are  available in the web-based textbook and not available in the  downloadable versions (EPUB, Digital PDF, Print_PDF, or  Open Document).',\n",
       "   ' Learning activities may be used across various mobile  devices, however, for the best user experience it is strongly  recommended that users complete these activities using a  desktop or laptop computer and in Google Chrome.',\n",
       "   '   An interactive or media element has been  excluded from this version of the text.',\n",
       "   'You can  view it online here:  http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=331    Fat-Soluble Vitamins  |  549'],\n",
       "  'page_sentence_count_spacy': 5,\n",
       "  'sentence_chunks': [['Learning Activities  Technology Note: The second edition of the Human  Nutrition Open Educational Resource (OER) textbook  features interactive learning activities.',\n",
       "    ' These activities are  available in the web-based textbook and not available in the  downloadable versions (EPUB, Digital PDF, Print_PDF, or  Open Document).',\n",
       "    ' Learning activities may be used across various mobile  devices, however, for the best user experience it is strongly  recommended that users complete these activities using a  desktop or laptop computer and in Google Chrome.',\n",
       "    '   An interactive or media element has been  excluded from this version of the text.',\n",
       "    'You can  view it online here:  http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=331    Fat-Soluble Vitamins  |  549']],\n",
       "  'num_chunks': 1}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample an example from the group (note: many samples have only 1 chunk as they have <=10 sentences total)\n",
    "random.sample(pages_and_texts, k=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>page_number</th>\n",
       "      <th>page_char_count</th>\n",
       "      <th>page_word_count</th>\n",
       "      <th>page_sentence_count_raw</th>\n",
       "      <th>page_token_count</th>\n",
       "      <th>page_sentence_count_spacy</th>\n",
       "      <th>num_chunks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "      <td>1208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1148.00</td>\n",
       "      <td>199.50</td>\n",
       "      <td>10.52</td>\n",
       "      <td>287.00</td>\n",
       "      <td>10.32</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>348.86</td>\n",
       "      <td>560.38</td>\n",
       "      <td>95.83</td>\n",
       "      <td>6.55</td>\n",
       "      <td>140.10</td>\n",
       "      <td>6.30</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-41.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>260.75</td>\n",
       "      <td>762.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>190.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>562.50</td>\n",
       "      <td>1231.50</td>\n",
       "      <td>216.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>307.88</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>864.25</td>\n",
       "      <td>1603.50</td>\n",
       "      <td>272.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>400.88</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1166.00</td>\n",
       "      <td>2308.00</td>\n",
       "      <td>430.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>577.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       page_number  page_char_count  page_word_count  page_sentence_count_raw  \\\n",
       "count      1208.00          1208.00          1208.00                  1208.00   \n",
       "mean        562.50          1148.00           199.50                    10.52   \n",
       "std         348.86           560.38            95.83                     6.55   \n",
       "min         -41.00             0.00             1.00                     1.00   \n",
       "25%         260.75           762.00           134.00                     5.00   \n",
       "50%         562.50          1231.50           216.00                    10.00   \n",
       "75%         864.25          1603.50           272.00                    15.00   \n",
       "max        1166.00          2308.00           430.00                    39.00   \n",
       "\n",
       "       page_token_count  page_sentence_count_spacy  num_chunks  \n",
       "count           1208.00                    1208.00     1208.00  \n",
       "mean             287.00                      10.32        1.53  \n",
       "std              140.10                       6.30        0.64  \n",
       "min                0.00                       0.00        0.00  \n",
       "25%              190.50                       5.00        1.00  \n",
       "50%              307.88                      10.00        1.00  \n",
       "75%              400.88                      15.00        2.00  \n",
       "max              577.00                      28.00        3.00  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame to get stats\n",
    "df = pd.DataFrame(pages_and_texts)\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting each chunk into its own item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a step to break down our list of sentences/text into smaller chunks.\n",
    "\n",
    "As you might've guessed, this process is referred to as **chunking**.\n",
    "\n",
    "Why do we do this?\n",
    "\n",
    "1. Easier to manage similar sized chunks of text.\n",
    "2. Don't overload the embedding models capacity for tokens (e.g. if an embedding model has a capacity of 384 tokens, there could be information loss if you try to embed a sequence of 400+ tokens).\n",
    "3. Our LLM context window (the amount of tokens an LLM can take in) may be limited and requires compute power so we want to make sure we're using it as well as possible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5df2313ae0c24c068b7084383d064c6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1208 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1843"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Split each chunk into its own item\n",
    "pages_and_chunks = []\n",
    "for item in tqdm(pages_and_texts):\n",
    "    for sentence_chunk in item[\"sentence_chunks\"]:\n",
    "        chunk_dict = {}\n",
    "        chunk_dict[\"page_number\"] = item[\"page_number\"]\n",
    "        \n",
    "        # Join the sentences together into a paragraph-like structure, aka a chunk (so they are a single string)\n",
    "        joined_sentence_chunk = \"\".join(sentence_chunk).replace(\"  \", \" \").strip()\n",
    "        joined_sentence_chunk = re.sub(r'\\.([A-Z])', r'. \\1', joined_sentence_chunk) # \".A\" -> \". A\" for any full-stop/capital letter combo \n",
    "        chunk_dict[\"sentence_chunk\"] = joined_sentence_chunk\n",
    "\n",
    "        # Get stats about the chunk\n",
    "        chunk_dict[\"chunk_char_count\"] = len(joined_sentence_chunk)\n",
    "        chunk_dict[\"chunk_word_count\"] = len([word for word in joined_sentence_chunk.split(\" \")])\n",
    "        chunk_dict[\"chunk_token_count\"] = len(joined_sentence_chunk) / 4 # 1 token = ~4 characters\n",
    "        \n",
    "        pages_and_chunks.append(chunk_dict)\n",
    "\n",
    "# How many chunks do we have?\n",
    "len(pages_and_chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 282,\n",
       "  'sentence_chunk': 'Sweeteners with Trade Name Calories Source/Origin Consumer Recommendations Controversial Issues Aspartame • NutraSweet • Equal 4 kcal/g Composed of two amino acids (phenylalanine + aspartic acid) + methanol. Two hundred times sweeter than sucrose. FDA set maximum Acceptable Daily Intakes (ADI):50 mg/kg body weight = 16 12 oz. diet soft drinks for adults. *Cannot be used in products requiring cooking. People with PKU should not consume aspartame. Children have potential to reach ADI if consuming many beverages, desserts, frozen desserts, and gums containing aspartame routinely. Saccharin • Sweet ‘n’ Low 0 kcal/g Discovered in 1878. The basic substance is benzoic sulfinide. Three hundred times sweeter than sucrose.',\n",
       "  'chunk_char_count': 722,\n",
       "  'chunk_word_count': 107,\n",
       "  'chunk_token_count': 180.5}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View a random sample\n",
    "random.sample(pages_and_chunks, k=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>page_number</th>\n",
       "      <th>chunk_char_count</th>\n",
       "      <th>chunk_word_count</th>\n",
       "      <th>chunk_token_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1843.00</td>\n",
       "      <td>1843.00</td>\n",
       "      <td>1843.00</td>\n",
       "      <td>1843.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>583.38</td>\n",
       "      <td>734.10</td>\n",
       "      <td>112.74</td>\n",
       "      <td>183.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>347.79</td>\n",
       "      <td>447.51</td>\n",
       "      <td>71.24</td>\n",
       "      <td>111.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-41.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>280.50</td>\n",
       "      <td>315.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>78.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>586.00</td>\n",
       "      <td>745.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>186.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>890.00</td>\n",
       "      <td>1118.00</td>\n",
       "      <td>173.00</td>\n",
       "      <td>279.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1166.00</td>\n",
       "      <td>1830.00</td>\n",
       "      <td>297.00</td>\n",
       "      <td>457.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       page_number  chunk_char_count  chunk_word_count  chunk_token_count\n",
       "count      1843.00           1843.00           1843.00            1843.00\n",
       "mean        583.38            734.10            112.74             183.52\n",
       "std         347.79            447.51             71.24             111.88\n",
       "min         -41.00             12.00              3.00               3.00\n",
       "25%         280.50            315.00             45.00              78.75\n",
       "50%         586.00            745.00            115.00             186.25\n",
       "75%         890.00           1118.00            173.00             279.50\n",
       "max        1166.00           1830.00            297.00             457.50"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get stats about our chunks\n",
    "df = pd.DataFrame(pages_and_chunks)\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk token count: 27.5 | Text: Figure 15.1 reused “Silohuette of Three People” by photo- nic.co.uk nic / Unsplash License 1158 | Attributions\n",
      "Chunk token count: 20.5 | Text: PART III CHAPTER 3. WATER AND ELECTROLYTES Chapter 3. Water and Electrolytes | 143\n",
      "Chunk token count: 21.0 | Text: PART XII CHAPTER 12. NUTRITION APPLICATIONS Chapter 12. Nutrition Applications | 705\n",
      "Chunk token count: 27.75 | Text: In exchange, for the reabsorption of sodium and water, potassium is excreted. Regulation of Water Balance | 169\n",
      "Chunk token count: 20.0 | Text: http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=507  Sports Nutrition | 971\n"
     ]
    }
   ],
   "source": [
    "# Show random chunks with under 30 tokens in length\n",
    "min_token_length = 30\n",
    "for row in df[df[\"chunk_token_count\"] <= min_token_length].sample(5).iterrows():\n",
    "    print(f'Chunk token count: {row[1][\"chunk_token_count\"]} | Text: {row[1][\"sentence_chunk\"]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': -39,\n",
       "  'sentence_chunk': 'Human Nutrition: 2020 Edition UNIVERSITY OF HAWAI‘I AT MĀNOA FOOD SCIENCE AND HUMAN NUTRITION PROGRAM ALAN TITCHENAL, SKYLAR HARA, NOEMI ARCEO CAACBAY, WILLIAM MEINKE-LAU, YA-YUN YANG, MARIE KAINOA FIALKOWSKI REVILLA, JENNIFER DRAPER, GEMADY LANGFELDER, CHERYL GIBBY, CHYNA NICOLE CHUN, AND ALLISON CALABRESE',\n",
       "  'chunk_char_count': 308,\n",
       "  'chunk_word_count': 42,\n",
       "  'chunk_token_count': 77.0},\n",
       " {'page_number': -38,\n",
       "  'sentence_chunk': 'Human Nutrition: 2020 Edition by University of Hawai‘i at Mānoa Food Science and Human Nutrition Program is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.',\n",
       "  'chunk_char_count': 210,\n",
       "  'chunk_word_count': 30,\n",
       "  'chunk_token_count': 52.5}]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter our dataframe/list of dict to only include chunks over 30 tokens in length\n",
    "pages_and_chunks_over_min_token_len = df[df[\"chunk_token_count\"] > min_token_length].to_dict(orient=\"records\")\n",
    "pages_and_chunks_over_min_token_len[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding our text chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While humans understand text, machines understand numbers best.\n",
    "\n",
    "An [embedding](https://vickiboykis.com/what_are_embeddings/index.html) is a broad concept.\n",
    "\n",
    "But one of my favourite and simple definitions is \"a useful numerical representation\".\n",
    "\n",
    "The most powerful thing about modern embeddings is that they are *learned* representations.\n",
    "\n",
    "Meaning rather than directly mapping words/tokens/characters to numbers directly (e.g. `{\"a\": 0, \"b\": 1, \"c\": 3...}`), the numerical representation of tokens is learned by going through large corpuses of text and figuring out how different tokens relate to each other.\n",
    "\n",
    "Ideally, embeddings of text will mean that similar meaning texts have similar numerical representation.\n",
    "\n",
    "> **Note:** Most modern NLP models deal with \"tokens\" which can be considered as multiple different sizes and combinations of words and characters rather than always whole words or single characters. For example, the string `\"hello world!\"` gets mapped to the token values `{15339: b'hello', 1917: b' world', 0: b'!'}` using [Byte pair encoding](https://en.wikipedia.org/wiki/Byte_pair_encoding) (or BPE via OpenAI's [`tiktoken`](https://github.com/openai/tiktoken) library). Google has a tokenization library called [SentencePiece](https://github.com/google/sentencepiece).\n",
    "\n",
    "Our goal is to turn each of our chunks into a numerical representation (an embedding vector, where a vector is a sequence of numbers arranged in order).\n",
    "\n",
    "Once our text samples are in embedding vectors, us humans will no longer be able to understand them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how about we import a text embedding model and see what an embedding looks like.\n",
    "\n",
    "To do so, we'll use the [`sentence-transformers`](https://www.sbert.net/docs/installation.html) library which contains many pre-trained embedding models.\n",
    "\n",
    "Specifically, we'll get the `all-mpnet-base-v2` model (you can see the model's intended use on the [Hugging Face model card](https://huggingface.co/sentence-transformers/all-mpnet-base-v2#intended-uses))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Work\\NanoRAG\\venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1142: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "c:\\Work\\NanoRAG\\venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1142: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: The Sentences Transformers library provides an easy and open-source way to create embeddings.\n",
      "Embedding: [-2.07981467e-02  3.03165019e-02 -2.01217644e-02  6.86483607e-02\n",
      " -2.55255550e-02 -8.47691298e-03 -2.07126126e-04 -6.32376894e-02\n",
      "  2.81606223e-02 -3.33353467e-02  3.02634444e-02  5.30720763e-02\n",
      " -5.03526330e-02  2.62288321e-02  3.33313420e-02 -4.51578535e-02\n",
      "  3.63043770e-02 -1.37112022e-03 -1.20171346e-02  1.14947064e-02\n",
      "  5.04510291e-02  4.70856875e-02  2.11913139e-02  5.14607169e-02\n",
      " -2.03746036e-02 -3.58889326e-02 -6.67864166e-04 -2.94393208e-02\n",
      "  4.95858863e-02 -1.05639445e-02 -1.52013898e-02 -1.31752773e-03\n",
      "  4.48197201e-02  1.56023223e-02  8.60379942e-07 -1.21392461e-03\n",
      " -2.37978473e-02 -9.09396389e-04  7.34480657e-03 -2.53929943e-03\n",
      "  5.23369759e-02 -4.68042754e-02  1.66214295e-02  4.71578874e-02\n",
      " -4.15599383e-02  9.01959022e-04  3.60278860e-02  3.42214592e-02\n",
      "  9.68227386e-02  5.94828725e-02 -1.64984707e-02 -3.51249240e-02\n",
