00-simple-local-rag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mrdbourke/simple-local-rag/blob/main/00-simple-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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is RAG?\n",
    "\n",
    "RAG stands for Retrieval Augmented Generation.\n",
    "\n",
    "It was introduced in the paper [*Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks*](https://arxiv.org/abs/2005.11401).\n",
    "\n",
    "Each step can be roughly broken down to:\n",
    "\n",
    "* **Retrieval** - Seeking relevant information from a source given a query. For example, getting relevant passages of Wikipedia text from a database given a question.\n",
    "* **Augmented** - Using the relevant retrieved information to modify an input to a generative model (e.g. an LLM).\n",
    "* **Generation** - Generating an output given an input. For example, in the case of an LLM, generating a passage of text given an input prompt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why RAG?\n",
    "\n",
    "The main goal of RAG is to improve the generation outptus of LLMs.\n",
    "\n",
    "Two primary improvements can be seen as:\n",
    "1. **Preventing hallucinations** - LLMs are incredible but they are prone to potential hallucination, as in, generating something that *looks* correct but isn't. RAG pipelines can help LLMs generate more factual outputs by providing them with factual (retrieved) inputs. And even if the generated answer from a RAG pipeline doesn't seem correct, because of retrieval, you also have access to the sources where it came from.\n",
    "2. **Work with custom data** - Many base LLMs are trained with internet-scale text data. This means they have a great ability to model language, however, they often lack specific knowledge. RAG systems can provide LLMs with domain-specific data such as medical information or company documentation and thus customized their outputs to suit specific use cases.\n",
    "\n",
    "The authors of the original RAG paper mentioned above outlined these two points in their discussion.\n",
    "\n",
    "> This work offers several positive societal benefits over previous work: the fact that it is more\n",
    "strongly grounded in real factual knowledge (in this case Wikipedia) makes it “hallucinate” less\n",
    "with generations that are more factual, and offers more control and interpretability. RAG could be\n",
    "employed in a wide variety of scenarios with direct benefit to society, for example by endowing it\n",
    "with a medical index and asking it open-domain questions on that topic, or by helping people be more\n",
    "effective at their jobs.\n",
    "\n",
    "RAG can also be a much quicker solution to implement than fine-tuning an LLM on specific data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## What kind of problems can RAG be used for?\n",
    "\n",
    "RAG can help anywhere there is a specific set of information that an LLM may not have in its training data (e.g. anything not publicly accessible on the internet).\n",
    "\n",
    "For example you could use RAG for:\n",
    "* **Customer support Q&A chat** - By treating your existing customer support documentation as a resource, when a customer asks a question, you could have a system retrieve relevant documentation snippets and then have an LLM craft those snippets into an answer. Think of this as a \"chatbot for your documentation\". Klarna, a large financial company, [uses a system like this](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) to save $40M per year on customer support costs.\n",
    "* **Email chain analysis** - Let's say you're an insurance company with long threads of emails between customers and insurance agents. Instead of searching through each individual email, you could retrieve relevant passages and have an LLM create strucutred outputs of insurance claims.\n",
    "* **Company internal documentation chat** - If you've worked at a large company, you know how hard it can be to get an answer sometimes. Why not let a RAG system index your company information and have an LLM answer questions you may have? The benefit of RAG is that you will have references to resources to learn more if the LLM answer doesn't suffice.\n",
    "* **Textbook Q&A** - Let's say you're studying for your exams and constantly flicking through a large textbook looking for answers to your quesitons. RAG can help provide answers as well as references to learn more.\n",
    "\n",
    "All of these have the common theme of retrieving relevant resources and then presenting them in an understandable way using an LLM.\n",
    "\n",
    "From this angle, you can consider an LLM a calculator for words.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why local?\n",
    "\n",
    "Privacy, speed, cost.\n",
    "\n",
    "Running locally means you use your own hardware.\n",
    "\n",
    "From a privacy standpoint, this means you don't have send potentially sensitive data to an API.\n",
    "\n",
    "From a speed standpoint, it means you won't necessarily have to wait for an API queue or downtime, if your hardware is running, the pipeline can run.\n",
    "\n",
    "And from a cost standpoint, running on your own hardware often has a heavier starting cost but little to no costs after that.\n",
    "\n",
    "Performance wise, LLM APIs may still perform better than an open-source model running locally on general tasks but there are more and more examples appearing of smaller, focused models outperforming larger models. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key terms\n",
    "\n",
    "| Term | Description |\n",
    "| ----- | ----- | \n",
    "| **Token** | A sub-word piece of text. For example, \"hello, world!\" could be split into [\"hello\", \",\", \"world\", \"!\"]. A token can be a whole word,<br> part of a word or group of punctuation characters. 1 token ~= 4 characters in English, 100 tokens ~= 75 words.<br> Text gets broken into tokens before being passed to an LLM. |\n",
    "| **Embedding** | A learned numerical representation of a piece of data. For example, a sentence of text could be represented by a vector with<br> 768 values. Similar pieces of text (in meaning) will ideally have similar values. |\n",
    "| **Embedding model** | A model designed to accept input data and output a numerical representation. For example, a text embedding model may take in 384 <br>tokens of text and turn it into a vector of size 768. An embedding model can and often is different to an LLM model. |\n",
    "| **Similarity search/vector search** | Similarity search/vector search aims to find two vectors which are close together in high-demensional space. For example, <br>two pieces of similar text passed through an embedding model should have a high similarity score, whereas two pieces of text about<br> different topics will have a lower similarity score. Common similarity score measures are dot product and cosine similarity. |\n",
    "| **Large Language Model (LLM)** | A model which has been trained to numerically represent the patterns in text. A generative LLM will continue a sequence when given a sequence. <br>For example, given a sequence of the text \"hello, world!\", a genertive LLM may produce \"we're going to build a RAG pipeline today!\".<br> This generation will be highly dependant on the training data and prompt. |\n",
    "| **LLM context window** | The number of tokens a LLM can accept as input. For example, as of March 2024, GPT-4 has a default context window of 32k tokens<br> (about 96 pages of text) but can go up to 128k if needed. A recent open-source LLM from Google, Gemma (March 2024) has a context<br> window of 8,192 tokens (about 24 pages of text). A higher context window means an LLM can accept more relevant information<br> to assist with a query. For example, in a RAG pipeline, if a model has a larger context window, it can accept more reference items<br> from the retrieval system to aid with its generation. |\n",
    "| **Prompt** | A common term for describing the input to a generative LLM. The idea of \"[prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering)\" is to structure a text-based<br> (or potentially image-based as well) input to a generative LLM in a specific way so that the generated output is ideal. This technique is<br> possible because of a LLMs capacity for in-context learning, as in, it is able to use its representation of language to breakdown <br>the prompt and recognize what a suitable output may be (note: the output of LLMs is probable, so terms like \"may output\" are used). | \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## What we're going to build\n",
    "\n",
    "We're going to build RAG pipeline which enables us to chat with a PDF document, specifically an open-source [nutrition textbook](https://pressbooks.oer.hawaii.edu/humannutrition2/), ~1200 pages long.\n",
    "\n",
    "You could call our project NutriChat!\n",
    "\n",
    "We'll write the code to:\n",
    "1. Open a PDF document (you could use almost any PDF here).\n",
    "2. Format the text of the PDF textbook ready for an embedding model (this process is known as text splitting/chunking).\n",
    "3. Embed all of the chunks of text in the textbook and turn them into numerical representation which we can store for later.\n",
    "4. Build a retrieval system that uses vector search to find relevant chunks of text based on a query.\n",
    "5. Create a prompt that incorporates the retrieved pieces of text.\n",
    "6. Generate an answer to a query based on passages from the textbook.\n",
    "\n",
    "The above steps can broken down into two major sections:\n",
    "1. Document preprocessing/embedding creation (steps 1-3).\n",
    "2. Search and answer (steps 4-6).\n",
    "\n",
    "And that's the structure we'll follow.\n",
    "\n",
    "It's similar to the workflow outlined on the NVIDIA blog which [details a local RAG pipeline](https://developer.nvidia.com/blog/rag-101-demystifying-retrieval-augmented-generation-pipelines/).\n",
    "\n",
    "<img src=\"https://github.com/mrdbourke/simple-local-rag/blob/main/images/simple-local-rag-workflow-flowchart.png?raw=true\" alt=\"flowchart of a local RAG workflow\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements and setup\n",
    "\n",
    "* Local NVIDIA GPU (I used a NVIDIA RTX 4090 on a Windows 11 machine) or Google Colab with access to a GPU.\n",
    "* Environment setup (see [setup details on GitHub](https://github.com/mrdbourke/simple-local-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": 1,
   "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\n",
    "\n",
    "Ingredients:\n",
    "* PDF document of choice.\n",
    "* Embedding model of choice.\n",
    "\n",
    "Steps:\n",
    "1. Import PDF document.\n",
    "2. Process text for embedding (e.g. split into chunks of sentences).\n",
    "3. Embed text chunks with embedding model.\n",
    "4. Save embeddings to file for later use (embeddings will store on file for many years or until you lose your hard drive)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import PDF Document \n",
    "\n",
    "This will work with many other kinds of documents.\n",
    "\n",
    "However, we'll start with PDF since many people have PDFs.\n",
    "\n",
    "But just keep in mind, text files, email chains, support documentation, articles and more can also work.\n",
    "\n",
    "We're going to pretend we're nutrition students at the University of Hawai'i, reading through the open-source PDF textbook [*Human Nutrition: 2020 Edition*](https://pressbooks.oer.hawaii.edu/humannutrition2/).\n",
    "\n",
    "There are several libraries to open PDFs with Python but I found that [PyMuPDF](https://github.com/pymupdf/pymupdf) works quite well in many cases.\n",
    "\n",
    "First we'll download the PDF if it doesn't exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File human-nutrition-text.pdf exists.\n"
     ]
    }
   ],
   "source": [
    "# Download PDF file\n",
    "import os\n",
    "import requests\n",
    "\n",
    "# Get PDF document\n",
    "pdf_path = \"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": "markdown",
   "metadata": {},
   "source": [
    "PDF acquired!\n",
    "\n",
    "We can import the pages of our PDF to text by first defining the PDF path and then opening and reading it with PyMuPDF (`import fitz`).\n",
    "\n",
    "We'll write a small helper function to preprocess the text as it gets read. Note that not all text will be read in the same so keep this in mind for when you prepare your text.\n",
    "\n",
    "We'll save each page to a dictionary and then append that dictionary to a list for ease of use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "[{'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": 3,
     "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": "markdown",
   "metadata": {},
   "source": [
    "Now let's get a random sample of the pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 84,\n",
       "  'page_char_count': 1372,\n",
       "  'page_word_count': 247,\n",