      "  5.92515897e-03 -7.07935484e-04 -2.41030715e-02  3.49741243e-02\n",
      " -2.94746589e-02  6.04273239e-03 -9.80645791e-03  2.83218399e-02\n",
      " -1.85376033e-02  3.63213159e-02  1.30292941e-02 -3.71233672e-02\n",
      "  5.27257435e-02 -1.19706169e-02 -7.18082413e-02  1.24431904e-02\n",
      " -6.70569623e-03  7.42154419e-02  1.16357086e-02 -1.74533129e-02\n",
      " -1.82406195e-02 -1.88930351e-02  2.82414556e-02  1.32828727e-02\n",
      " -3.51910442e-02  8.87332368e-04  5.79572879e-02  3.22093144e-02\n",
      " -3.48585588e-03  4.13768813e-02  1.44358072e-02 -3.28044556e-02\n",
      " -9.79080796e-03 -3.16492878e-02  4.23871167e-02 -4.70847338e-02\n",
      " -2.08937209e-02 -1.91249661e-02 -1.22626992e-02  1.01604499e-02\n",
      "  3.91922109e-02 -2.61895154e-02  1.09028341e-02  1.35722794e-02\n",
      " -5.79266995e-02 -3.21499743e-02 -5.75724756e-03 -2.43517123e-02\n",
      "  5.23417890e-02  5.46126813e-03 -2.30995845e-02  2.57173833e-03\n",
      " -6.63346201e-02  3.54125835e-02 -1.03907501e-02  2.25409921e-02\n",
      " -1.84574537e-02 -2.42005624e-02 -4.78365086e-02 -4.79243882e-03\n",
      " -5.34138307e-02  3.01790573e-02 -1.56130241e-02 -5.51476479e-02\n",
      " -3.91874798e-02  5.92152737e-02 -3.47646587e-02  9.68123134e-03\n",
      "  2.13415138e-02  2.30417270e-02  1.91712454e-02  2.77378634e-02\n",
      " -7.73513271e-03  1.04445452e-02 -2.67719887e-02 -2.40199696e-02\n",
      " -1.92289464e-02  3.91503749e-03 -2.54714470e-02  3.61942723e-02\n",
      "  5.12867272e-02 -8.41696374e-03 -3.13829519e-02  1.47484103e-02\n",
      "  2.13939510e-02 -3.84900905e-02  2.01946441e-02  1.20765623e-02\n",
      " -3.12088802e-03  7.84029718e-03  3.30338534e-03 -4.94357497e-02\n",
      "  5.83886281e-02  3.26131610e-03  4.84488392e-03 -4.50681821e-02\n",
      "  2.45683175e-02  3.55428495e-02 -5.32505661e-02  9.21152756e-02\n",
      "  2.04394981e-02 -3.36952358e-02 -6.19804151e-02 -2.11038999e-02\n",
      "  7.82360286e-02  5.11907600e-02  5.93170859e-02 -1.25062492e-04\n",
      "  4.96349148e-02 -1.55722424e-02 -3.35673452e-03  1.82015505e-02\n",
      " -2.73444094e-02 -1.08772069e-02  1.41476076e-02  1.09876860e-02\n",
      "  4.32551978e-03  8.23312178e-02 -9.85429971e-04  7.58790746e-02\n",
      "  9.45000350e-03  2.37688012e-02  1.61929652e-02  6.24994077e-02\n",
      "  4.75921743e-02 -3.92624689e-03  9.07524750e-02  4.49874029e-02\n",
      " -3.47131416e-02  2.14078035e-02 -3.35604697e-02  4.93849926e-02\n",
      "  1.08670797e-02  2.63447426e-02 -3.26089002e-02  8.00303444e-02\n",
      "  9.29771084e-03  7.16572301e-03 -2.79172380e-02 -3.06820776e-02\n",
      "  4.01067315e-03 -4.93906848e-02 -3.13776592e-03  4.00538035e-02\n",
      " -3.97855230e-02  5.48014417e-02  1.35517903e-05 -8.38373601e-02\n",
      " -1.21547710e-02  3.40950266e-02  3.22400127e-03  6.11846559e-02\n",
      "  5.60066700e-02  9.62874573e-03  2.54615955e-02 -4.64168973e-02\n",
      " -3.98899764e-02  7.68132657e-02  2.28408258e-02 -2.26567332e-02\n",
      " -1.91193093e-02 -6.53028041e-02  4.56780568e-02 -4.43658093e-03\n",
      "  1.49631863e-02 -2.15078667e-02  2.74239946e-03  1.90358218e-02\n",
      "  5.91888502e-02 -2.47569457e-02  3.66144739e-02  5.63083962e-02\n",
      " -8.86444468e-03 -1.74324736e-02 -1.03284826e-03  2.47666985e-02\n",
      "  1.30762663e-02  5.04633002e-02 -5.28496411e-03  5.92397079e-02\n",
      "  6.29906282e-02 -4.36783768e-02 -4.97831404e-02  5.56297228e-02\n",
      " -2.44854856e-02 -8.26755017e-02  2.04911344e-02 -1.06446348e-01\n",
      "  6.64835051e-03  2.97303535e-02 -2.36440394e-02 -8.84609390e-03\n",
      "  2.45549064e-03 -3.35234888e-02  7.52212703e-02 -5.89879863e-02\n",
      " -3.67807820e-02  3.41542140e-02  5.41130528e-02 -1.74904894e-02\n",
      "  1.33920191e-02  4.71682027e-02  1.46117238e-02 -2.12310776e-02\n",
      " -6.55338690e-02  1.23858098e-02  2.76075155e-02 -8.02155212e-03\n",
      " -4.59636860e-02 -8.22445098e-03  9.16950498e-03 -1.56399012e-02\n",
      "  7.54618039e-03  1.58316432e-03 -3.03958915e-02 -5.10671102e-02\n",
      "  1.96313523e-02  1.26262959e-02 -1.51740247e-03  2.02890728e-02\n",
      "  1.37817496e-02  1.49110695e-02  2.50766557e-02 -3.62870768e-02\n",
      "  1.08084828e-02  2.74130632e-03  1.81510448e-02  5.39872870e-02\n",
      " -4.74541709e-02 -4.28731330e-02 -2.89914124e-02  2.13234909e-02\n",
      " -3.85161750e-02  6.31922334e-02 -5.77975027e-02  3.77894752e-03\n",
      " -2.54394766e-02 -1.77194786e-04  9.08245519e-03  1.59094520e-02\n",
      "  4.11799215e-02 -3.94369476e-02 -9.64429975e-03  1.30792353e-02\n",
      "  6.87962547e-02  4.32193317e-02  7.54084263e-04  6.77741244e-02\n",
      "  4.93706800e-02 -3.47820204e-03 -1.06054023e-02  6.72491919e-03\n",
      " -1.39062051e-02  4.88276705e-02 -1.05736125e-02  3.50227114e-03\n",
      "  2.90227775e-03  2.40044724e-02  1.20272655e-02 -2.09797062e-02\n",
      " -2.39112470e-02  3.26579511e-02 -1.01326581e-03 -5.92751568e-03\n",
      " -7.40541331e-03  3.63139738e-03 -2.26698387e-02 -2.21242309e-02\n",
      "  3.86995897e-02  1.72321573e-02  3.85921784e-02 -5.04711159e-02\n",
      " -3.42144929e-02 -4.00443152e-02 -3.57910842e-02 -4.62560803e-02\n",
      "  6.70231953e-02 -4.61643422e-03 -3.29676620e-03  2.08444428e-02\n",
      " -5.14255231e-03 -5.00850379e-02  2.22504288e-02  4.66933623e-02\n",
      "  1.36208972e-02  1.77530795e-02  4.28080792e-03 -2.79332548e-02\n",
      " -1.93421338e-02 -3.87860909e-02 -3.09555214e-02 -6.64133877e-02\n",
      " -1.13433758e-02  1.64267439e-02  1.77630018e-02 -2.28232634e-03\n",
      " -3.30087878e-02 -1.36265368e-03 -2.17933934e-02 -2.67508533e-02\n",
      " -1.26375761e-02  1.61861244e-03 -4.95673046e-02  7.85445124e-02\n",
      "  4.10962105e-02  9.65918135e-03 -1.14643537e-02  1.68854021e-03\n",
      "  5.37663363e-02  2.05537095e-03 -4.11201380e-02  1.46330548e-02\n",
      " -3.75564322e-02 -3.35688777e-02  5.19251823e-03 -6.33089170e-02\n",
      "  3.32963616e-02  8.76124110e-03  1.33853895e-03 -3.95757705e-03\n",
      " -1.61678158e-02  8.26747119e-02  4.75944839e-02 -3.43055390e-02\n",
      "  2.50881426e-02 -3.50976661e-02  3.68657559e-02  4.12656739e-03\n",
      "  4.16018292e-02 -1.35181740e-01 -4.76337261e-02 -1.20025352e-02\n",
      " -3.48891951e-02  3.25453952e-02 -2.93570454e-03 -4.85060085e-03\n",
      " -1.04223736e-01  2.78611183e-02  1.41570205e-02  3.94396409e-02\n",
      " -3.88806053e-02 -1.42463697e-02 -5.19984812e-02  8.92742537e-03\n",
      " -1.99771635e-02 -2.51725223e-02 -3.41300145e-02  1.93040986e-02\n",
      " -5.20207435e-02 -6.72000423e-02 -9.46364831e-03 -1.25589897e-03\n",
      " -5.66048138e-02  2.62098089e-02  9.91587434e-03  4.38287370e-02\n",
      "  2.26633972e-03 -3.11896205e-02 -6.25468194e-02 -3.87792177e-02\n",
      " -6.83938861e-02  4.93722707e-02  5.85508160e-02 -4.08729725e-02\n",
      " -1.98638439e-02 -2.12635454e-02  4.98037189e-02 -4.51747961e-02\n",
      " -2.37141885e-02  2.32674647e-02  1.00594826e-01  9.87110659e-03\n",
      " -1.38014425e-02 -5.21041341e-02  9.08214319e-03  1.72428340e-02\n",
      "  5.91432787e-02  2.62337066e-02 -7.04644760e-03 -1.50031652e-02\n",
      " -3.76663171e-03  6.28263969e-03 -5.23982160e-02 -4.96639162e-02\n",
      "  3.06611061e-02 -3.33645917e-03  2.34911349e-02 -8.58830139e-02\n",
      " -4.62449230e-02  5.59701361e-02  3.09083000e-04  2.01729629e-02\n",
      " -2.98063643e-03  1.76645368e-02  1.54668940e-02 -7.41718486e-02\n",
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      "  9.58635751e-03  1.46733401e-02  5.05845360e-02  3.06891706e-02\n",
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      " -3.39237042e-02  1.49903307e-02 -3.03336196e-02 -6.06489554e-03\n",
      " -4.81764227e-03  1.72137376e-02 -8.23365711e-03  1.55548090e-02\n",
      "  2.69106831e-02  5.44309663e-03 -1.06899217e-02 -7.82138947e-03\n",
      " -4.44506258e-02  2.55873818e-02 -5.74760772e-02 -2.05442850e-02\n",
      " -3.07849664e-02 -1.57854836e-02 -7.07542244e-03 -4.21312191e-02\n",
      "  3.79934609e-02  6.27764910e-02 -7.67793367e-03 -3.18353623e-02\n",
      "  1.99277746e-34  1.04834251e-02 -3.39326598e-02  3.93821113e-02\n",
      "  5.53065762e-02  9.42166336e-03  1.09727820e-02 -4.91940193e-02\n",
      "  2.95024179e-02 -8.85373354e-03 -5.96248135e-02 -2.37825457e-02]\n",
      "\n",
      "Sentence: Embeddings are one of the most powerful concepts in machine learning!\n",
      "Embedding: [-2.98611447e-02 -1.37522146e-02 -4.75401804e-02  2.72126738e-02\n",
      "  3.40054370e-02  3.16465423e-02  4.26963344e-02  3.29791452e-03\n",
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      " -2.08857451e-02  4.30556349e-02  1.07643634e-01  4.40618396e-02\n",
      "  1.47962598e-02  2.44862698e-02 -3.86268497e-02  1.80744119e-02\n",
      " -1.47839170e-03  7.74167180e-02 -4.19566147e-02 -3.80529501e-02\n",
      "  3.61257121e-02  1.59030000e-03  1.95323508e-02 -2.00080872e-02\n",
      "  4.22538035e-02  3.06110848e-02 -3.53690307e-03  5.93099231e-03\n",
      " -2.23223418e-02 -2.07131375e-02 -3.62903043e-03  1.74653623e-02\n",
      " -4.08759154e-02  5.91594800e-02 -5.89099787e-02 -3.96753475e-02\n",
      " -3.33529189e-02  1.02162026e-02  6.97290618e-03  7.70389736e-02\n",
      " -1.86911859e-02 -1.82568245e-02 -2.42318437e-02 -3.40700452e-03\n",
      " -3.60555239e-02  4.33389135e-02 -3.48603278e-02  5.27768508e-02\n",
      "  2.89709400e-02 -4.98462431e-02 -1.94748733e-02  1.16397887e-02\n",
      " -3.04615125e-02  8.04637671e-02  6.56247884e-02 -2.84533408e-02\n",
      "  1.81615347e-34 -4.19158861e-03 -2.57882755e-02  5.17320447e-02\n",
      "  4.94421050e-02  1.32476315e-02 -4.21994179e-02 -1.12458291e-02\n",
      " -2.61519514e-02  5.51130362e-02  2.20025014e-02 -2.51170713e-02]\n",
      "\n",
      "Sentence: Learn to use embeddings well and you'll be well on your way to being an AI engineer.\n",
      "Embedding: [-2.20731031e-02  2.08950881e-02 -6.03005402e-02  8.43947474e-03\n",
      "  4.37650681e-02  1.55070266e-02  4.99907769e-02 -3.03232577e-02\n",
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      " -7.44785881e-03  2.94555053e-02  3.52999149e-03 -6.10421710e-02]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Requires !pip install sentence-transformers\n",
    "from sentence_transformers import SentenceTransformer\n",
    "embedding_model = SentenceTransformer(model_name_or_path=\"all-mpnet-base-v2\", \n",
    "                                      device=\"cpu\") # choose the device to load the model to (note: GPU will often be *much* faster than CPU)\n",
    "\n",
    "# Create a list of sentences to turn into numbers\n",
    "sentences = [\n",
    "    \"The Sentences Transformers library provides an easy and open-source way to create embeddings.\",\n",
    "    \"Sentences can be embedded one by one or as a list of strings.\",\n",
    "    \"Embeddings are one of the most powerful concepts in machine learning!\",\n",
    "    \"Learn to use embeddings well and you'll be well on your way to being an AI engineer.\"\n",
    "]\n",
    "\n",
    "# Sentences are encoded/embedded by calling model.encode()\n",
    "embeddings = embedding_model.encode(sentences)\n",
    "embeddings_dict = dict(zip(sentences, embeddings))\n",
    "\n",
    "# See the embeddings\n",
    "for sentence, embedding in embeddings_dict.items():\n",
    "    print(\"Sentence:\", sentence)\n",
    "    print(\"Embedding:\", embedding)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: Yo! How cool are embeddings?\n",
      "Embedding:\n",
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      " -9.87676065e-03  2.72835679e-02  3.66426259e-02 -3.30214575e-03\n",
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      " -5.75199760e-02  6.33692592e-02  4.43207659e-02 -4.49507162e-02\n",
      "  1.25284335e-02 -2.52012201e-02 -3.55292112e-02  1.29558695e-02\n",
      "  8.67022853e-03 -1.92917529e-02  3.55632463e-03  1.89505722e-02\n",
      " -1.47127640e-02 -9.39843338e-03  7.64170848e-03  9.62187909e-03\n",
      " -5.98929077e-03 -3.90168838e-02 -5.47824912e-02 -5.67453681e-03\n",
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      " -8.77263770e-03  2.39382926e-02 -2.32784450e-02  8.04999322e-02\n",
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      " -1.49368625e-02 -1.70671288e-02 -4.19716649e-02  3.94656090e-03\n",
      " -2.24301834e-02  2.07291506e-02 -4.61415648e-02  8.50216392e-03\n",
      " -2.56864503e-02 -1.65337939e-02 -1.51846576e-02 -1.00042522e-02\n",
      "  2.19642855e-02  2.61104424e-02  7.31359124e-02 -1.83710139e-02\n",
      "  1.93979504e-34 -7.27933319e-03  5.96485985e-03  4.44310792e-02\n",
      "  4.14823182e-02  1.12917116e-02 -1.93217881e-02  4.41878401e-02\n",
      " -8.93787015e-03  3.61119732e-02 -5.52126355e-02 -2.89572533e-02]\n",
      "Embedding size: (768,)\n"
     ]
    }
   ],
   "source": [
    "# one sentence embedding at a time\n",
    "\n",
    "single_sentence = \"Yo! How cool are embeddings?\"\n",
    "single_embedding = embedding_model.encode(single_sentence)\n",
    "print(f\"Sentence: {single_sentence}\")\n",
    "print(f\"Embedding:\\n{single_embedding}\")\n",
    "print(f\"Embedding size: {single_embedding.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "# create embeddings in the CPU\n",
    "%time\n",
    "\n",
    "# Uncomment to see how long it takes to create embeddings on CPU\n",
    "# # Make sure the model is on the CPU\n",
    "# embedding_model.to(\"cpu\")\n",
    "\n",
    "# # Embed each chunk one by one\n",
    "# for item in tqdm(pages_and_chunks_over_min_token_len):\n",
    "#     item[\"embedding\"] = embedding_model.encode(item[\"sentence_chunk\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e4c9aebac634597bb3a02b96fab37e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1680 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create embeddings with a GPU\n",
    "\n",
    "%time\n",
    "\n",
    "# Send the model to the GPU\n",
    "embedding_model.to(\"cuda\") # requires a GPU installed, for reference on my local machine, I'm using a NVIDIA RTX 4090\n",
    "\n",
    "# Create embeddings one by one on the GPU\n",
    "for item in tqdm(pages_and_chunks_over_min_token_len):\n",
    "    item[\"embedding\"] = embedding_model.encode(item[\"sentence_chunk\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn text chunks into a single list\n",
    "text_chunks = [item[\"sentence_chunk\"] for item in pages_and_chunks_over_min_token_len]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0674,  0.0902, -0.0051,  ..., -0.0221, -0.0232,  0.0126],\n",
       "        [ 0.0552,  0.0592, -0.0166,  ..., -0.0120, -0.0103,  0.0227],\n",
       "        [ 0.0280,  0.0340, -0.0206,  ..., -0.0054,  0.0213,  0.0313],\n",
       "        ...,\n",
       "        [ 0.0771,  0.0098, -0.0122,  ..., -0.0409, -0.0752, -0.0241],\n",
       "        [ 0.1030, -0.0165,  0.0083,  ..., -0.0574, -0.0283, -0.0295],\n",
       "        [ 0.0864, -0.0125, -0.0113,  ..., -0.0522, -0.0337, -0.0299]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "\n",
    "# Embed all texts in batches\n",
    "text_chunk_embeddings = embedding_model.encode(text_chunks,\n",
    "                                               batch_size=32, # you can use different batch sizes here for speed/performance, I found 32 works well for this use case\n",
    "                                               convert_to_tensor=True) # optional to return embeddings as tensor instead of array\n",
    "\n",
    "text_chunk_embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save embeddings to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save embeddings to file\n",
    "text_chunks_and_embeddings_df = pd.DataFrame(pages_and_chunks_over_min_token_len)\n",
    "embeddings_df_save_path = \"text_chunks_and_embeddings_df.csv\"\n",
    "text_chunks_and_embeddings_df.to_csv(embeddings_df_save_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>page_number</th>\n",
       "      <th>sentence_chunk</th>\n",
       "      <th>chunk_char_count</th>\n",
       "      <th>chunk_word_count</th>\n",
       "      <th>chunk_token_count</th>\n",
       "      <th>embedding</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-39</td>\n",
       "      <td>Human Nutrition: 2020 Edition UNIVERSITY OF HA...</td>\n",
       "      <td>308</td>\n",
       "      <td>42</td>\n",
       "      <td>77.00</td>\n",
       "      <td>[ 6.74242675e-02  9.02281329e-02 -5.09549491e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-38</td>\n",
       "      <td>Human Nutrition: 2020 Edition by University of...</td>\n",