       "  'page_sentence_count_raw': 12,\n",
       "  'page_token_count': 343.0,\n",
       "  'text': '“Blood Flow  Through the  Heart” by  OpenStax  College / CC  BY 3.0  The cardiovascular system is one of the eleven organ systems of  the human body. Its main function is to transport nutrients to cells  and wastes from cells (Figure 2.12 “Cardiovascular Transportation  of Nutrients”). This system consists of the heart, blood, and blood  vessels. The heart pumps the blood, and the blood is the  transportation fluid. The transportation route to all tissues, a highly  intricate blood-vessel network, comprises arteries, veins, and  capillaries. Nutrients absorbed in the small intestine travel mainly to  the liver through the hepatic portal vein. From the liver, nutrients  travel upward through the inferior vena cava blood vessel to the  heart. The heart forcefully pumps the nutrient-rich blood first to  the lungs to pick up some oxygen and then to all other cells in  the body. Arteries become smaller and smaller on their way to  cells, so that by the time blood reaches a cell, the artery’s diameter  is extremely small and the vessel is now called a capillary. The  reduced diameter of the blood vessel substantially slows the speed  of blood flow. This dramatic reduction in blood flow gives cells time  to harvest the nutrients in blood and exchange metabolic wastes.  Figure 2.11 The Blood Flow in the Cardiovascular System  84  |  The Cardiovascular System'},\n",
       " {'page_number': 897,\n",
       "  'page_char_count': 1635,\n",
       "  'page_word_count': 279,\n",
       "  'page_sentence_count_raw': 14,\n",
       "  'page_token_count': 408.75,\n",
       "  'text': 'such as watching television, playing video games, or surfing the  Internet.  Programs to address childhood obesity can include behavior  modification, exercise counseling, psychological support or  therapy, family counseling, and family meal-planning advice. For  most, the goal is not weight loss, but rather allowing height to catch  up with weight as the child continues to grow. Rapid weight loss is  not recommended for preteens or younger children due to the risk  of deficiencies and stunted growth.  Avoiding Added Sugars  One major contributing factor to childhood obesity is the  consumption of added sugars. Added sugars include not only sugar  added to food at the table, but also are ingredients in items such as  bread, cookies, cakes, pies, jams, and soft drinks. The added sugar  in store-bought items may be listed as white sugar, brown sugar,  high-fructose corn syrup, honey, malt syrup, maple syrup, molasses,  anhydrous dextrose, crystal dextrose, and concentrated fruit juice.  (Not included are sugars that occur naturally in foods, such as the  lactose in milk or the fructose in fruits.) In addition, sugars are  often “hidden” in items added to foods after they’re prepared, such  as ketchup, salad dressing, and other condiments. According to  the National Center for Health Statistics, young children and  adolescents consume an average of 322 calories per day from added  sugars, or about 16 percent of daily calories.7  7. Ervin RB, Kit BK, Carroll MD. (2012). Consumption of  Added Sugar among US Children and Adolescents,  2005–2008. National Center for Health Statistics. NCHS  Late Adolescence  |  897'},\n",
       " {'page_number': 815,\n",
       "  'page_char_count': 645,\n",
       "  'page_word_count': 116,\n",
       "  'page_sentence_count_raw': 4,\n",
       "  'page_token_count': 161.25,\n",
       "  'text': 'Image by  Centers for  Disease  Control and  Prevention /  Public  Domain  Nutritional Requirements  Requirements for macronutrients and micronutrients on a per- kilogram basis are higher during infancy than at any other stage  in the human life cycle. These needs are affected by the rapid cell  division that occurs during growth, which requires energy and  protein, along with the nutrients that are involved in synthesis of  DNA and other cellular components. During this period, children  are entirely dependent on their parents or other caregivers to meet  these needs. For almost all infants six months or younger, breast  Infancy  |  815'}]"
      ]
     },
     "execution_count": 4,
     "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\n",
    "\n",
    "Let's perform a rough exploratory data analysis (EDA) to get an idea of the size of the texts (e.g. character counts, word counts etc) we're working with.\n",
    "\n",
    "The different sizes of texts will be a good indicator into how we should split our texts.\n",
    "\n",
    "Many embedding models have limits on the size of texts they can ingest, for example, the [`sentence-transformers`](https://www.sbert.net/docs/pretrained_models.html) model [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) has an input size of 384 tokens.\n",
    "\n",
    "This means that the model has been trained in ingest and turn into embeddings texts with 384 tokens (1 token ~= 4 characters ~= 0.75 words).\n",
    "\n",
    "Texts over 384 tokens which are encoded by this model will be auotmatically reduced to 384 tokens in length, potentially losing some information.\n",
    "\n",
    "We'll discuss this more in the embedding section.\n",
    "\n",
    "For now, let's turn our list of dictionaries into a DataFrame and explore it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 5,
     "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": 6,
   "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",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get stats\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, looks like our average token count per page is 287.\n",
    "\n",
    "For this particular use case, it means we could embed an average whole page with the `all-mpnet-base-v2` model (this model has an input capacity of 384)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further text processing (splitting pages into sentences)\n",
    "\n",
    "The ideal way of processing text before embedding it is still an active area of research.\n",
    "\n",
    "A simple method I've found helpful is to break the text into chunks of sentences.\n",
    "\n",
    "As in, chunk a page of text into groups of 5, 7, 10 or more sentences (these values are not set in stone and can be explored).\n",
    "\n",
    "But we want to follow the workflow of:\n",
    "\n",
    "`Ingest text -> split it into groups/chunks -> embed the groups/chunks -> use the embeddings`\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[This is a sentence., This another sentence.]"
      ]
     },
     "execution_count": 7,
     "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": "markdown",
   "metadata": {},
   "source": [
    "We don't necessarily need to use spaCy, however, it's an open-source library designed to do NLP tasks like this at scale.\n",
    "\n",
    "So let's run our small sentencizing pipeline on our pages of text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "177454fcc3344b03b750eda50269c27f",
       "version_major": 2,
       "version_minor": 0
      },
      "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 419,\n",
       "  'page_char_count': 1427,\n",
       "  'page_word_count': 308,\n",
       "  'page_sentence_count_raw': 9,\n",
       "  'page_token_count': 356.75,\n",
       "  'text': 'consumed by the population, so that, on average, Americans obtain  44.6 percent of their foods in the meat and beans group from meats.  If you choose to follow a lacto-ovo vegetarian diet, the meats,  poultry, and fish can be replaced by consuming a higher percentage  of soy products, nuts, seeds, dry beans, and peas. As an aside, the  DGAC notes that these dietary patterns may not exactly align with  the typical diet patterns of people in the United States. However,  they do say that they can be adapted as a guide to develop a more  plant-based diet that does not significantly affect nutrient  adequacy.  Table 6.6 Percentage of “Meat and Beans Group” Components in  the USDA Base Diet, and Three Vegetarian Variations  Food Category  Base USDA  (%)  Plant-Based  (%)  Lacto-Ovo  Vegetarian (%)  Vegan  (%)  Meats  44.6  10.5  0  0  Poultry  27.9  8.0  0  0  Fish (high  omega-3)  2.2  3.0  0  0  Fish (low  omega-3)  7.1  10.0  0  0  Eggs  7.9  7.6  10.0  0  Soy products  0.9  15.0  30.0  25.0  Nuts and  seeds  9.4  20.9  35.0  40.0  Dry beans and  peas  n/a*  25.0  25.0  35.0  Total  100.0  100.0  100.0  100.0  *The dry beans and peas are in the vegetable food group of the base  diet.  Source:  Vegetarian Food Patterns: Food Pattern Modeling Analysis.  US  Department  of  Agriculture.  Appendix  E-3.3.  http://www.cnpp.usda.gov/Publications/DietaryGuidelines/2010/ Proteins, Diet, and Personal Choices  |  419',\n",
       "  'sentences': ['consumed by the population, so that, on average, Americans obtain  44.6 percent of their foods in the meat and beans group from meats.',\n",
       "   ' If you choose to follow a lacto-ovo vegetarian diet, the meats,  poultry, and fish can be replaced by consuming a higher percentage  of soy products, nuts, seeds, dry beans, and peas.',\n",
       "   'As an aside, the  DGAC notes that these dietary patterns may not exactly align with  the typical diet patterns of people in the United States.',\n",
       "   'However,  they do say that they can be adapted as a guide to develop a more  plant-based diet that does not significantly affect nutrient  adequacy.',\n",
       "   ' Table 6.6 Percentage of “Meat and Beans Group” Components in  the USDA Base Diet, and Three Vegetarian Variations  Food Category  Base USDA  (%)  Plant-Based  (%)  Lacto-Ovo  Vegetarian (%)  Vegan  (%)  Meats  44.6  10.5  0  0  Poultry  27.9  8.0  0  0  Fish (high  omega-3)  2.2  3.0  0  0  Fish (low  omega-3)  7.1  10.0  0  0  Eggs  7.9  7.6  10.0  0  Soy products  0.9  15.0  30.0  25.0  Nuts and  seeds  9.4  20.9  35.0  40.0  Dry beans and  peas  n/a*  25.0  25.0  35.0  Total  100.0  100.0  100.0  100.0  *The dry beans and peas are in the vegetable food group of the base  diet.',\n",
       "   ' Source:  Vegetarian Food Patterns: Food Pattern Modeling Analysis.',\n",
       "   ' US  Department  of  Agriculture.',\n",
       "   ' Appendix  E-3.3.',\n",
       "   ' http://www.cnpp.usda.gov/Publications/DietaryGuidelines/2010/ Proteins, Diet, and Personal Choices  |  419'],\n",
       "  'page_sentence_count_spacy': 9}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect an example\n",
    "random.sample(pages_and_texts, k=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful!\n",
    "\n",
    "Now let's turn out list of dictionaries into a DataFrame and get some stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(pages_and_texts)\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our set of text, it looks like our raw sentence count (e.g. splitting on `\". \"`) is quite close to what spaCy came up with.\n",
    "\n",
    "Now we've got our text split into sentences, how about we gorup those sentences?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunking our sentences together\n",
    "\n",
    "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",
    "\n",
    "Something to note is that there are many different ways emerging for creating chunks of information/text.\n",
    "\n",
    "For now, we're going to keep it simple and break our pages of sentences into groups of 10 (this number is arbitrary and can be changed, I just picked it because it seemed to line up well with our embedding model capacity of 384).\n",
    "\n",
    "On average each of our pages has 10 sentences.\n",
    "\n",
    "And an average total of 287 tokens per page.\n",
    "\n",
    "So our groups of 10 sentences will also be ~287 tokens long.\n",
    "\n",