       "      <td>210</td>\n",
       "      <td>30</td>\n",
       "      <td>52.50</td>\n",
       "      <td>[ 5.52156270e-02  5.92139587e-02 -1.66167449e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-37</td>\n",
       "      <td>Contents Preface University of Hawai‘i at Māno...</td>\n",
       "      <td>766</td>\n",
       "      <td>116</td>\n",
       "      <td>191.50</td>\n",
       "      <td>[ 2.79801972e-02  3.39813679e-02 -2.06426457e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-36</td>\n",
       "      <td>Lifestyles and Nutrition University of Hawai‘i...</td>\n",
       "      <td>941</td>\n",
       "      <td>144</td>\n",
       "      <td>235.25</td>\n",
       "      <td>[ 6.82566985e-02  3.81275043e-02 -8.46854504e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-35</td>\n",
       "      <td>The Cardiovascular System University of Hawai‘...</td>\n",
       "      <td>998</td>\n",
       "      <td>152</td>\n",
       "      <td>249.50</td>\n",
       "      <td>[ 3.30264494e-02 -8.49768892e-03  9.57159698e-...</td>\n",
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       "   page_number                                     sentence_chunk  \\\n",
       "0          -39  Human Nutrition: 2020 Edition UNIVERSITY OF HA...   \n",
       "1          -38  Human Nutrition: 2020 Edition by University of...   \n",
       "2          -37  Contents Preface University of Hawai‘i at Māno...   \n",
       "3          -36  Lifestyles and Nutrition University of Hawai‘i...   \n",
       "4          -35  The Cardiovascular System University of Hawai‘...   \n",
       "\n",
       "   chunk_char_count  chunk_word_count  chunk_token_count  \\\n",
       "0               308                42              77.00   \n",
       "1               210                30              52.50   \n",
       "2               766               116             191.50   \n",
       "3               941               144             235.25   \n",
       "4               998               152             249.50   \n",
       "\n",
       "                                           embedding  \n",
       "0  [ 6.74242675e-02  9.02281329e-02 -5.09549491e-...  \n",
       "1  [ 5.52156270e-02  5.92139587e-02 -1.66167449e-...  \n",
       "2  [ 2.79801972e-02  3.39813679e-02 -2.06426457e-...  \n",
       "3  [ 6.82566985e-02  3.81275043e-02 -8.46854504e-...  \n",
       "4  [ 3.30264494e-02 -8.49768892e-03  9.57159698e-...  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import saved file and view\n",
    "text_chunks_and_embedding_df_load = pd.read_csv(embeddings_df_save_path)\n",
    "text_chunks_and_embedding_df_load.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunking and embedding questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which embedding model should I use?**\n",
    "\n",
    "This depends on many factors. My best advice is to experiment, experiment, experiment! \n",
    "\n",
    "If you want the model to run locally, you'll have to make sure it's feasible to run on your own hardware. \n",
    "\n",
    "A good place to see how different models perform on a wide range of embedding tasks is the [Hugging Face Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).\n",
    "\n",
    "> **What other forms of text chunking/splitting are there?**\n",
    "\n",
    "There are a fair few options here too. We've kept it simple with groups of sentences.\n",
    "\n",
    "For more, [Pinecone has a great guide on different kinds of chunking](https://www.pinecone.io/learn/chunking-strategies/) including for different kinds of data such as markdown and LaTeX.\n",
    "\n",
    "Libraries such as [LangChain also have a good amount of in-built text splitting options](https://python.langchain.com/docs/modules/data_connection/document_transformers/).\n",
    "\n",
    "> **What should I think about when creating my embeddings?**\n",
    "\n",
    "Our model turns text inputs up to 384 tokens long in embedding vectors of size 768.\n",
    "\n",
    "Generally, the larger the vector size, the more information that gets encoded into the embedding (however, this is not always the case, as smaller, better models can outperform larger ones).\n",
    "\n",
    "Though with larger vector sizes comes larger storage and compute requirements.\n",
    "\n",
    "Our model is also relatively small (420MB) in size compared to larger models that are available.\n",
    "\n",
    "Larger models may result in better performance but will also require more compute.\n",
    "\n",
    "So some things to think about:\n",
    "* Size of input - If you need to embed longer sequences, choose a model with a larger input capacity.\n",
    "* Size of embedding vector - Larger is generally a better representation but requires more compute/storage.\n",
    "* Size of model - Larger models generally result in better embeddings but require more compute power/time to run.\n",
    "* Open or closed - Open models allow you to run them on your own hardware whereas closed models can be easier to setup but require an API call to get embeddings.\n",
    "\n",
    "> **Where should I store my embeddings?**\n",
    "\n",
    "If you've got a relatively small dataset, for example, under 100,000 examples (this number is rough and only based on first hand experience), `np.array` or `torch.tensor` can work just fine as your dataset.\n",
    "\n",
    "But if you've got a production system and want to work with 100,000+ embeddings, you may want to look into a [vector database]( https://en.wikipedia.org/wiki/Vector_database) (these have become very popular lately and there are many offerings).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. RAG - Search and Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1680, 768])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "import torch\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "# Import texts and embedding df\n",
    "text_chunks_and_embedding_df = pd.read_csv(\"text_chunks_and_embeddings_df.csv\")\n",
    "\n",
    "# Convert embedding column back to np.array (it got converted to string when it got saved to CSV)\n",
    "text_chunks_and_embedding_df[\"embedding\"] = text_chunks_and_embedding_df[\"embedding\"].apply(lambda x: np.fromstring(x.strip(\"[]\"), sep=\" \"))\n",
    "\n",
    "# Convert texts and embedding df to list of dicts\n",
    "pages_and_chunks = text_chunks_and_embedding_df.to_dict(orient=\"records\")\n",
    "\n",
    "# Convert embeddings to torch tensor and send to device (note: NumPy arrays are float64, torch tensors are float32 by default)\n",
    "embeddings = torch.tensor(np.array(text_chunks_and_embedding_df[\"embedding\"].tolist()), dtype=torch.float32).to(device)\n",
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
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       "   page_number                                     sentence_chunk  \\\n",
       "0          -39  Human Nutrition: 2020 Edition UNIVERSITY OF HA...   \n",
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       "2          -37  Contents Preface University of Hawai‘i at Māno...   \n",
       "3          -36  Lifestyles and Nutrition University of Hawai‘i...   \n",
       "4          -35  The Cardiovascular System University of Hawai‘...   \n",
       "\n",
       "   chunk_char_count  chunk_word_count  chunk_token_count  \\\n",
       "0               308                42              77.00   \n",
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       "2               766               116             191.50   \n",
       "3               941               144             235.25   \n",
       "4               998               152             249.50   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.0674242675, 0.0902281329, -0.00509549491, -...  \n",
       "1  [0.055215627, 0.0592139587, -0.0166167449, -0....  \n",
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       "4  [0.0330264494, -0.00849768892, 0.00957159698, ...  "
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     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_chunks_and_embedding_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "         4.1844e-02, -5.3879e-02,  2.9447e-02,  1.4859e-02, -4.2965e-02,\n",
       "         3.4447e-02, -2.7505e-02,  1.3751e-02,  4.0990e-02, -8.8896e-03,\n",
       "        -3.0511e-03,  3.3440e-02, -4.0466e-03,  3.2865e-02, -1.4610e-02,\n",
       "         2.0716e-02, -3.0506e-03, -3.8943e-02,  4.8812e-03, -2.9872e-02,\n",
       "         1.7439e-03,  8.6081e-02,  6.6861e-03, -1.6257e-02, -8.1288e-03,\n",
       "        -3.5705e-02, -4.7969e-02,  2.2529e-02,  2.2744e-02, -1.9377e-03,\n",
       "         1.1901e-02, -1.0321e-02, -1.3257e-02, -1.4464e-02, -3.1800e-03,\n",
       "        -2.8913e-02,  4.8882e-03,  4.5425e-02, -1.0888e-01, -4.4595e-02,\n",
       "        -1.6905e-03,  1.7857e-02,  5.7418e-02, -1.3633e-02,  2.3022e-02,\n",
       "        -4.6197e-02, -3.1276e-02,  6.2125e-03, -4.9911e-02, -3.6829e-02,\n",
       "        -3.4771e-02,  2.0124e-03,  3.9963e-04,  6.6996e-02, -1.7236e-02,\n",
       "        -5.3030e-02,  9.9721e-03,  2.3808e-02, -1.7870e-02,  1.9527e-02,\n",
       "        -5.1236e-02, -3.7996e-02,  2.5240e-02, -3.1528e-02,  2.8217e-02,\n",
       "        -1.4053e-02,  2.7565e-02, -7.5722e-03, -3.1351e-02, -1.0788e-03,\n",
       "         3.0862e-02,  8.4648e-02, -2.3276e-02, -6.3262e-02,  6.4850e-02,\n",
       "         5.1187e-02, -4.6951e-03,  2.1690e-02,  2.2844e-02, -1.7596e-02,\n",
       "        -1.7416e-06,  5.0318e-02, -1.4955e-02,  1.5985e-02,  2.3257e-03,\n",
       "        -5.8727e-02,  3.7002e-02,  7.2775e-02, -3.3827e-02, -1.8754e-02,\n",
       "         4.6750e-02, -1.5560e-02,  3.5652e-02,  9.9984e-03, -2.0313e-02,\n",
       "        -5.6848e-02, -4.9714e-02, -2.9255e-02,  2.4087e-02, -4.6807e-02,\n",
       "         1.0582e-02,  4.2214e-03, -8.8039e-03, -2.4846e-03,  7.1090e-03,\n",
       "        -4.0460e-03,  1.7800e-02, -2.3911e-02, -6.8735e-03,  3.7128e-02,\n",
       "        -1.4069e-02, -1.2391e-02,  3.3488e-02,  3.9902e-02, -1.6672e-02,\n",
       "         1.4881e-02,  3.4921e-02,  4.1948e-02,  4.1065e-02,  2.8328e-02,\n",
       "         3.0433e-02, -2.0902e-02,  2.8019e-02, -5.1244e-02,  2.0354e-02,\n",
       "        -1.4630e-02, -9.9522e-03, -2.1091e-02,  4.4857e-02, -3.2936e-02,\n",
       "        -4.5591e-02,  4.4177e-02, -2.7671e-03,  5.7345e-02, -4.1759e-03,\n",
       "        -2.9215e-02,  2.7704e-02, -1.5260e-02, -6.1995e-02,  2.9332e-02,\n",
       "         4.4823e-02,  2.1181e-02, -9.1190e-03, -3.6573e-03, -3.8882e-02,\n",
       "         1.7185e-02,  7.2466e-02, -5.5369e-02, -4.0100e-02, -1.9544e-02,\n",
       "        -1.3069e-02,  1.2657e-02,  4.2051e-03, -3.1517e-02,  9.3331e-03,\n",
       "         7.5865e-03,  1.2584e-02,  3.9474e-02,  9.2081e-02,  3.6145e-02,\n",
       "         2.1324e-02,  9.9828e-03, -2.4977e-02,  4.9934e-02,  3.2386e-02,\n",
       "        -8.3485e-04,  4.2720e-02, -5.2461e-02,  1.9335e-02, -2.2124e-02,\n",
       "         4.5479e-02, -1.2786e-02, -1.7430e-02,  2.5903e-02, -2.3038e-02,\n",
       "        -3.1163e-02,  1.5773e-02,  2.0822e-03, -3.2866e-02, -2.6236e-02,\n",
       "        -4.7034e-02,  5.6060e-02,  5.2010e-03,  1.2913e-02,  1.7967e-02,\n",
       "        -1.7415e-02, -5.5965e-02, -1.7394e-02, -2.7446e-02,  3.9135e-02,\n",
       "        -4.8180e-03,  2.6728e-03, -8.5087e-03, -4.7705e-02,  5.8733e-03,\n",
       "         7.4838e-03, -3.3042e-02, -2.7491e-02, -8.2585e-03, -4.7996e-02,\n",
       "         1.5387e-02,  4.0562e-02, -1.7994e-02, -1.5388e-02, -5.4152e-03,\n",
       "         3.6740e-02, -3.1735e-02, -9.0054e-03, -1.2232e-02,  2.1254e-02,\n",
       "         1.1946e-02, -8.7441e-03,  1.6887e-02, -1.4598e-02, -3.3291e-02,\n",
       "         3.6413e-02, -5.1190e-02,  7.4620e-03, -3.6541e-02, -4.2688e-02,\n",
       "        -2.8181e-02, -1.3403e-02,  2.6602e-02,  2.1015e-02, -1.6868e-02,\n",
       "        -6.1321e-02, -2.1097e-03, -2.4802e-02,  2.2264e-02,  9.2585e-03,\n",
       "        -5.5378e-03, -5.5574e-02,  7.0564e-03, -2.4968e-02, -2.0095e-02,\n",
       "         6.4291e-03,  1.3989e-02, -1.4590e-02, -4.8052e-02,  8.3201e-02,\n",
       "         5.4959e-02,  2.7590e-02, -4.8724e-03,  4.7631e-02,  2.1820e-02,\n",
       "        -7.3083e-03, -2.9660e-02,  3.7069e-03, -6.4659e-02, -2.8210e-02,\n",
       "         7.4635e-03, -8.5547e-02,  1.2753e-02,  2.8452e-03,  1.8569e-02,\n",
       "         5.4310e-02, -1.2299e-01, -7.5443e-02,  2.1505e-02, -1.2050e-01,\n",
       "         3.2535e-03, -6.5993e-04,  1.9948e-03, -9.9720e-03,  5.7540e-02,\n",
       "        -6.9637e-33,  3.8571e-02, -4.3609e-02,  2.1520e-02,  1.8536e-02,\n",
       "         4.1757e-02,  2.0367e-02, -1.1547e-02, -2.7678e-02, -1.8432e-02,\n",
       "         8.4662e-03, -8.7624e-04,  8.2839e-04,  2.2791e-02, -3.1303e-02,\n",
       "         2.5084e-02, -8.7316e-03, -4.6752e-03, -2.5510e-02,  5.0539e-03,\n",
       "        -3.3755e-02, -4.9491e-02,  1.5632e-02, -6.8095e-03,  2.4801e-02,\n",
       "         3.0501e-02,  2.1558e-02,  2.2217e-02, -1.8151e-02, -5.8037e-02,\n",
       "        -1.7057e-02,  1.6337e-02,  2.1900e-02,  2.7349e-02,  3.9039e-02,\n",
       "        -2.3340e-02, -6.2867e-02,  2.0381e-02, -3.0317e-02, -3.2317e-02,\n",
       "        -7.2066e-02, -3.8694e-02, -9.8274e-03,  2.5109e-02,  3.6795e-02,\n",
       "        -1.7297e-02,  3.0267e-02, -1.5756e-02, -3.9794e-02,  2.3033e-02,\n",
       "         3.1124e-02, -1.9287e-02,  1.1844e-02, -4.8268e-03,  4.0782e-02,\n",
       "         1.0385e-03,  1.0464e-01,  1.9063e-02,  8.3571e-03,  2.5337e-03,\n",
       "        -3.1320e-03, -2.7825e-02,  1.8403e-02,  2.0702e-02,  3.6908e-02,\n",
       "         2.7416e-03, -6.0158e-02,  2.9791e-02,  1.8967e-02, -3.4473e-02,\n",
       "         1.5104e-02, -3.6422e-02, -2.4567e-02,  1.1861e-02,  1.5127e-02,\n",
       "        -2.7633e-02, -8.7480e-02, -1.2038e-02,  8.1492e-02,  1.3503e-04,\n",
       "        -7.9989e-03, -8.2924e-02, -1.4916e-03,  2.3558e-02,  7.7108e-03,\n",
       "        -4.4561e-02,  5.7388e-03, -1.6240e-02,  7.5966e-03,  2.8973e-03,\n",
       "         3.0138e-03, -1.8075e-02, -1.6125e-02,  7.4536e-03,  3.0270e-02,\n",
       "         5.3882e-02, -8.8308e-03,  4.1331e-02, -4.5255e-02,  2.6336e-02,\n",
       "        -2.3341e-02, -5.7294e-02,  3.0712e-02,  3.2013e-02, -2.2280e-02,\n",
       "         2.1893e-02, -8.8448e-02, -1.6441e-02, -1.8578e-02,  3.1832e-02,\n",
       "        -5.1782e-02,  1.1617e-02, -4.0834e-03,  4.0055e-02, -2.3541e-02,\n",
       "        -4.2422e-02,  1.2550e-02,  4.6806e-02,  3.4818e-02,  5.2723e-02,\n",
       "         1.2301e-02, -4.1895e-02, -6.2660e-03,  1.3237e-02, -7.7754e-02,\n",
       "        -4.5434e-03, -4.8801e-02,  1.6945e-02,  2.7172e-02, -1.5703e-02,\n",
       "        -1.9612e-02,  5.4214e-02, -1.0483e-02,  3.1630e-07,  1.2421e-02,\n",
       "        -2.5716e-02, -1.3393e-02, -1.1920e-01, -1.7808e-02, -3.8919e-02,\n",
       "         5.4256e-02,  3.0124e-03, -1.1077e-02,  7.0451e-03,  6.0348e-02,\n",
       "        -5.5597e-02,  2.3159e-03, -1.3970e-02,  4.7007e-02,  1.1927e-02,\n",
       "        -1.9803e-02, -2.2539e-02, -1.1684e-02, -4.7200e-03,  8.4690e-03,\n",
       "         3.2068e-02, -2.6291e-02, -5.7842e-02,  2.1788e-02,  1.2782e-02,\n",
       "        -1.2676e-02, -5.4928e-02, -1.7036e-02, -3.7918e-02, -2.4087e-02,\n",
       "         1.8712e-02,  5.3388e-02, -4.9064e-02, -6.8377e-02, -4.9259e-02,\n",
       "        -1.8381e-02, -4.3865e-03, -6.1605e-02,  9.1380e-03, -5.5073e-02,\n",
       "         5.1462e-02, -3.0244e-02, -3.7612e-02, -1.2178e-02, -2.2920e-02,\n",
       "        -5.9107e-03,  5.5501e-02,  3.2965e-02, -2.2425e-02,  6.9093e-03,\n",
       "         5.7825e-03,  1.3703e-02,  3.6597e-02, -1.7868e-02,  4.0320e-02,\n",
       "         3.9890e-02,  2.2399e-02,  3.5767e-02, -4.6013e-02, -2.4015e-02,\n",
       "         6.7122e-03, -5.1820e-02, -4.2709e-02, -5.3403e-02,  7.7334e-03,\n",
       "         2.8628e-02,  3.2577e-34, -5.5594e-02, -1.8639e-02, -1.0194e-03,\n",
       "        -3.2433e-02,  1.4610e-02,  9.1389e-03, -2.7832e-02,  1.9247e-02,\n",
       "        -2.2116e-02, -2.3214e-02,  1.2569e-02], device='cuda:0')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Work\\NanoRAG\\venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1142: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import util, SentenceTransformer\n",
    "\n",
    "embedding_model = SentenceTransformer(model_name_or_path=\"all-mpnet-base-v2\", \n",