    "This gives us plenty of room for the text to embedded by our `all-mpnet-base-v2` model (it has a capacity of 384 tokens).\n",
    "\n",
    "To split our groups of sentences into chunks of 10 or less, let's create a function which accepts a list as input and recursively breaks into down into sublists of a specified size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d37ee16f5d1f4f46bb489856aaeaa677",
       "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 1074,\n",
       "  'page_char_count': 1748,\n",
       "  'page_word_count': 292,\n",
       "  'page_sentence_count_raw': 22,\n",
       "  'page_token_count': 437.0,\n",
       "  'text': 'connection to body cues of hunger and fullness and the selection  of foods based on both pleasure and nutrient density.24  When an  individual is able to disconnect from diet culture and a focus on  weight loss, they are able to eat in ways that support having energy  throughout the day and feel competent around all foods.  Instead  of an outward focus on counting calories, dietary restriction, and  measuring food portions, intuitive eating teaches a mindfulness  practice of going inward and learning to respond to the cues and  rhythms of the body.  A review of 22 intervention studies that  compared traditional weight loss to the intuitive eating approach  concluded that the participants in the non-diet groups were able  to stop unhealthy weight controlling behaviors, improve metabolic  fitness and reduce risk factors, increase body satisfaction, and  improve psychological distress.25  While the non-diet approach has  not been shown to promote weight loss, a review of clinical trials  concluded that intuitive eating was able to stabilize weight and help  to prevent future weight gain, in addition to an improvement in  blood pressure and cholesterol levels, and dietary intake.26  24. The Original Intuitive Eating Pros. (2007 - 2019). 10  Principles of Intuitive Eating.  https://www.intuitiveeating.org/10-principles-of- intuitive-eating/  25. Schaefer J. T., Magnuson A. B. (2014). A review of  interventions that promote eating my internal cues.  Journal of the Academy of Nutrition and Dietetics. 114(5),  734-760.  26. Van Dyke N., Drinkwater E. J. (2014). Relationships  between intuitive eating and health indicators:  Literature review. Public Health Nutrition. 17(8),  1757-1766.  1074  |  Calories In Versus Calories Out',\n",
       "  'sentences': ['connection to body cues of hunger and fullness and the selection  of foods based on both pleasure and nutrient density.24  When an  individual is able to disconnect from diet culture and a focus on  weight loss, they are able to eat in ways that support having energy  throughout the day and feel competent around all foods.',\n",
       "   ' Instead  of an outward focus on counting calories, dietary restriction, and  measuring food portions, intuitive eating teaches a mindfulness  practice of going inward and learning to respond to the cues and  rhythms of the body.',\n",
       "   ' A review of 22 intervention studies that  compared traditional weight loss to the intuitive eating approach  concluded that the participants in the non-diet groups were able  to stop unhealthy weight controlling behaviors, improve metabolic  fitness and reduce risk factors, increase body satisfaction, and  improve psychological distress.25  While the non-diet approach has  not been shown to promote weight loss, a review of clinical trials  concluded that intuitive eating was able to stabilize weight and help  to prevent future weight gain, in addition to an improvement in  blood pressure and cholesterol levels, and dietary intake.26  24.',\n",
       "   'The Original Intuitive Eating Pros. (',\n",
       "   '2007 - 2019).',\n",
       "   '10  Principles of Intuitive Eating.',\n",
       "   ' https://www.intuitiveeating.org/10-principles-of- intuitive-eating/  25.',\n",
       "   'Schaefer J. T., Magnuson A. B. (2014).',\n",
       "   'A review of  interventions that promote eating my internal cues.',\n",
       "   ' Journal of the Academy of Nutrition and Dietetics.',\n",
       "   '114(5),  734-760.',\n",
       "   ' 26.',\n",
       "   'Van Dyke N., Drinkwater E. J. (2014).',\n",
       "   'Relationships  between intuitive eating and health indicators:  Literature review.',\n",
       "   'Public Health Nutrition.',\n",
       "   '17(8),  1757-1766.',\n",
       "   ' 1074  |  Calories In Versus Calories Out'],\n",
       "  'page_sentence_count_spacy': 17,\n",
       "  'sentence_chunks': [['connection to body cues of hunger and fullness and the selection  of foods based on both pleasure and nutrient density.24  When an  individual is able to disconnect from diet culture and a focus on  weight loss, they are able to eat in ways that support having energy  throughout the day and feel competent around all foods.',\n",
       "    ' Instead  of an outward focus on counting calories, dietary restriction, and  measuring food portions, intuitive eating teaches a mindfulness  practice of going inward and learning to respond to the cues and  rhythms of the body.',\n",