    "                                      device=device) # choose the device to load the model to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: macronutrients functions\n",
      "Time take to get scores on 1680 embeddings: 0.00042 seconds.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.return_types.topk(\n",
       "values=tensor([0.6926, 0.6738, 0.6646, 0.6536, 0.6473], device='cuda:0'),\n",
       "indices=tensor([42, 47, 41, 51, 46], device='cuda:0'))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Define the query\n",
    "# Note: This could be anything. But since we're working with a nutrition textbook, we'll stick with nutrition-based queries.\n",
    "query = \"macronutrients functions\"\n",
    "print(f\"Query: {query}\")\n",
    "\n",
    "# 2. Embed the query to the same numerical space as the text examples \n",
    "# Note: It's important to embed your query with the same model you embedded your examples with.\n",
    "query_embedding = embedding_model.encode(query, convert_to_tensor=True)\n",
    "\n",
    "# 3. Get similarity scores with the dot product (we'll time this for fun)\n",
    "from time import perf_counter as timer\n",
    "\n",
    "start_time = timer()\n",
    "dot_scores = util.dot_score(a=query_embedding, b=embeddings)[0]\n",
    "end_time = timer()\n",
    "\n",
    "print(f\"Time take to get scores on {len(embeddings)} embeddings: {end_time-start_time:.5f} seconds.\")\n",
    "\n",
    "# 4. Get the top-k results (we'll keep this to 5)\n",
    "top_results_dot_product = torch.topk(dot_scores, k=5)\n",
    "top_results_dot_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings shape: torch.Size([168000, 768])\n",
      "Time take to get scores on 168000 embeddings: 0.00616 seconds.\n"
     ]
    }
   ],
   "source": [
    "larger_embeddings = torch.randn(100*embeddings.shape[0], 768).to(device)\n",
    "print(f\"Embeddings shape: {larger_embeddings.shape}\")\n",
    "\n",
    "# Perform dot product across 168,000 embeddings\n",
    "start_time = timer()\n",
    "dot_scores = util.dot_score(a=query_embedding, b=larger_embeddings)[0]\n",
    "end_time = timer()\n",
    "\n",
    "print(f\"Time take to get scores on {len(larger_embeddings)} embeddings: {end_time-start_time:.5f} seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define helper function to print wrapped text \n",
    "import textwrap\n",
    "\n",
    "def print_wrapped(text, wrap_length=80):\n",
    "    wrapped_text = textwrap.fill(text, wrap_length)\n",
    "    print(wrapped_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: 'macronutrients functions'\n",
      "\n",
      "Results:\n",
      "Score: 0.6926\n",
      "Text:\n",
      "Macronutrients Nutrients that are needed in large amounts are called\n",
      "macronutrients. There are three classes of macronutrients: carbohydrates,\n",
      "lipids, and proteins. These can be metabolically processed into cellular energy.\n",
      "The energy from macronutrients comes from their chemical bonds. This chemical\n",
      "energy is converted into cellular energy that is then utilized to perform work,\n",
      "allowing our bodies to conduct their basic functions. A unit of measurement of\n",
      "food energy is the calorie. On nutrition food labels the amount given for\n",
      "“calories” is actually equivalent to each calorie multiplied by one thousand. A\n",
      "kilocalorie (one thousand calories, denoted with a small “c”) is synonymous with\n",
      "the “Calorie” (with a capital “C”) on nutrition food labels. Water is also a\n",
      "macronutrient in the sense that you require a large amount of it, but unlike the\n",
      "other macronutrients, it does not yield calories. Carbohydrates Carbohydrates\n",
      "are molecules composed of carbon, hydrogen, and oxygen.\n",
      "Page number: 5\n",
      "\n",
      "\n",
      "Score: 0.6738\n",
      "Text:\n",
      "Water There is one other nutrient that we must have in large quantities: water.\n",
      "Water does not contain carbon, but is composed of two hydrogens and one oxygen\n",
      "per molecule of water. More than 60 percent of your total body weight is water.\n",
      "Without it, nothing could be transported in or out of the body, chemical\n",
      "reactions would not occur, organs would not be cushioned, and body temperature\n",
      "would fluctuate widely. On average, an adult consumes just over two liters of\n",
      "water per day from food and drink combined. Since water is so critical for\n",
      "life’s basic processes, the amount of water input and output is supremely\n",
      "important, a topic we will explore in detail in Chapter 4. Micronutrients\n",
      "Micronutrients are nutrients required by the body in lesser amounts, but are\n",
      "still essential for carrying out bodily functions. Micronutrients include all\n",
      "the essential minerals and vitamins. There are sixteen essential minerals and\n",
      "thirteen vitamins (See Table 1.1 “Minerals and Their Major Functions” and Table\n",
      "1.2 “Vitamins and Their Major Functions” for a complete list and their major\n",
      "functions). In contrast to carbohydrates, lipids, and proteins, micronutrients\n",
      "are not sources of energy (calories), but they assist in the process as\n",
      "cofactors or components of enzymes (i.e., coenzymes).\n",
      "Page number: 8\n",
      "\n",
      "\n",
      "Score: 0.6646\n",
      "Text:\n",
      "Learning Objectives By the end of this chapter, you will be able to: • Describe\n",
      "basic concepts in nutrition • Describe factors that affect your nutritional\n",
      "needs • Describe the importance of research and scientific methods to\n",
      "understanding nutrition What are Nutrients? The foods we eat contain nutrients.\n",
      "Nutrients are substances required by the body to perform its basic functions.\n",
      "Nutrients must be obtained from our diet, since the human body does not\n",
      "synthesize or produce them. Nutrients have one or more of three basic functions:\n",
      "they provide energy, contribute to body structure, and/or regulate chemical\n",
      "processes in the body. These basic functions allow us to detect and respond to\n",
      "environmental surroundings, move, excrete wastes, respire (breathe), grow, and\n",
      "reproduce. There are six classes of nutrients required for the body to function\n",
      "and maintain overall health. These are carbohydrates, lipids, proteins, water,\n",
      "vitamins, and minerals. Foods also contain non-nutrients that may be harmful\n",
      "(such as natural toxins common in plant foods and additives like some dyes and\n",
      "preservatives) or beneficial (such as antioxidants). 4 | Introduction\n",
      "Page number: 4\n",
      "\n",
      "\n",
      "Score: 0.6536\n",
      "Text:\n",
      "Vitamins Major Functions Water-soluble Thiamin (B1) Coenzyme, energy metabolism\n",
      "assistance Riboflavin (B2 ) Coenzyme, energy metabolism assistance Niacin (B3)\n",
      "Coenzyme, energy metabolism assistance Pantothenic acid (B5) Coenzyme, energy\n",
      "metabolism assistance Pyridoxine (B6) Coenzyme, amino acid synthesis assistance\n",
      "Biotin (B7) Coenzyme, amino acid and fatty acid metabolism Folate (B9) Coenzyme,\n",
      "essential for growth Cobalamin (B12) Coenzyme, red blood cell synthesis C\n",
      "(ascorbic acid) Collagen synthesis, antioxidant Fat-soluble A Vision,\n",
      "reproduction, immune system function D Bone and teeth health maintenance, immune\n",
      "system function E Antioxidant, cell membrane protection K Bone and teeth health\n",
      "maintenance, blood clotting Vitamin deficiencies can cause severe health\n",
      "problems and even death. For example, a deficiency in niacin causes a disease\n",
      "called pellagra, which was common in the early twentieth century in some parts\n",
      "of America. The common signs and symptoms of pellagra are known as the\n",
      "“4D’s—diarrhea, dermatitis, dementia, and death.” Until scientists found out\n",
      "that better diets relieved the signs and symptoms of pellagra, many people with\n",
      "the disease ended up hospitalized in insane asylums awaiting death. Other\n",
      "vitamins were also found to prevent certain disorders and diseases such as\n",
      "scurvy (vitamin C), night blindness vitamin A, and rickets (vitamin D). Table\n",
      "1.3 Functions of Nutrients Introduction | 11\n",
      "Page number: 11\n",
      "\n",
      "\n",
      "Score: 0.6473\n",
      "Text:\n",
      "Figure 1.1 The Macronutrie nts: Carbohydrat es, Lipids, Protein, and Water\n",
      "Proteins Proteins are macromolecules composed of chains of subunits called amino\n",
      "acids. Amino acids are simple subunits composed of carbon, oxygen, hydrogen, and\n",
      "nitrogen. Food sources of proteins include meats, dairy products, seafood, and a\n",
      "variety of different plant- based foods, most notably soy. The word protein\n",
      "comes from a Greek word meaning “of primary importance,” which is an apt\n",
      "description of these macronutrients; they are also known colloquially as the\n",
      "“workhorses” of life. Proteins provide four kilocalories of energy per gram;\n",
      "however providing energy is not protein’s most important function. Proteins\n",
      "provide structure to bones, muscles and skin, and play a role in conducting most\n",
      "of the chemical reactions that take place in the body. Scientists estimate that\n",
      "greater than one-hundred thousand different proteins exist within the human\n",
      "body. The genetic codes in DNA are basically protein recipes that determine the\n",
      "order in which 20 different amino acids are bound together to make thousands of\n",
      "specific proteins. Figure 1.1 The Macronutrients: Carbohydrates, Lipids,\n",
      "Protein, and Water Introduction | 7\n",
      "Page number: 7\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Query: '{query}'\\n\")\n",
    "print(\"Results:\")\n",
    "# Loop through zipped together scores and indicies from torch.topk\n",
    "for score, idx in zip(top_results_dot_product[0], top_results_dot_product[1]):\n",
    "    print(f\"Score: {score:.4f}\")\n",
    "    # Print relevant sentence chunk (since the scores are in descending order, the most relevant chunk will be first)\n",
    "    print(\"Text:\")\n",
    "    print_wrapped(pages_and_chunks[idx][\"sentence_chunk\"])\n",
    "    # Print the page number too so we can reference the textbook further (and check the results)\n",
    "    print(f\"Page number: {pages_and_chunks[idx]['page_number']}\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import fitz\n",
    "\n",
    "# Open PDF and load target page\n",
    "pdf_path = \"assets/human-nutrition-text.pdf\" # requires PDF to be downloaded\n",
    "doc = fitz.open(pdf_path)\n",
    "page = doc.load_page(5 + 41) # number of page (our doc starts page numbers on page 41)\n",
    "\n",
    "# Get the image of the page\n",
    "img = page.get_pixmap(dpi=300)\n",
    "\n",
    "# Optional: save the image\n",
    "#img.save(\"output_filename.png\")\n",
    "doc.close()\n",
    "\n",
    "# Convert the Pixmap to a numpy array\n",
    "img_array = np.frombuffer(img.samples_mv, \n",
    "                          dtype=np.uint8).reshape((img.h, img.w, img.n))\n",
    "\n",
    "# Display the image using Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(13, 10))\n",
    "plt.imshow(img_array)\n",
    "plt.title(f\"Query: '{query}' | Most relevant page:\")\n",
    "plt.axis('off') # Turn off axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity measures: dot product and cosine similarity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specifically, embedding vectors which are representations of data with magnitude and direction in high dimensional space (our embedding vectors have 768 dimensions).\n",
    "\n",
    "Two of the most common you'll across are the dot product and cosine similarity.\n",
    "\n",
    "They are quite similar.\n",
    "\n",
    "The main difference is that cosine similarity has a normalization step.\n",
    "\n",
    "| Similarity measure | Description | Code |\n",
    "| ----- | ----- | ----- |\n",
    "| [Dot Product](https://en.wikipedia.org/wiki/Dot_product) | - Measure of magnitude and direction between two vectors<br>- Vectors that are aligned in direction and magnitude have a higher positive value<br>- Vectors that are opposite in direction and magnitude have a higher negative value | [`torch.dot`](https://pytorch.org/docs/stable/generated/torch.dot.html), [`np.dot`](https://numpy.org/doc/stable/reference/generated/numpy.dot.html), [`sentence_transformers.util.dot_score`](https://www.sbert.net/docs/package_reference/util.html#sentence_transformers.util.dot_score) | \n",
    "| [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) | - Vectors get normalized by magnitude/[Euclidean norm](https://en.wikipedia.org/wiki/Norm_(mathematics))/L2 norm so they have unit length and are compared more so on direction<br>- Vectors that are aligned in direction have a value close to 1<br>- Vectors that are opposite in direction have a value close to -1 | [`torch.nn.functional.cosine_similarity`](https://pytorch.org/docs/stable/generated/torch.nn.functional.cosine_similarity.html), [`1 - scipy.spatial.distance.cosine`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cosine.html) (subtract the distance from 1 for similarity measure), [`sentence_transformers.util.cos_sim`](https://www.sbert.net/docs/package_reference/util.html#sentence_transformers.util.cos_sim) |\n",
    "\n",
    "For text similarity, you generally want to use cosine similarity as you are after the semantic measurements (direction) rather than magnitude. \n",
    "\n",
    "In our case, our embedding model `all-mpnet-base-v2` outputs normalized outputs (see the [Hugging Face model card](https://huggingface.co/sentence-transformers/all-mpnet-base-v2#usage-huggingface-transformers) for more on this) so dot product and cosine similarity return the same results. However, dot product is faster due to not need to perform a normalize step.\n",
    "\n",
    "To make things bit more concrete, let's make simple dot product and cosine similarity functions and view their results on different vectors.\n",
    "\n",
    "> **Note:** Similarity measures between vectors and embeddings can be used on any kind of embeddings, not just text embeddings. For example, you could measure image embedding similarity or audio embedding similarity. Or with text and image models like [CLIP](https://github.com/mlfoundations/open_clip), you can measure the similarity between text and image embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot product between vector1 and vector2: tensor(14.)\n",
      "Dot product between vector1 and vector3: tensor(32.)\n",
      "Dot product between vector1 and vector4: tensor(-14.)\n",
      "Cosine similarity between vector1 and vector2: tensor(1.0000)\n",
      "Cosine similarity between vector1 and vector3: tensor(0.9746)\n",
      "Cosine similarity between vector1 and vector4: tensor(-1.0000)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "def dot_product(vector1, vector2):\n",
    "    return torch.dot(vector1, vector2)\n",
    "\n",
    "def cosine_similarity(vector1, vector2):\n",
    "    dot_product = torch.dot(vector1, vector2)\n",
    "\n",
    "    # Get Euclidean/L2 norm of each vector (removes the magnitude, keeps direction)\n",
    "    norm_vector1 = torch.sqrt(torch.sum(vector1**2))\n",
    "    norm_vector2 = torch.sqrt(torch.sum(vector2**2))\n",
    "\n",
    "    return dot_product / (norm_vector1 * norm_vector2)\n",
    "\n",
    "# Example tensors\n",
    "vector1 = torch.tensor([1, 2, 3], dtype=torch.float32)\n",
    "vector2 = torch.tensor([1, 2, 3], dtype=torch.float32)\n",
    "vector3 = torch.tensor([4, 5, 6], dtype=torch.float32)\n",
    "vector4 = torch.tensor([-1, -2, -3], dtype=torch.float32)\n",
    "\n",
    "# Calculate dot product\n",
    "print(\"Dot product between vector1 and vector2:\", dot_product(vector1, vector2))\n",
    "print(\"Dot product between vector1 and vector3:\", dot_product(vector1, vector3))\n",
    "print(\"Dot product between vector1 and vector4:\", dot_product(vector1, vector4))\n",
    "\n",
    "# Calculate cosine similarity\n",
    "print(\"Cosine similarity between vector1 and vector2:\", cosine_similarity(vector1, vector2))\n",
    "print(\"Cosine similarity between vector1 and vector3:\", cosine_similarity(vector1, vector3))\n",