       "    ' A review of 22 intervention studies that  compared traditional weight loss to the intuitive eating approach  concluded that the participants in the non-diet groups were able  to stop unhealthy weight controlling behaviors, improve metabolic  fitness and reduce risk factors, increase body satisfaction, and  improve psychological distress.25  While the non-diet approach has  not been shown to promote weight loss, a review of clinical trials  concluded that intuitive eating was able to stabilize weight and help  to prevent future weight gain, in addition to an improvement in  blood pressure and cholesterol levels, and dietary intake.26  24.',\n",
       "    'The Original Intuitive Eating Pros. (',\n",
       "    '2007 - 2019).',\n",
       "    '10  Principles of Intuitive Eating.',\n",
       "    ' https://www.intuitiveeating.org/10-principles-of- intuitive-eating/  25.',\n",
       "    'Schaefer J. T., Magnuson A. B. (2014).',\n",
       "    'A review of  interventions that promote eating my internal cues.',\n",
       "    ' Journal of the Academy of Nutrition and Dietetics.'],\n",
       "   ['114(5),  734-760.',\n",
       "    ' 26.',\n",
       "    'Van Dyke N., Drinkwater E. J. (2014).',\n",
       "    'Relationships  between intuitive eating and health indicators:  Literature review.',\n",
       "    'Public Health Nutrition.',\n",
       "    '17(8),  1757-1766.',\n",
       "    ' 1074  |  Calories In Versus Calories Out']],\n",
       "  'num_chunks': 2}]"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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": 13,
     "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": [
    "Note how the average number of chunks is around 1.5, this is expected since many of our pages only contain an average of 10 sentences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting each chunk into its own item\n",
    "\n",
    "We'd like to embed each chunk of sentences into its own numerical representation.\n",
    "\n",
    "So to keep things clean, let's create a new list of dictionaries each containing a single chunk of sentences with relative information such as page number as well statistics about each chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94ccc5effc2e470d9c910dd607cbcaa9",
       "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": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'page_number': 877,\n",
       "  'sentence_chunk': 'Dakota County, Minnesota.https://www.co.dakota.mn.us/ HealthFamily/HealthyLiving/DietNutrition/ Documents/ReturnFamilyMeals.doc. Updated April 30, 2012. Accessed December 4, 2017. Childhood | 877',\n",
       "  'chunk_char_count': 195,\n",
       "  'chunk_word_count': 16,\n",
       "  'chunk_token_count': 48.75}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View a random sample\n",
    "random.sample(pages_and_chunks, k=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent!\n",
    "\n",
    "Now we've broken our whole textbook into chunks of 10 sentences or less as well as the page number they came from.\n",
    "\n",
    "This means we could reference a chunk of text and know its source.\n",
    "\n",
    "Let's get some stats about our chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "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": "markdown",
   "metadata": {},
   "source": [
    "Hmm looks like some of our chunks have quite a low token count.\n",
    "\n",
    "How about we check for samples with less than 30 tokens (about the length of a sentence) and see if they are worth keeping?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk token count: 5.25 | Text: Young Adulthood | 907\n",
      "Chunk token count: 9.0 | Text: 1088 | Nutrition, Health and Disease\n",
      "Chunk token count: 12.5 | Text: https://www.fda.gov/food/ 1022 | Food Preservation\n",
      "Chunk token count: 19.25 | Text: 2018). Centers for Disease Control and 998 | The Causes of Food Contamination\n",
      "Chunk token count: 12.5 | Text: Figure 11.2 The Structure of Hemoglobin Iron | 655\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": "markdown",
   "metadata": {},
   "source": [
    "Looks like many of these are headers and footers of different pages.\n",
    "\n",
    "They don't seem to offer too much information.\n",
    "\n",
    "Let's filter our DataFrame/list of dictionaries to only include chunks with over 30 tokens in length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
    "Smaller chunks filtered!\n",
    "\n",
    "Time to embed our chunks of text!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding our text chunks\n",
    "\n",
    "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.\n",
    "\n",
    "However, we don't need to.\n",
    "\n",
    "The embedding vectors are for our computers to understand.\n",
    "\n",
    "We'll use our computers to find patterns in the embeddings and then we can use their text mappings to further our understanding.\n",
    "\n",
    "Enough talking, 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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: The Sentences Transformers library provides an easy and open-source way to create embeddings.\n",
      "Embedding: [-2.07982454e-02  3.03164609e-02 -2.01217923e-02  6.86484575e-02\n",
      " -2.55255867e-02 -8.47687386e-03 -2.07212070e-04 -6.32377639e-02\n",