    "print(\"Cosine similarity between vector1 and vector4:\", cosine_similarity(vector1, vector4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functionizing our semantic search pipeline\n",
    "\n",
    "Let's put all of the steps from above for semantic search into a function or two so we can repeat the workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_relevant_resources(query: str,\n",
    "                                embeddings: torch.tensor,\n",
    "                                model: SentenceTransformer=embedding_model,\n",
    "                                n_resources_to_return: int=5,\n",
    "                                print_time: bool=True):\n",
    "    \"\"\"\n",
    "    Embeds a query with model and returns top k scores and indices from embeddings.\n",
    "    \"\"\"\n",
    "\n",
    "    # Embed the query\n",
    "    query_embedding = model.encode(query, \n",
    "                                   convert_to_tensor=True) \n",
    "\n",
    "    # Get dot product scores on embeddings\n",
    "    start_time = timer()\n",
    "    dot_scores = util.dot_score(query_embedding, embeddings)[0]\n",
    "    end_time = timer()\n",
    "\n",
    "    if print_time:\n",
    "        print(f\"[INFO] Time taken to get scores on {len(embeddings)} embeddings: {end_time-start_time:.5f} seconds.\")\n",
    "\n",
    "    scores, indices = torch.topk(input=dot_scores, \n",
    "                                 k=n_resources_to_return)\n",
    "\n",
    "    return scores, indices\n",
    "\n",
    "def print_top_results_and_scores(query: str,\n",
    "                                 embeddings: torch.tensor,\n",
    "                                 pages_and_chunks: list[dict]=pages_and_chunks,\n",
    "                                 n_resources_to_return: int=5):\n",
    "    \"\"\"\n",
    "    Takes a query, retrieves most relevant resources and prints them out in descending order.\n",
    "\n",
    "    Note: Requires pages_and_chunks to be formatted in a specific way (see above for reference).\n",
    "    \"\"\"\n",
    "    \n",
    "    scores, indices = retrieve_relevant_resources(query=query,\n",
    "                                                  embeddings=embeddings,\n",
    "                                                  n_resources_to_return=n_resources_to_return)\n",
    "    \n",
    "    print(f\"Query: {query}\\n\")\n",
    "    print(\"Results:\")\n",
    "    # Loop through zipped together scores and indicies\n",
    "    for score, index in zip(scores, indices):\n",
    "        print(f\"Score: {score:.4f}\")\n",
    "        # Print relevant sentence chunk (since the scores are in descending order, the most relevant chunk will be first)\n",
    "        print_wrapped(pages_and_chunks[index][\"sentence_chunk\"])\n",
    "        # Print the page number too so we can reference the textbook further and check the results\n",
    "        print(f\"Page number: {pages_and_chunks[index]['page_number']}\")\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00006 seconds.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([0.5000, 0.3741, 0.2959, 0.2793, 0.2721], device='cuda:0'),\n",
       " tensor([ 822,  853, 1536, 1555, 1531], device='cuda:0'))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"symptoms of pellagra\"\n",
    "\n",
    "# Get just the scores and indices of top related results\n",
    "scores, indices = retrieve_relevant_resources(query=query,\n",
    "                                              embeddings=embeddings)\n",
    "scores, indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00007 seconds.\n",
      "Query: symptoms of pellagra\n",
      "\n",
      "Results:\n",
      "Score: 0.5000\n",
      "Niacin deficiency is commonly known as pellagra and the symptoms include\n",
      "fatigue, decreased appetite, and indigestion. These symptoms are then commonly\n",
      "followed by the four D’s: diarrhea, dermatitis, dementia, and sometimes death.\n",
      "Figure 9.12 Conversion of Tryptophan to Niacin Water-Soluble Vitamins | 565\n",
      "Page number: 565\n",
      "\n",
      "\n",
      "Score: 0.3741\n",
      "car. Does it drive faster with a half-tank of gas or a full one?It does not\n",
      "matter; the car drives just as fast as long as it has gas. Similarly, depletion\n",
      "of B vitamins will cause problems in energy metabolism, but having more than is\n",
      "required to run metabolism does not speed it up. Buyers of B-vitamin supplements\n",
      "beware; B vitamins are not stored in the body and all excess will be flushed\n",
      "down the toilet along with the extra money spent. B vitamins are naturally\n",
      "present in numerous foods, and many other foods are enriched with them. In the\n",
      "United States, B-vitamin deficiencies are rare; however in the nineteenth\n",
      "century some vitamin-B deficiencies plagued many people in North America. Niacin\n",
      "deficiency, also known as pellagra, was prominent in poorer Americans whose main\n",
      "dietary staple was refined cornmeal. Its symptoms were severe and included\n",
      "diarrhea, dermatitis, dementia, and even death. Some of the health consequences\n",
      "of pellagra are the result of niacin being in insufficient supply to support the\n",
      "body’s metabolic functions.\n",
      "Page number: 591\n",
      "\n",
      "\n",
      "Score: 0.2959\n",
      "The carbon dioxide gas bubbles infiltrate the stretchy gluten, giving bread its\n",
      "porosity and tenderness. For those who are sensitive to gluten, it is good to\n",
      "know that corn, millet, buckwheat, and oats do not contain the proteins that\n",
      "make gluten. However, some people who have celiac disease also may have a\n",
      "response to products containing oats. This is most likely the result of cross-\n",
      "contamination of grains during harvest, storage, packaging, and processing.\n",
      "Celiac disease is most common in people of European descent and is rare in\n",
      "people of African American, Japanese, and Chinese descent. It is much more\n",
      "prevalent in women and in people with Type 1 diabetes, autoimmune thyroid\n",
      "disease, and Down and Turner syndromes. Symptoms can range from mild to severe\n",
      "and can include pale, fatty, loose stools, gastrointestinal upset, abdominal\n",
      "pain, weight loss and, in children, a failure to grow and thrive. The symptoms\n",
      "can appear in infancy or much later in life, even Nutrition, Health and Disease\n",
      "| 1079\n",
      "Page number: 1079\n",
      "\n",
      "\n",
      "Score: 0.2793\n",
      "Image by BruceBlaus/ CC BY 4.0 When the vertebral bone tissue is weakened, it\n",
      "can cause the spine to curve. The increase in spine curvature not only causes\n",
      "pain, but also decreases a person’s height. Curvature of the upper spine\n",
      "produces what is called Dowager’s hump, also known as kyphosis. Severe upper-\n",
      "spine deformity can compress the chest cavity and cause difficulty breathing. It\n",
      "may also cause abdominal pain and loss of appetite because of the increased\n",
      "pressure on the abdomen. 1090 | Nutrition, Health and Disease\n",
      "Page number: 1090\n",
      "\n",
      "\n",
      "Score: 0.2721\n",
      "esophagus and cause irritation. It is estimated that GERD affects 25 to 35\n",
      "percent of the US population. An analysis of several studies published in the\n",
      "August 2005 issue of Annals of Internal Medicine concludes that GERD is much\n",
      "more prevalent in people who are obese.1 The most common GERD symptom is\n",
      "heartburn, but people with GERD may also experience regurgitation (flow of the\n",
      "stomach’s acidic contents into the mouth), frequent coughing, and trouble\n",
      "swallowing. There are other causative factors of GERD that may be separate from\n",
      "or intertwined with obesity. The sphincter that separates the stomach’s internal\n",
      "contents from the esophagus often does not function properly and acidic gastric\n",
      "contents seep upward. Sometimes the peristaltic contractions of the esophagus\n",
      "are also sluggish and compromise the clearance of acidic contents. In addition\n",
      "to having an unbalanced, high-fat diet, some people with GERD are sensitive to\n",
      "particular foods—chocolate, garlic, spicy foods, fried foods, and tomato-based\n",
      "foods—which worsen symptoms. Drinks containing alcohol or caffeine may also\n",
      "worsen GERD symptoms. GERD is diagnosed most often by a history of the frequency\n",
      "of recurring symptoms. A more proper diagnosis can be made when a doctor inserts\n",
      "a small device into the lower esophagus that measures the acidity of the\n",
      "contents during one’s daily activities.\n",
      "Page number: 1077\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print out the texts of the top scores\n",
    "print_top_results_and_scores(query=query,\n",
    "                             embeddings=embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Semantic search/vector search extensions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is small and allows for an exhaustive search (comparing the query to *every* possible result) but if you start to work with large scale datasets with hundred of thousands, millions or even billions of vectors, you'll want to implement an index.\n",
    "\n",
    "You can think of an index as sorting your embeddings before you search through them.\n",
    "\n",
    "So it narrows down the search space.\n",
    "\n",
    "One of the most popular indexing libraries is [Faiss](https://github.com/facebookresearch/faiss). \n",
    "\n",
    "Faiss is open-source and was originally created by Facebook to deal with internet-scale vectors and implements many algorithms such as [HNSW](https://arxiv.org/abs/1603.09320) (Hierarchical Naviganle Small Worlds)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting an LLM for local generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which LLM should I use?**\n",
    "\n",
    "There are many LLMs available.\n",
    "\n",
    "Two of the main questions to ask from this is:\n",
    "1. Do I want it to run locally? \n",
    "2. If yes, how much compute power can I dedicate?\n",
    "\n",
    "If you're after the absolute best performance, you'll likely want to use an API (not running locally) such as GPT-4 or Claude 3. However, this comes with the tradeoff of sending your data away and then awaiting a response.\n",
    "\n",
    "For our case, since we want to set up a local pipeline and run it on our own GPU, we'd answer \"yes\" to the first question and then the second question will depend on what hardware we have available.\n",
    "\n",
    "To find open-source LLMs, one great resource is the [Hugging Face open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n",
    "\n",
    "The leaderboard compares many of the latest and greatest LLMs on various benchmarks.\n",
    "\n",
    "Another great resource is [TheBloke on Hugging Face](https://huggingface.co/TheBloke), an account which provides an extensive range of quantized (models that have been made smaller) LLMs.\n",
    "\n",
    "A rule of thumb for LLMs (and deep learning models in general) is that the higher the number of parameters, the better the model performs. \n",
    "\n",
    "It may be tempting to go for the largest size model (e.g. a 70B parameter model rather than a 7B parameter model) but a larger size model may not be able to run on your available hardware.\n",
    "\n",
    "The following table gives an insight into how much GPU memory you'll need to load an LLM with different sizes and different levels of [numerical precision](https://en.wikipedia.org/wiki/Precision_(computer_science)).\n",
    "\n",
    "They are based on the fact that 1 float32 value (e.g. `0.69420`) requires 4 bytes of memory and 1GB is approximately 1,000,000,000 (one billion) bytes.\n",
    "\n",
    "| Model Size (Billion Parameters) | Float32 VRAM (GB) | Float16 VRAM (GB) | 8-bit VRAM (GB) | 4-bit VRAM (GB) |\n",
    "|-----|-----|-----|-----|-----|\n",
    "| 1B                              | ~4                | ~2                | ~1              | ~0.5            |\n",
    "| 7B (e.g., [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b), [Gemma 7B](https://huggingface.co/google/gemma-7b-it), [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1))             | ~28               | ~14               | ~7              | ~3.5            |\n",
    "| 10B                             | ~40               | ~20               | ~10             | ~5              |\n",
    "| 70B (e.g, Llama 2 70B)          | ~280              | ~140              | ~70             | ~35             |\n",
    "| 100B                            | ~400              | ~200              | ~100            | ~50             |\n",
    "| 175B                            | ~700              | ~350              | ~175            | ~87.5           |\n",
    "\n",
    "<br>\n",
    "\n",
    "> **Note:** Loading a model in a lower precision (e.g. 8-bit instead of float16) generally lowers performance. Lower precision can help to reduce computing requirements, however sometimes the performance degradation in terms of model output can be substantial. Finding the right speed/performance tradeoff will often require many experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking local GPU memory availability\n",
    "\n",
    "Let's find out what hardware we've got available and see what kind of model(s) we'll be able to load.\n",
    "\n",
    "> **Note:** You can also check this with the `!nvidia-smi` command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available GPU memory: 8 GB\n"
     ]
    }
   ],
   "source": [
    "# Get GPU available memory\n",
    "import torch\n",
    "gpu_memory_bytes = torch.cuda.get_device_properties(0).total_memory\n",
    "gpu_memory_gb = round(gpu_memory_bytes / (2**30))\n",
    "print(f\"Available GPU memory: {gpu_memory_gb} GB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following table shows different amounts of GPU memory requirements for different verions of the Gemma LLMs with varying levels of precision.\n",
    "\n",
    "| Model             | Precision | Min-Memory (Bytes) | Min-Memory (MB) | Min-Memory (GB) | Recommended Memory (GB) | Hugging Face ID |\n",
    "|-------------------|-----------|----------------|-------------|-------------| ----- | ----- |\n",
    "| [Gemma 2B](https://huggingface.co/google/gemma-2b-it)          | 4-bit     | 2,106,749,952  | 2009.15     | 1.96        | ~5.0 | [`gemma-2b`](https://huggingface.co/google/gemma-2b) or [`gemma-2b-it`](https://huggingface.co/google/gemma-2b-it) for instruction tuned version | \n",
    "| Gemma 2B          | Float16   | 5,079,453,696  | 4844.14     | 4.73        | ~8.0 | Same as above |\n",
    "| [Gemma 7B](https://huggingface.co/google/gemma-7b-it)          | 4-bit     | 5,515,859,968  | 5260.33     | 5.14        | ~8.0 | [`gemma-7b`](https://huggingface.co/google/gemma-7b) or [`gemma-7b-it`](https://huggingface.co/google/gemma-7b-it) for instruction tuned version |\n",
    "| Gemma 7B          | Float16   | 17,142,470,656 | 16348.33    | 15.97       | ~19 | Same as above |\n",
    "\n",
    "> **Note:** `gemma-7b-it` means \"instruction tuned\", as in, a base LLM (`gemma-7b`) has been fine-tuned to follow instructions, similar to [`Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) and [`Mistral-7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).\n",
    "> \n",
    "> There are also further quantized and smaller variants of Gemma (and other LLMs) available in various formats such as GGUF. You can see many of these on [TheBloke account on Hugging Face](https://huggingface.co/TheBloke).\n",
    "> \n",
    "> The version of LLM you choose to use will be largely based on project requirements and experimentation.\n",
    "\n",
    "Based on the table above, let's write a simple if/else statement which recommends which Gemma variant we should look into using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU memory: 8 | Recommended model: Gemma 2B in 4-bit precision.\n",
      "use_quantization_config set to: False\n",
      "model_id set to: google/gemma-2b-it\n"
     ]
    }
   ],
   "source": [
    "# Note: the following is Gemma focused, however, there are more and more LLMs of the 2B and 7B size appearing for local use.\n",
    "if gpu_memory_gb < 5.1:\n",
    "    print(f\"Your available GPU memory is {gpu_memory_gb}GB, you may not have enough memory to run a Gemma LLM locally without quantization.\")\n",
    "elif gpu_memory_gb < 8.1:\n",
    "    print(f\"GPU memory: {gpu_memory_gb} | Recommended model: Gemma 2B in 4-bit precision.\")\n",
    "    use_quantization_config = False \n",
    "    model_id = \"google/gemma-2b-it\"\n",
    "elif gpu_memory_gb < 19.0:\n",
    "    print(f\"GPU memory: {gpu_memory_gb} | Recommended model: Gemma 2B in float16 or Gemma 7B in 4-bit precision.\")\n",
    "    use_quantization_config = False \n",
    "    model_id = \"google/gemma-2b-it\"\n",
    "elif gpu_memory_gb > 19.0:\n",
    "    print(f\"GPU memory: {gpu_memory_gb} | Recommend model: Gemma 7B in 4-bit or float16 precision.\")\n",
    "    use_quantization_config = False \n",
    "    model_id = \"google/gemma-7b-it\"\n",
    "\n",
    "print(f\"use_quantization_config set to: {use_quantization_config}\")\n",
    "print(f\"model_id set to: {model_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading an LLM locally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load our LLM, we're going to need a few things:\n",
    "1. A quantization config (optional) - This will determine whether or not we load the model in 4bit precision for lower memory usage. The we can create this with the [`transformers.BitsAndBytesConfig`](https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/quantization#transformers.BitsAndBytesConfig) class (requires installing the [`bitsandbytes` library](https://github.com/TimDettmers/bitsandbytes)).\n",
    "2. A model ID - This is the reference Hugging Face model ID which will determine which tokenizer and model gets used. For example `gemma-7b-it`.\n",
    "3. A tokenzier - This is what will turn our raw text into tokens ready for the model. We can create it using the [`transformers.AutoTokenzier.from_pretrained`](https://huggingface.co/docs/transformers/v4.38.2/en/model_doc/auto#transformers.AutoTokenizer) method and passing it our model ID.\n",
    "4. An LLM model - Again, using our model ID we can load a specific LLM model. To do so we can use the [`transformers.AutoModelForCausalLM.from_pretrained`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM.from_pretrained) method and passing it our model ID as well as other various parameters.\n",
    "\n",
    "As a bonus, we'll check if [Flash Attention 2](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2) is available using `transformers.utils.is_flash_attn_2_available()`. Flash Attention 2 speeds up the attention mechanism in Transformer architecture models (which is what many modern LLMs are based on, including Gemma). So if it's available and the model is supported (not all models support Flash Attention 2), we'll use it. If it's not available, you can install it by following the instructions on the [GitHub repo](https://github.com/Dao-AILab/flash-attention). \n",
    "\n",
    "> **Note:** Flash Attention 2 currently works on NVIDIA GPUs with a compute capability score of 8.0+ (Ampere, Ada Lovelace, Hopper architectures). We can check our GPU compute capability score with [`torch.cuda.get_device_capability(0)`](https://pytorch.org/docs/stable/generated/torch.cuda.get_device_capability.html). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using attention implementation: sdpa\n",
      "[INFO] Using model_id: google/gemma-2b-it\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbf31d68d7d741269aebb3cf106cd2de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from transformers.utils import is_flash_attn_2_available \n",
    "\n",
    "# 1. Create quantization config for smaller model loading (optional)\n",
    "# Requires !pip install bitsandbytes accelerate, see: https://github.com/TimDettmers/bitsandbytes, https://huggingface.co/docs/accelerate/\n",
    "# For models that require 4-bit quantization (use this if you have low GPU memory available)\n",
    "from transformers import BitsAndBytesConfig\n",
    "quantization_config = BitsAndBytesConfig(load_in_4bit=True,\n",
    "                                         bnb_4bit_compute_dtype=torch.float16)\n",
    "\n",
    "# Bonus: Setup Flash Attention 2 for faster inference, default to \"sdpa\" or \"scaled dot product attention\" if it's not available\n",
    "# Flash Attention 2 requires NVIDIA GPU compute capability of 8.0 or above, see: https://developer.nvidia.com/cuda-gpus\n",
    "# Requires !pip install flash-attn, see: https://github.com/Dao-AILab/flash-attention \n",
    "if (is_flash_attn_2_available()) and (torch.cuda.get_device_capability(0)[0] >= 8):\n",
    "  attn_implementation = \"flash_attention_2\"\n",
    "else:\n",
    "  attn_implementation = \"sdpa\"\n",
    "print(f\"[INFO] Using attention implementation: {attn_implementation}\")\n",
    "\n",
    "# 2. Pick a model we'd like to use (this will depend on how much GPU memory you have available)\n",
    "#model_id = \"google/gemma-7b-it\"\n",
    "model_id = model_id # (we already set this above)\n",
    "print(f\"[INFO] Using model_id: {model_id}\")\n",
    "\n",
    "# 3. Instantiate tokenizer (tokenizer turns text into numbers ready for the model) \n",
    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_id)\n",
    "\n",
    "# 4. Instantiate the model\n",
    "llm_model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id, \n",
    "                                                 torch_dtype=torch.float16, # datatype to use, we want float16\n",
    "                                                 quantization_config=quantization_config if use_quantization_config else None,\n",
    "                                                 low_cpu_mem_usage=False, # use full memory \n",
    "                                                 attn_implementation=attn_implementation) # which attention version to use\n",
    "\n",
    "if not use_quantization_config: # quantization takes care of device setting automatically, so if it's not used, send model to GPU \n",
    "    llm_model.to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GemmaForCausalLM(\n",
       "  (model): GemmaModel(\n",
       "    (embed_tokens): Embedding(256000, 2048, padding_idx=0)\n",
       "    (layers): ModuleList(\n",
       "      (0-17): 18 x GemmaDecoderLayer(\n",
       "        (self_attn): GemmaSdpaAttention(\n",
       "          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
       "          (k_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
       "          (v_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
       "          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
       "          (rotary_emb): GemmaRotaryEmbedding()\n",
       "        )\n",
       "        (mlp): GemmaMLP(\n",
       "          (gate_proj): Linear(in_features=2048, out_features=16384, bias=False)\n",
       "          (up_proj): Linear(in_features=2048, out_features=16384, bias=False)\n",
       "          (down_proj): Linear(in_features=16384, out_features=2048, bias=False)\n",
       "          (act_fn): GELUActivation()\n",
       "        )\n",
       "        (input_layernorm): GemmaRMSNorm()\n",
       "        (post_attention_layernorm): GemmaRMSNorm()\n",
       "      )\n",
       "    )\n",
       "    (norm): GemmaRMSNorm()\n",
       "  )\n",
       "  (lm_head): Linear(in_features=2048, out_features=256000, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2506172416"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_model_num_params(model: torch.nn.Module):\n",
    "    return sum([param.numel() for param in model.parameters()])\n",
    "\n",
    "get_model_num_params(llm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_mem_bytes': 5079453696, 'model_mem_mb': 4844.14, 'model_mem_gb': 4.73}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_model_mem_size(model: torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Get how much memory a PyTorch model takes up.\n",
    "\n",
    "    See: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822\n",
    "    \"\"\"\n",
    "    # Get model parameters and buffer sizes\n",
    "    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])\n",
    "    mem_buffers = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])\n",
    "\n",
    "    # Calculate various model sizes\n",
    "    model_mem_bytes = mem_params + mem_buffers # in bytes\n",
    "    model_mem_mb = model_mem_bytes / (1024**2) # in megabytes\n",
    "    model_mem_gb = model_mem_bytes / (1024**3) # in gigabytes\n",
    "\n",
    "    return {\"model_mem_bytes\": model_mem_bytes,\n",
    "            \"model_mem_mb\": round(model_mem_mb, 2),\n",
    "            \"model_mem_gb\": round(model_mem_gb, 2)}\n",
    "\n",
    "get_model_mem_size(llm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating text with our LLM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can generate text with our LLM `model` instance by calling the [`generate()` method](https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/text_generation#transformers.GenerationConfig) (this method has plenty of options to pass into it alongside the text) on it and passing it a tokenized input.\n",
    "\n",
    "The tokenized input comes from passing a string of text to our `tokenizer`.\n",
    "\n",
    "In this case, our `tokenizer` has a [`apply_chat_template()` method](https://huggingface.co/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template) which can prepare our input text in the right format for the model.\n",
    "\n",
    "Let's try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input text:\n",
      "What are the macronutrients, and what roles do they play in the human body?\n",
      "\n",
      "Prompt (formatted):\n",
      "<bos><start_of_turn>user\n",
      "What are the macronutrients, and what roles do they play in the human body?<end_of_turn>\n",
      "<start_of_turn>model\n",
      "\n"
     ]
    }
   ],
   "source": [
    "input_text = \"What are the macronutrients, and what roles do they play in the human body?\"\n",
    "print(f\"Input text:\\n{input_text}\")\n",
    "\n",
    "# Create prompt template for instruction-tuned model\n",
    "dialogue_template = [\n",
    "    {\"role\": \"user\",\n",
    "     \"content\": input_text}\n",
    "]\n",
    "\n",
    "# Apply the chat template\n",
    "prompt = tokenizer.apply_chat_template(conversation=dialogue_template,\n",
    "                                       tokenize=False, # keep as raw text (not tokenized)\n",
    "                                       add_generation_prompt=True)\n",
    "print(f\"\\nPrompt (formatted):\\n{prompt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n",
      "Model input (tokenized):\n",
      "{'input_ids': tensor([[     2,      2,    106,   1645,    108,   1841,    708,    573, 186809,\n",
      "         184592, 235269,    578,   1212,  16065,    749,    984,   1554,    575,\n",
      "            573,   3515,   2971, 235336,    107,    108,    106,   2516,    108]],\n",
      "       device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1]], device='cuda:0')}\n",
      "\n",
      "Model output (tokens):\n",
      "tensor([     2,      2,    106,   1645,    108,   1841,    708,    573, 186809,\n",
      "        184592, 235269,    578,   1212,  16065,    749,    984,   1554,    575,\n",
      "           573,   3515,   2971, 235336,    107,    108,    106,   2516,    108,\n",
      "         21404, 235269,   1517, 235303, 235256,    476,  25497,    576,    573,\n",
      "        186809, 184592,    578,   1024,  16065,    575,    573,   3515,   2971,\n",
      "        235292,    109,    688,  12298,   1695, 184592,  66058,    109, 235287,\n",
      "          5231, 156615,  56227,  66058,    108,    141, 235287,  34428,   4134,\n",
      "           604,    573,   2971, 235303, 235256,   5999,    578,  29703, 235265,\n",
      "           108,    141, 235287, 110165,  56227,    708,    573,   7920,   4303,\n",
      "           576,   4134,    604,   1546,   5999, 235265,    108,    141, 235287,\n",
      "         25280,  72780,    708,   1941,    674,   1987,   5543,    577,  55997,\n",
      "        235269,   1582,    685,   3733,  29907, 235269,  16803, 235269,    578,\n",
      "         19574, 235265,    108,    141, 235287,  13702,  72780,    708,   1941,\n",
      "           674,    708,   7290, 122712, 235269,   1582,    685,   9347, 235269,\n",
      "         57634, 235269,    578, 109955, 235265,    109, 235287,   5231, 216954,\n",
      "         66058,    108,    141, 235287,   8108,    578,  12158,  29703, 235269,\n",
      "         44760, 235269,    578,  53186, 235265,    108,    141, 235287,  96084,\n",
      "           708,   8727,    604,  24091,   1411, 235269,  42696,   4584, 235269,\n",
      "           578,  14976,  12158, 235265,    108,    141, 235287,   2456,    708,\n",
      "          2167,   5088,    576,  20361, 235269,   1853,    675,   3724,   7257,\n",
      "        235265,    109, 235287,   5231, 235311,   1989,  66058,    108,    141,\n",
      "        235287,  34428,   4134, 235269,  38823, 235269,    578,   1707,  33398,\n",
      "         48765, 235265,    108,    141, 235287,  41137,  61926,   3707,  21361,\n",
      "          4684, 235269,  59269, 235269,  22606, 235269,    578,  15741, 235265,\n",
      "           108,    141, 235287,   3776,  61926,    798,  12310,  45365,   5902,\n",
      "           578,   4740,    573,   5685,    576,   3760,   7197, 235265,    109,\n",
      "           688,  33771,    576,  97586, 184592,    575,    573,   9998,  14427,\n",
      "         66058,    109, 235287,   5231,  23920,   4584,  66058, 110165,  56227,\n",
      "        235269,  20361, 235269,    578,  61926,   3658,    573,   2971,    675,\n",
      "          4134, 235265,    108, 235287,   5231,  25251,    578,  68808,  29703,\n",
      "         66058,  96084,    708,   8727,    604,   4547,    578,  68808,  29703,\n",
      "        235269,   3359,  22488, 235269], device='cuda:0')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "\n",
    "# Tokenize the input text (turn it into numbers) and send it to GPU\n",
    "input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "print(f\"Model input (tokenized):\\n{input_ids}\\n\")\n",
    "\n",
    "# Generate outputs passed on the tokenized input\n",
    "# See generate docs: https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/text_generation#transformers.GenerationConfig \n",
    "outputs = llm_model.generate(**input_ids,\n",
    "                             max_new_tokens=256) # define the maximum number of new tokens to create\n",
    "print(f\"Model output (tokens):\\n{outputs[0]}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model output (decoded):\n",
      "<bos><bos><start_of_turn>user\n",
      "What are the macronutrients, and what roles do they play in the human body?<end_of_turn>\n",
      "<start_of_turn>model\n",
      "Sure, here's a breakdown of the macronutrients and their roles in the human body:\n",
      "\n",
      "**Macronutrients:**\n",
      "\n",
      "* **Carbohydrates:**\n",
      "    * Provide energy for the body's cells and tissues.\n",
      "    * Carbohydrates are the primary source of energy for most cells.\n",
      "    * Complex carbohydrates are those that take longer to digest, such as whole grains, fruits, and vegetables.\n",
      "    * Simple carbohydrates are those that are quickly digested, such as sugar, starch, and lactose.\n",
      "\n",
      "* **Proteins:**\n",
      "    * Build and repair tissues, enzymes, and hormones.\n",
      "    * Proteins are essential for immune function, hormone production, and tissue repair.\n",
      "    * There are different types of proteins, each with specific functions.\n",
      "\n",
      "* **Fats:**\n",
      "    * Provide energy, insulation, and help absorb vitamins.\n",
      "    * Healthy fats include olive oil, avocado, nuts, and seeds.\n",
      "    * Trans fats can raise cholesterol levels and increase the risk of heart disease.\n",
      "\n",
      "**Roles of Macronutrients in the Human Body:**\n",
      "\n",
      "* **Energy production:** Carbohydrates, proteins, and fats provide the body with energy.\n",
      "* **Building and repairing tissues:** Proteins are essential for building and repairing tissues, including muscles,\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Decode the output tokens to text\n",
    "outputs_decoded = tokenizer.decode(outputs[0])\n",
    "print(f\"Model output (decoded):\\n{outputs_decoded}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input text: What are the macronutrients, and what roles do they play in the human body?\n",