      "  2.81606745e-02 -3.33353467e-02  3.02634109e-02  5.30721210e-02\n",
      " -5.03527038e-02  2.62288693e-02  3.33313644e-02 -4.51577567e-02\n",
      "  3.63044813e-02 -1.37120159e-03 -1.20171094e-02  1.14947222e-02\n",
      "  5.04510775e-02  4.70857173e-02  2.11913772e-02  5.14606424e-02\n",
      " -2.03746744e-02 -3.58889550e-02 -6.67760614e-04 -2.94393618e-02\n",
      "  4.95859087e-02 -1.05639603e-02 -1.52014010e-02 -1.31759921e-03\n",
      "  4.48197499e-02  1.56023111e-02  8.60379430e-07 -1.21391343e-03\n",
      " -2.37978753e-02 -9.09375725e-04  7.34487409e-03 -2.53929920e-03\n",
      "  5.23370244e-02 -4.68043610e-02  1.66214723e-02  4.71579656e-02\n",
      " -4.15599272e-02  9.01959487e-04  3.60278003e-02  3.42214368e-02\n",
      "  9.68227237e-02  5.94829023e-02 -1.64984372e-02 -3.51248942e-02\n",
      "  5.92515618e-03 -7.07919884e-04 -2.41031293e-02  3.49740721e-02\n",
      " -2.94746570e-02  6.04268815e-03 -9.80651285e-03  2.83217859e-02\n",
      " -1.85375959e-02  3.63213159e-02  1.30292540e-02 -3.71232852e-02\n",
      "  5.27256243e-02 -1.19706932e-02 -7.18082264e-02  1.24431672e-02\n",
      " -6.70565153e-03  7.42154568e-02  1.16356211e-02 -1.74533334e-02\n",
      " -1.82405449e-02 -1.88930929e-02  2.82414965e-02  1.32829025e-02\n",
      " -3.51909734e-02  8.87319213e-04  5.79572693e-02  3.22093591e-02\n",
      " -3.48583376e-03  4.13768925e-02  1.44358333e-02 -3.28044333e-02\n",
      " -9.79082379e-03 -3.16492803e-02  4.23870832e-02 -4.70846593e-02\n",
      " -2.08937023e-02 -1.91249400e-02 -1.22627355e-02  1.01606343e-02\n",
      "  3.91921662e-02 -2.61896141e-02  1.09028099e-02  1.35723241e-02\n",
      " -5.79267219e-02 -3.21500227e-02 -5.75723080e-03 -2.43516192e-02\n",
      "  5.23416586e-02  5.46122668e-03 -2.30996273e-02  2.57173367e-03\n",
      " -6.63346648e-02  3.54125202e-02 -1.03907483e-02  2.25410443e-02\n",
      " -1.84573643e-02 -2.42007263e-02 -4.78365682e-02 -4.79226233e-03\n",
      " -5.34138270e-02  3.01790554e-02 -1.56130763e-02 -5.51477037e-02\n",
      " -3.91874239e-02  5.92152961e-02 -3.47646959e-02  9.68120154e-03\n",
      "  2.13415734e-02  2.30416805e-02  1.91713180e-02  2.77378783e-02\n",
      " -7.73507589e-03  1.04445936e-02 -2.67720185e-02 -2.40199827e-02\n",
      " -1.92290265e-02  3.91501049e-03 -2.54714675e-02  3.61942947e-02\n",
      "  5.12867272e-02 -8.41693301e-03 -3.13829891e-02  1.47483349e-02\n",
      "  2.13940125e-02 -3.84900682e-02  2.01944988e-02  1.20765846e-02\n",
      " -3.12067196e-03  7.84032047e-03  3.30336904e-03 -4.94357236e-02\n",
      "  5.83886877e-02  3.26138176e-03  4.84485738e-03 -4.50682305e-02\n",
      "  2.45682914e-02  3.55428122e-02 -5.32506108e-02  9.21152756e-02\n",
      "  2.04394944e-02 -3.36951762e-02 -6.19803444e-02 -2.11038999e-02\n",
      "  7.82358870e-02  5.11908047e-02  5.93170673e-02 -1.25127830e-04\n",
      "  4.96350378e-02 -1.55722536e-02 -3.35680065e-03  1.82016157e-02\n",
      " -2.73444057e-02 -1.08772172e-02  1.41476458e-02  1.09877652e-02\n",
      "  4.32554306e-03  8.23311135e-02 -9.85354185e-04  7.58791193e-02\n",
      "  9.44996160e-03  2.37687863e-02  1.61927994e-02  6.24993294e-02\n",
      "  4.75922041e-02 -3.92627763e-03  9.07524526e-02  4.49874774e-02\n",
      " -3.47131789e-02  2.14077085e-02 -3.35604288e-02  4.93849479e-02\n",
      "  1.08669819e-02  2.63447762e-02 -3.26089375e-02  8.00303519e-02\n",
      "  9.29764844e-03  7.16581196e-03 -2.79172380e-02 -3.06821149e-02\n",
      "  4.01063403e-03 -4.93906885e-02 -3.13771330e-03  4.00537476e-02\n",
      " -3.97855230e-02  5.48013970e-02  1.36247127e-05 -8.38373303e-02\n",
      " -1.21547850e-02  3.40949968e-02  3.22405831e-03  6.11846223e-02\n",
      "  5.60066774e-02  9.62873735e-03  2.54616197e-02 -4.64168899e-02\n",
      " -3.98900993e-02  7.68132210e-02  2.28408985e-02 -2.26568803e-02\n",
      " -1.91193279e-02 -6.53029159e-02  4.56781238e-02 -4.43655485e-03\n",
      "  1.49631742e-02 -2.15077978e-02  2.74250261e-03  1.90358665e-02\n",
      "  5.91888651e-02 -2.47569308e-02  3.66144367e-02  5.63084036e-02\n",
      " -8.86443444e-03 -1.74324550e-02 -1.03287061e-03  2.47666650e-02\n",
      "  1.30762933e-02  5.04633151e-02 -5.28495526e-03  5.92397265e-02\n",
      "  6.29906282e-02 -4.36783172e-02 -4.97830249e-02  5.56296594e-02\n",
      " -2.44854074e-02 -8.26754570e-02  2.04910599e-02 -1.06446259e-01\n",
      "  6.64840546e-03  2.97304653e-02 -2.36440487e-02 -8.84616375e-03\n",
      "  2.45555700e-03 -3.35234329e-02  7.52212331e-02 -5.89880235e-02\n",
      " -3.67808491e-02  3.41542959e-02  5.41130975e-02 -1.74904373e-02\n",
      "  1.33920917e-02  4.71682772e-02  1.46115953e-02 -2.12310832e-02\n",
      " -6.55338392e-02  1.23857716e-02  2.76074540e-02 -8.02161917e-03\n",
      " -4.59636338e-02 -8.22444260e-03  9.16958041e-03 -1.56399086e-02\n",
      "  7.54623115e-03  1.58309506e-03 -3.03958114e-02 -5.10671288e-02\n",
      "  1.96313709e-02  1.26263117e-02 -1.51738490e-03  2.02891193e-02\n",
      "  1.37817496e-02  1.49110453e-02  2.50766650e-02 -3.62870768e-02\n",
      "  1.08085275e-02  2.74132914e-03  1.81510746e-02  5.39872311e-02\n",
      " -4.74542081e-02 -4.28731479e-02 -2.89914068e-02  2.13235207e-02\n",