      "\n",
      "Output text:\n",
      "Sure, here's a breakdown of the macronutrients and their roles in the human body:\n",
      "\n",
      "**Macronutrients:**\n",
      "\n",
      "* **Carbohydrates:**\n",
      "    * Provide energy for the body's cells and tissues.\n",
      "    * Carbohydrates are the primary source of energy for most cells.\n",
      "    * Complex carbohydrates are those that take longer to digest, such as whole grains, fruits, and vegetables.\n",
      "    * Simple carbohydrates are those that are quickly digested, such as sugar, starch, and lactose.\n",
      "\n",
      "* **Proteins:**\n",
      "    * Build and repair tissues, enzymes, and hormones.\n",
      "    * Proteins are essential for immune function, hormone production, and tissue repair.\n",
      "    * There are different types of proteins, each with specific functions.\n",
      "\n",
      "* **Fats:**\n",
      "    * Provide energy, insulation, and help absorb vitamins.\n",
      "    * Healthy fats include olive oil, avocado, nuts, and seeds.\n",
      "    * Trans fats can raise cholesterol levels and increase the risk of heart disease.\n",
      "\n",
      "**Roles of Macronutrients in the Human Body:**\n",
      "\n",
      "* **Energy production:** Carbohydrates, proteins, and fats provide the body with energy.\n",
      "* **Building and repairing tissues:** Proteins are essential for building and repairing tissues, including muscles,\n"
     ]
    }
   ],
   "source": [
    "print(f\"Input text: {input_text}\\n\")\n",
    "print(f\"Output text:\\n{outputs_decoded.replace(prompt, '').replace('<bos>', '').replace('<eos>', '')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nutrition-style questions generated with GPT4\n",
    "gpt4_questions = [\n",
    "    \"What are the macronutrients, and what roles do they play in the human body?\",\n",
    "    \"How do vitamins and minerals differ in their roles and importance for health?\",\n",
    "    \"Describe the process of digestion and absorption of nutrients in the human body.\",\n",
    "    \"What role does fibre play in digestion? Name five fibre containing foods.\",\n",
    "    \"Explain the concept of energy balance and its importance in weight management.\"\n",
    "]\n",
    "\n",
    "# Manually created question list\n",
    "manual_questions = [\n",
    "    \"How often should infants be breastfed?\",\n",
    "    \"What are symptoms of pellagra?\",\n",
    "    \"How does saliva help with digestion?\",\n",
    "    \"What is the RDI for protein per day?\",\n",
    "    \"water soluble vitamins\"\n",
    "]\n",
    "\n",
    "query_list = gpt4_questions + manual_questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: What is the RDI for protein per day?\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00067 seconds.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([0.6793, 0.6771, 0.6418, 0.6109, 0.5908], device='cuda:0'),\n",
       " tensor([ 609,  607, 1007,  608, 1005], device='cuda:0'))"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "query = random.choice(query_list)\n",
    "\n",
    "print(f\"Query: {query}\")\n",
    "\n",
    "# Get just the scores and indices of top related results\n",
    "scores, indices = retrieve_relevant_resources(query=query,\n",
    "                                              embeddings=embeddings)\n",
    "scores, indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Augmenting our prompt with context items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we'd like to do with augmentation is take the results from our search for relevant resources and put them into the prompt that we pass to our LLM.\n",
    "\n",
    "In essence, we start with a base prompt and update it with context text.\n",
    "\n",
    "We'll apply the dialogue and chat template to our prompt before returning it as well.\n",
    "\n",
    "> **Note:** The process of augmenting or changing a prompt to an LLM is known as prompt engineering. And the best way to do it is an active area of research. For a comprehensive guide on different prompt engineering techniques, I'd recommend the Prompt Engineering Guide ([promptingguide.ai](https://www.promptingguide.ai/)), [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering) and the paper [Prompt Design and Engineering: Introduction and Advanced Models](https://arxiv.org/abs/2401.14423)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prompt_formatter(query: str, \n",
    "                     context_items: list[dict]) -> str:\n",
    "    \"\"\"\n",
    "    Augments query with text-based context from context_items.\n",
    "    \"\"\"\n",
    "    # Join context items into one dotted paragraph\n",
    "    context = \"- \" + \"\\n- \".join([item[\"sentence_chunk\"] for item in context_items])\n",
    "\n",
    "    # Create a base prompt with examples to help the model\n",
    "    # Note: this is very customizable, I've chosen to use 3 examples of the answer style we'd like.\n",
    "    # We could also write this in a txt file and import it in if we wanted.\n",
    "    base_prompt = \"\"\"Based on the following context items, please answer the query.\n",
    "Give yourself room to think by extracting relevant passages from the context before answering the query.\n",
    "Don't return the thinking, only return the answer.\n",
    "Make sure your answers are as explanatory as possible.\n",
    "Use the following examples as reference for the ideal answer style.\n",
    "\\nExample 1:\n",
    "Query: What are the fat-soluble vitamins?\n",
    "Answer: The fat-soluble vitamins include Vitamin A, Vitamin D, Vitamin E, and Vitamin K. These vitamins are absorbed along with fats in the diet and can be stored in the body's fatty tissue and liver for later use. Vitamin A is important for vision, immune function, and skin health. Vitamin D plays a critical role in calcium absorption and bone health. Vitamin E acts as an antioxidant, protecting cells from damage. Vitamin K is essential for blood clotting and bone metabolism.\n",
    "\\nExample 2:\n",
    "Query: What are the causes of type 2 diabetes?\n",
    "Answer: Type 2 diabetes is often associated with overnutrition, particularly the overconsumption of calories leading to obesity. Factors include a diet high in refined sugars and saturated fats, which can lead to insulin resistance, a condition where the body's cells do not respond effectively to insulin. Over time, the pancreas cannot produce enough insulin to manage blood sugar levels, resulting in type 2 diabetes. Additionally, excessive caloric intake without sufficient physical activity exacerbates the risk by promoting weight gain and fat accumulation, particularly around the abdomen, further contributing to insulin resistance.\n",
    "\\nExample 3:\n",
    "Query: What is the importance of hydration for physical performance?\n",
    "Answer: Hydration is crucial for physical performance because water plays key roles in maintaining blood volume, regulating body temperature, and ensuring the transport of nutrients and oxygen to cells. Adequate hydration is essential for optimal muscle function, endurance, and recovery. Dehydration can lead to decreased performance, fatigue, and increased risk of heat-related illnesses, such as heat stroke. Drinking sufficient water before, during, and after exercise helps ensure peak physical performance and recovery.\n",
    "\\nNow use the following context items to answer the user query:\n",
    "{context}\n",
    "\\nRelevant passages: <extract relevant passages from the context here>\n",
    "User query: {query}\n",
    "Answer:\"\"\"\n",
    "\n",
    "    # Update base prompt with context items and query   \n",
    "    base_prompt = base_prompt.format(context=context, query=query)\n",
    "\n",
    "    # Create prompt template for instruction-tuned model\n",
    "    dialogue_template = [\n",
    "        {\"role\": \"user\",\n",
    "        \"content\": base_prompt}\n",
    "    ]\n",
    "\n",
    "    # Apply the chat template\n",
    "    prompt = tokenizer.apply_chat_template(conversation=dialogue_template,\n",
    "                                          tokenize=False,\n",
    "                                          add_generation_prompt=True)\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: Describe the process of digestion and absorption of nutrients in the human body.\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00004 seconds.\n",
      "<bos><start_of_turn>user\n",
      "Based on the following context items, please answer the query.\n",
      "Give yourself room to think by extracting relevant passages from the context before answering the query.\n",
      "Don't return the thinking, only return the answer.\n",
      "Make sure your answers are as explanatory as possible.\n",
      "Use the following examples as reference for the ideal answer style.\n",
      "\n",
      "Example 1:\n",
      "Query: What are the fat-soluble vitamins?\n",
      "Answer: The fat-soluble vitamins include Vitamin A, Vitamin D, Vitamin E, and Vitamin K. These vitamins are absorbed along with fats in the diet and can be stored in the body's fatty tissue and liver for later use. Vitamin A is important for vision, immune function, and skin health. Vitamin D plays a critical role in calcium absorption and bone health. Vitamin E acts as an antioxidant, protecting cells from damage. Vitamin K is essential for blood clotting and bone metabolism.\n",
      "\n",
      "Example 2:\n",
      "Query: What are the causes of type 2 diabetes?\n",
      "Answer: Type 2 diabetes is often associated with overnutrition, particularly the overconsumption of calories leading to obesity. Factors include a diet high in refined sugars and saturated fats, which can lead to insulin resistance, a condition where the body's cells do not respond effectively to insulin. Over time, the pancreas cannot produce enough insulin to manage blood sugar levels, resulting in type 2 diabetes. Additionally, excessive caloric intake without sufficient physical activity exacerbates the risk by promoting weight gain and fat accumulation, particularly around the abdomen, further contributing to insulin resistance.\n",
      "\n",
      "Example 3:\n",
      "Query: What is the importance of hydration for physical performance?\n",
      "Answer: Hydration is crucial for physical performance because water plays key roles in maintaining blood volume, regulating body temperature, and ensuring the transport of nutrients and oxygen to cells. Adequate hydration is essential for optimal muscle function, endurance, and recovery. Dehydration can lead to decreased performance, fatigue, and increased risk of heat-related illnesses, such as heat stroke. Drinking sufficient water before, during, and after exercise helps ensure peak physical performance and recovery.\n",
      "\n",
      "Now use the following context items to answer the user query:\n",
      "- all other organ systems in the human body. We will learn the process of nutrient digestion and absorption, which further reiterates the importance of developing a healthy diet to maintain a healthier you. The evidence abounds that food can indeed be “thy medicine.” Learning Activities Technology Note: The second edition of the Human Nutrition Open Educational Resource (OER) textbook features interactive learning activities. These activities are available in the web-based textbook and not available in the downloadable versions (EPUB, Digital PDF, Print_PDF, or Open Document). Learning activities may be used across various mobile devices, however, for the best user experience it is strongly recommended that users complete these activities using a desktop or laptop computer and in Google Chrome.  An interactive or media element has been excluded from this version of the text. You can view it online here: http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=71 60 | Introduction\n",
      "- The Digestive System UNIVERSITY OF HAWAI‘I AT MĀNOA FOOD SCIENCE AND HUMAN NUTRITION PROGRAM AND HUMAN NUTRITION PROGRAM The process of digestion begins even before you put food into your mouth. When you feel hungry, your body sends a message to your brain that it is time to eat. Sights and smells influence your body’s preparedness for food. Smelling food sends a message to your brain. Your brain then tells the mouth to get ready, and you start to salivate in preparation for a meal. Once you have eaten, your digestive system (Figure 2.4 “The Human Digestive System”) starts the process that breaks down the components of food into smaller components that can be absorbed and taken into the body. To do this, the digestive system functions on two levels, mechanically to move and mix ingested food and chemically to break down large molecules. The smaller nutrient molecules can then be absorbed and processed by cells throughout the body for energy or used as building blocks for new cells. The digestive system is one of the eleven organ systems of the human body, and it is composed of several hollow tube-shaped organs including the mouth, pharynx, esophagus, stomach, small intestine, large intestine (colon), rectum, and anus. It is lined with mucosal tissue that secretes digestive juices (which aid in the breakdown of food) and mucus (which facilitates the propulsion of food through the tract).\n",
      "- An interactive or media element has been excluded from this version of the text. You can view it online here: http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=182  An interactive or media element has been excluded from this version of the text. You can view it online here: http://pressbooks.oer.hawaii.edu/ humannutrition2/?p=182 252 | Digestion and Absorption of Carbohydrates\n",
      "- All eleven organ systems in the human body require nutrient input to perform their specific biological functions. Overall health and the ability to carry out all of life’s basic processes is fueled by energy- supplying nutrients (carbohydrate, fat, and protein). Without them, organ systems would fail, humans would not reproduce, and the race would disappear. In this section, we will discuss some of the critical nutrients that support specific organ system functions. Learning Activities Technology Note: The second edition of the Human Nutrition Open Educational Resource (OER) textbook features interactive learning activities. These activities are available in the web-based textbook and not available in the downloadable versions (EPUB, Digital PDF, Print_PDF, or Open Document). Learning activities may be used across various mobile devices, however, for the best user experience it is strongly recommended that users complete these activities using a desktop or laptop computer and in Google Chrome.  An interactive or media element has been excluded from this version of the text. You can view it online here: 80 | The Digestive System\n",
      "- Metabolic pathways of a cell down, it is not metabolically efficient for a cell to synthesize fatty acids and break them down at the same time. Catabolism of food molecules begins when food enters the mouth, as the enzyme salivary amylase initiates the breakdown of the starch in foods. The entire process of digestion converts the large polymers in food to monomers that can be absorbed. Starches are broken down to monosaccharides, lipids are broken down to fatty acids, and proteins are broken down to amino acids. These monomers are absorbed into the bloodstream either directly, as is the case with monosaccharides and amino acids, or repackaged in intestinal cells for transport by an indirect route through lymphatic vessels, as is the case with most fatty acids and other fat-soluble molecules. Once absorbed, water-soluble nutrients first travel to the liver which controls their passage into the blood that transports the nutrients to cells throughout the body. The fat-soluble nutrients gradually pass from the lymphatic vessels into blood flowing to body cells. Cells requiring energy or building blocks take up the nutrients from the blood and process them in either catabolic or anabolic pathways. The organ systems of the body require fuel and building blocks to perform the many functions of the body, such as digesting, absorbing, breathing, pumping blood, transporting nutrients in and wastes out, maintaining body temperature, and making new cells. Figure 8.3 Cellular Metabolic Processes The Atom | 463\n",
      "\n",
      "Relevant passages: <extract relevant passages from the context here>\n",
      "User query: Describe the process of digestion and absorption of nutrients in the human body.\n",
      "Answer:<end_of_turn>\n",
      "<start_of_turn>model\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = random.choice(query_list)\n",
    "print(f\"Query: {query}\")\n",
    "\n",
    "# Get relevant resources\n",
    "scores, indices = retrieve_relevant_resources(query=query,\n",
    "                                              embeddings=embeddings)\n",
    "    \n",
    "# Create a list of context items\n",
    "context_items = [pages_and_chunks[i] for i in indices]\n",
    "\n",
    "# Format prompt with context items\n",
    "prompt = prompt_formatter(query=query,\n",
    "                          context_items=context_items)\n",
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n",
      "Query: Describe the process of digestion and absorption of nutrients in the human body.\n",
      "RAG answer:\n",
      "<bos>Sure, here's a summary of the process of digestion and absorption of nutrients in the human body:\n",
      "\n",
      "**Step 1: Food intake and sensory perception**\n",
      "- When we feel hungry, our bodies send a message to the brain, signaling the need for energy.\n",
      "- The brain interprets this signal and triggers the release of digestive hormones and other signaling molecules that prepare the body for food intake.\n",
      "\n",
      "**Step 2: The digestive system breaks down food**\n",
      "- Digestive system functions on two levels: mechanically to physically move and mix ingested food and chemically to break down large molecules into smaller ones that can be absorbed and processed.\n",
      "- The digestive system is composed of several organs, including the mouth, pharynx, esophagus, stomach, small intestine, large intestine (colon), rectum, and anus.\n",
      "\n",
      "**Step 3: Nutrient absorption**\n",
      "- After food is swallowed, it enters the mouth and then moves down the digestive tract, where it is broken down by enzymes and saliva.\n",
      "- Nutrients are absorbed through the walls of the small intestine into the bloodstream.\n",
      "- Water-soluble nutrients first travel to the liver, where they are processed and packaged for transport to the cells.\n",
      "- Fat-soluble nutrients are absorbed into the bloodstream directly from the intestinal lumen\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "\n",
    "input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "\n",
    "# Generate an output of tokens\n",
    "outputs = llm_model.generate(**input_ids,\n",
    "                             temperature=0.7, # lower temperature = more deterministic outputs, higher temperature = more creative outputs\n",
    "                             do_sample=True, # whether or not to use sampling, see https://huyenchip.com/2024/01/16/sampling.html for more\n",
    "                             max_new_tokens=256) # how many new tokens to generate from prompt \n",
    "\n",
    "# Turn the output tokens into text\n",
    "output_text = tokenizer.decode(outputs[0])\n",
    "\n",
    "print(f\"Query: {query}\")\n",
    "print(f\"RAG answer:\\n{output_text.replace(prompt, '')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functionizing Generation Step from LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ask(query, \n",
    "        temperature=0.7,\n",
    "        max_new_tokens=512,\n",
    "        format_answer_text=True, \n",
    "        return_answer_only=True):\n",
    "    \"\"\"\n",
    "    Takes a query, finds relevant resources/context and generates an answer to the query based on the relevant resources.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Get just the scores and indices of top related results\n",
    "    scores, indices = retrieve_relevant_resources(query=query,\n",
    "                                                  embeddings=embeddings)\n",
    "    \n",
    "    # Create a list of context items\n",
    "    context_items = [pages_and_chunks[i] for i in indices]\n",
    "\n",
    "    # Add score to context item\n",
    "    for i, item in enumerate(context_items):\n",
    "        item[\"score\"] = scores[i].cpu() # return score back to CPU \n",
    "        \n",
    "    # Format the prompt with context items\n",
    "    prompt = prompt_formatter(query=query,\n",
    "                              context_items=context_items)\n",
    "    \n",
    "    # Tokenize the prompt\n",
    "    input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "\n",
    "    # Generate an output of tokens\n",
    "    outputs = llm_model.generate(**input_ids,\n",
    "                                 temperature=temperature,\n",
    "                                 do_sample=True,\n",
    "                                 max_new_tokens=max_new_tokens)\n",
    "    \n",
    "    # Turn the output tokens into text\n",
    "    output_text = tokenizer.decode(outputs[0])\n",
    "\n",
    "    if format_answer_text:\n",
    "        # Replace special tokens and unnecessary help message\n",
    "        output_text = output_text.replace(prompt, \"\").replace(\"<bos>\", \"\").replace(\"<eos>\", \"\").replace(\"Sure, here is the answer to the user query:\\n\\n\", \"\")\n",
    "\n",
    "    # Only return the answer without the context items\n",
    "    if return_answer_only:\n",
    "        return output_text\n",
    "    \n",
    "    return output_text, context_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: What are symptoms of pellagra?\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00005 seconds.\n",
      "Answer:\n",
      "\n",
      "Sure, here are the symptoms of pellagra mentioned in the context:  - Diarrhea -\n",
      "Dermatitis - Dementia - Death\n",
      "Context items:\n"
     ]
    },
    {
     "data": {
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       "[{'page_number': 565,\n",
       "  'sentence_chunk': 'Niacin deficiency is commonly known as pellagra and the symptoms include fatigue, decreased appetite, and indigestion. These symptoms are then commonly followed by the four D’s: diarrhea, dermatitis, dementia, and sometimes death. Figure 9.12 Conversion of Tryptophan to Niacin Water-Soluble Vitamins | 565',\n",
       "  'chunk_char_count': 306,\n",
       "  'chunk_word_count': 43,\n",
       "  'chunk_token_count': 76.5,\n",
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       " {'page_number': 591,\n",
       "  'sentence_chunk': 'car. Does it drive faster with a half-tank of gas or a full one?It does not matter; the car drives just as fast as long as it has gas. Similarly, depletion of B vitamins will cause problems in energy metabolism, but having more than is required to run metabolism does not speed it up. Buyers of B-vitamin supplements beware; B vitamins are not stored in the body and all excess will be flushed down the toilet along with the extra money spent. B vitamins are naturally present in numerous foods, and many other foods are enriched with them. In the United States, B-vitamin deficiencies are rare; however in the nineteenth century some vitamin-B deficiencies plagued many people in North America. Niacin deficiency, also known as pellagra, was prominent in poorer Americans whose main dietary staple was refined cornmeal. Its symptoms were severe and included diarrhea, dermatitis, dementia, and even death. Some of the health consequences of pellagra are the result of niacin being in insufficient supply to support the body’s metabolic functions.',\n",
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       "  'sentence_chunk': 'The carbon dioxide gas bubbles infiltrate the stretchy gluten, giving bread its porosity and tenderness. For those who are sensitive to gluten, it is good to know that corn, millet, buckwheat, and oats do not contain the proteins that make gluten. However, some people who have celiac disease also may have a response to products containing oats. This is most likely the result of cross-contamination of grains during harvest, storage, packaging, and processing. Celiac disease is most common in people of European descent and is rare in people of African American, Japanese, and Chinese descent. It is much more prevalent in women and in people with Type 1 diabetes, autoimmune thyroid disease, and Down and Turner syndromes. Symptoms can range from mild to severe and can include pale, fatty, loose stools, gastrointestinal upset, abdominal pain, weight loss and, in children, a failure to grow and thrive. The symptoms can appear in infancy or much later in life, even Nutrition, Health and Disease | 1079',\n",
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       " {'page_number': 1090,\n",
       "  'sentence_chunk': 'Image by BruceBlaus/ CC BY 4.0 When the vertebral bone tissue is weakened, it can cause the spine to curve. The increase in spine curvature not only causes pain, but also decreases a person’s height. Curvature of the upper spine produces what is called Dowager’s hump, also known as kyphosis. Severe upper-spine deformity can compress the chest cavity and cause difficulty breathing. It may also cause abdominal pain and loss of appetite because of the increased pressure on the abdomen. 1090 | Nutrition, Health and Disease',\n",
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       " {'page_number': 1077,\n",
       "  'sentence_chunk': 'esophagus and cause irritation. It is estimated that GERD affects 25 to 35 percent of the US population. An analysis of several studies published in the August 2005 issue of Annals of Internal Medicine concludes that GERD is much more prevalent in people who are obese.1 The most common GERD symptom is heartburn, but people with GERD may also experience regurgitation (flow of the stomach’s acidic contents into the mouth), frequent coughing, and trouble swallowing. There are other causative factors of GERD that may be separate from or intertwined with obesity. The sphincter that separates the stomach’s internal contents from the esophagus often does not function properly and acidic gastric contents seep upward. Sometimes the peristaltic contractions of the esophagus are also sluggish and compromise the clearance of acidic contents. In addition to having an unbalanced, high-fat diet, some people with GERD are sensitive to particular foods—chocolate, garlic, spicy foods, fried foods, and tomato-based foods—which worsen symptoms. Drinks containing alcohol or caffeine may also worsen GERD symptoms. GERD is diagnosed most often by a history of the frequency of recurring symptoms. A more proper diagnosis can be made when a doctor inserts a small device into the lower esophagus that measures the acidity of the contents during one’s daily activities.',\n",
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       "          6.10336103e-02,  1.53106386e-02, -3.17388475e-02,  1.55391088e-02,\n",
       "         -2.74698669e-03,  6.35283217e-02, -2.82733198e-02, -2.76987385e-02,\n",
       "          2.38858089e-02,  9.14989877e-03, -3.36745419e-02, -5.74642234e-02,\n",
       "          8.17291215e-02,  3.95217165e-02, -8.00353196e-03,  2.73501668e-02,\n",
       "         -3.65348011e-02, -5.09556085e-02, -6.91084936e-02, -9.67170522e-02,\n",
       "         -2.13295836e-02, -1.11834081e-02,  4.94349115e-02, -4.14123088e-02,\n",
       "         -1.67513564e-02,  5.85031928e-03,  8.99993815e-04,  5.90685895e-03,\n",
       "          1.27878664e-02, -1.06550984e-01, -2.03814674e-02,  3.56015936e-02,\n",
       "         -9.34122782e-03, -2.38187704e-02, -1.53745469e-02, -2.09171586e-02,\n",
       "          3.40367109e-02,  1.13433059e-02,  1.06750447e-02,  4.15799618e-02,\n",
       "          1.86144207e-02, -2.75171082e-02,  2.46972330e-02,  1.88420247e-02,\n",
       "          4.39365171e-02, -2.98439641e-03, -5.27350698e-03,  2.91167218e-02,\n",
       "          3.40045206e-02,  3.05184145e-02, -1.30263986e-02,  3.27733122e-02,\n",
       "          1.43336030e-02, -1.48011558e-02,  4.15125862e-02, -3.24570648e-02,\n",
       "         -4.88501005e-02, -3.12288105e-02, -5.65916784e-02, -1.67724323e-02,\n",
       "         -9.46463551e-03,  9.54916421e-03, -3.72844711e-02,  1.57966744e-02,\n",
       "         -1.63697451e-02,  2.49343030e-02, -8.69573131e-02, -3.46590653e-02,\n",
       "         -4.41001877e-02, -6.54439554e-02,  3.58093195e-02, -2.19692197e-02,\n",
       "          3.59513164e-02,  5.34921661e-02, -9.93068442e-02, -3.23890001e-02,\n",
       "         -1.46301379e-02, -2.69573666e-02, -1.20684784e-02, -2.90089697e-02,\n",
       "         -1.25298165e-02,  3.73641923e-02,  1.45500954e-02, -8.92970040e-02,\n",
       "         -5.66669507e-03,  1.18048210e-02,  1.48320660e-01, -3.01065692e-03,\n",
       "          3.12293787e-02,  1.63574945e-02,  1.33103989e-02,  6.20237552e-03,\n",
       "          4.12822217e-02, -1.92943700e-02,  1.46980150e-04,  1.25061292e-02,\n",
       "          4.81469780e-02,  9.65289026e-03,  2.11142488e-02, -2.48228083e-03,\n",
       "          1.48439556e-02,  2.26218011e-02, -1.11322023e-03, -2.40939185e-02,\n",
       "         -9.02826991e-03, -2.73634624e-02, -2.57379655e-02,  6.80219531e-02,\n",
       "         -6.75356423e-04,  3.86389159e-02, -3.43064964e-02,  5.73634021e-02,\n",
       "         -5.91256469e-03,  3.96261103e-02,  2.66928319e-03,  4.43086028e-02,\n",
       "         -4.10007639e-03,  1.08326850e-02, -2.43261475e-02, -2.81300303e-03,\n",
       "         -2.48145442e-02, -3.43435891e-02,  1.84908807e-02,  6.74107624e-03,\n",
       "         -3.95936109e-02, -3.17802243e-02,  2.37179101e-02, -4.71149907e-02,\n",
       "          2.79924507e-07,  1.23621104e-02, -2.95126289e-02,  1.37103805e-02,\n",
       "          5.79985883e-03, -1.56977866e-02,  1.53956248e-03,  4.44341525e-02,\n",
       "          5.05852103e-02, -1.23070425e-03, -2.24524792e-02, -1.94147192e-02,\n",
       "          3.06341238e-02, -3.08307223e-02,  1.72229633e-02,  1.77848153e-02,\n",
       "         -1.25594242e-02, -2.30832212e-02,  8.29358101e-02,  3.68292467e-03,\n",
       "         -1.83243665e-03,  5.22549935e-02, -2.84631029e-02, -5.58212511e-02,\n",
       "         -4.50611375e-02, -7.19257537e-03, -6.47943886e-03, -3.08414176e-02,\n",
       "          1.35782501e-02, -1.05106994e-03, -9.16012190e-03,  2.82972436e-02,\n",
       "         -2.65486408e-02,  5.24806976e-02,  6.11404888e-03, -4.00571488e-02,\n",
       "         -2.82973647e-02,  2.29500849e-02, -5.93024604e-02, -2.20876578e-02,\n",
       "         -4.13870178e-02,  5.62005751e-02,  5.98054267e-02, -1.88976303e-02,\n",
       "         -6.31388202e-02, -1.59233827e-02, -4.39983280e-03,  3.44755989e-03,\n",
       "          1.28477871e-01,  4.14162353e-02, -3.74711230e-02, -1.35490578e-02,\n",
       "          1.24214096e-02,  1.16751026e-02,  2.36481079e-03, -1.10410135e-02,\n",
       "          5.90929873e-02,  3.49255875e-02, -3.43810283e-02,  3.43910977e-02,\n",
       "          5.60172834e-02, -3.41356173e-02, -1.05728758e-02,  9.53233335e-03,\n",
       "          1.28590362e-02, -5.24582807e-03,  8.19498207e-03, -4.65004519e-02,\n",
       "          2.56272807e-34, -5.30559570e-02,  4.01622057e-02, -4.06770557e-02,\n",
       "         -5.62329255e-02,  1.20808473e-02, -8.78201518e-03, -9.09225829e-03,\n",
       "          4.16994914e-02,  1.60410884e-03, -8.99688806e-03, -2.07996089e-02]),\n",
       "  'score': tensor(0.2941)}]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = random.choice(query_list)\n",
    "print(f\"Query: {query}\")\n",
    "\n",
    "# Answer query with context and return context \n",
    "answer, context_items = ask(query=query, \n",
    "                            temperature=0.7,\n",
    "                            max_new_tokens=512,\n",
    "                            return_answer_only=False)\n",
    "\n",
    "print(f\"Answer:\\n\")\n",
    "print_wrapped(answer)\n",
    "print(f\"Context items:\")\n",
    "context_items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Local RAG workflow complete!\n",
    "\n",
    "We've now officially got a way to Retrieve, Augment and Generate answers based on a source.\n",
    "\n",
    "For now we can verify our answers manually by reading them and reading through the textbook.\n",
    "\n",
    "But if you want to put this into a production system, it'd be a good idea to have some kind of evaluation on how well our pipeline works.\n",
    "\n",
    "For example, you could use another LLM to rate the answers returned by our LLM and then use those ratings as a proxy evaluation."
   ]
  }
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