      " -3.85161228e-02  6.31922409e-02 -5.77975884e-02  3.77887511e-03\n",
      " -2.54394282e-02 -1.77263806e-04  9.08242725e-03  1.59095172e-02\n",
      "  4.11799923e-02 -3.94367687e-02 -9.64436587e-03  1.30791496e-02\n",
      "  6.87962398e-02  4.32192646e-02  7.54004228e-04  6.77741393e-02\n",
      "  4.93706055e-02 -3.47818411e-03 -1.06055075e-02  6.72496343e-03\n",
      " -1.39062339e-02  4.88276444e-02 -1.05734980e-02  3.50225763e-03\n",
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      " -2.39112247e-02  3.26579213e-02 -1.01325929e-03 -5.92755107e-03\n",
      " -7.40532018e-03  3.63147026e-03 -2.26698536e-02 -2.21241675e-02\n",
      "  3.86996605e-02  1.72321685e-02  3.85920107e-02 -5.04711345e-02\n",
      " -3.42145078e-02 -4.00444157e-02 -3.57910842e-02 -4.62560840e-02\n",
      "  6.70232400e-02 -4.61650547e-03 -3.29677551e-03  2.08444390e-02\n",
      " -5.14257140e-03 -5.00850044e-02  2.22504158e-02  4.66933623e-02\n",
      "  1.36208991e-02  1.77529808e-02  4.28078743e-03 -2.79332008e-02\n",
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      " -3.30087356e-02 -1.36265485e-03 -2.17933990e-02 -2.67508384e-02\n",
      " -1.26375761e-02  1.61870918e-03 -4.95672561e-02  7.85446018e-02\n",
      "  4.10962999e-02  9.65919718e-03 -1.14643425e-02  1.68855779e-03\n",
      "  5.37662916e-02  2.05534603e-03 -4.11200002e-02  1.46330288e-02\n",
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      "  3.32963541e-02  8.76122341e-03  1.33860519e-03 -3.95748066e-03\n",
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      "  1.11088331e-03  1.18732065e-01  8.13258141e-02  3.32448892e-02\n",
      "  4.38327380e-02 -2.49559525e-02 -3.59626524e-02  1.66319646e-02\n",
      "  5.93769737e-03 -1.43972356e-02  4.46708733e-03 -6.01986498e-02\n",
      " -5.65911643e-02 -8.21538642e-03  5.83059294e-03 -1.69482417e-02\n",
      "  9.58636124e-03  1.46733765e-02  5.05845807e-02  3.06891203e-02\n",
      "  6.60468414e-02 -2.56551486e-02 -2.78858002e-02 -3.19173522e-02\n",
      " -3.39236669e-02  1.49903176e-02 -3.03336084e-02 -6.06493466e-03\n",
      " -4.81772423e-03  1.72137339e-02 -8.23371764e-03  1.55547718e-02\n",
      "  2.69106552e-02  5.44307241e-03 -1.06899571e-02 -7.82139320e-03\n",
      " -4.44507077e-02  2.55873892e-02 -5.74760661e-02 -2.05443539e-02\n",
      " -3.07849925e-02 -1.57855190e-02 -7.07538938e-03 -4.21312153e-02\n",
      "  3.79933640e-02  6.27765357e-02 -7.67792296e-03 -3.18353251e-02\n",
      "  1.99277815e-34  1.04834130e-02 -3.39326560e-02  3.93821411e-02\n",
      "  5.53065836e-02  9.42169875e-03  1.09728323e-02 -4.91939299e-02\n",
      "  2.95024477e-02 -8.85372981e-03 -5.96248657e-02 -2.37825774e-02]\n",
      "\n",
      "Sentence: Embeddings are one of the most powerful concepts in machine learning!\n",
      "Embedding: [-2.98611391e-02 -1.37522556e-02 -4.75402176e-02  2.72126682e-02\n",
      "  3.40053439e-02  3.16466093e-02  4.26963717e-02  3.29800230e-03\n",
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      "  4.97339256e-02 -7.45570883e-02 -1.07008610e-02 -7.66055519e-03\n",
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      " -3.69248837e-02  3.64752375e-02  4.26422060e-02 -4.39826772e-03\n",
      "  1.78133324e-02 -2.67076790e-02 -7.13896286e-03  5.59975952e-02\n",
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      " -2.21660137e-02 -2.47735865e-02 -1.18190926e-02 -7.89703801e-03\n",
      " -2.08857339e-02  4.30556275e-02  1.07643493e-01  4.40618955e-02\n",
      "  1.47963287e-02  2.44862698e-02 -3.86268310e-02  1.80743616e-02\n",
      " -1.47837668e-03  7.74167180e-02 -4.19565290e-02 -3.80529240e-02\n",
      "  3.61258090e-02  1.59038510e-03  1.95323862e-02 -2.00080164e-02\n",
      "  4.22538556e-02  3.06111779e-02 -3.53699224e-03  5.93109662e-03\n",
      " -2.23223139e-02 -2.07131505e-02 -3.62917106e-03  1.74654163e-02\n",
      " -4.08759080e-02  5.91594800e-02 -5.89099191e-02 -3.96754257e-02\n",
      " -3.33529040e-02  1.02161923e-02  6.97289873e-03  7.70389959e-02\n",
      " -1.86911728e-02 -1.82568450e-02 -2.42319182e-02 -3.40701151e-03\n",
      " -3.60554829e-02  4.33389135e-02 -3.48602757e-02  5.27768210e-02\n",
      "  2.89710183e-02 -4.98462878e-02 -1.94748789e-02  1.16398484e-02\n",
      " -3.04614771e-02  8.04637522e-02  6.56247959e-02 -2.84533110e-02\n",
      "  1.81615220e-34 -4.19153506e-03 -2.57882737e-02  5.17319627e-02\n",
      "  4.94421162e-02  1.32476231e-02 -4.21993807e-02 -1.12458626e-02\n",
      " -2.61520445e-02  5.51130325e-02  2.20025312e-02 -2.51170639e-02]\n",
      "\n",
      "Sentence: Learn to use embeddings well and you'll be well on your way to being an AI engineer.\n",
      "Embedding: [-2.20731087e-02  2.08950397e-02 -6.03005849e-02  8.43948312e-03\n",
      "  4.37650830e-02  1.55070657e-02  4.99907173e-02 -3.03232279e-02\n",
      "  4.94784378e-02  2.35511567e-02  3.29350494e-02  1.53877605e-02\n",
      " -6.68355078e-02  1.11002848e-01  6.92676604e-02 -2.31889151e-02\n",
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      "  1.56423915e-02  1.02110989e-01  8.10766220e-03 -4.03859979e-03\n",
      "  2.02352605e-34 -1.38293765e-02 -1.17623918e-02  1.51005816e-02\n",
      "  8.25896412e-02  2.39228513e-02 -1.10377939e-02  3.65660689e-03\n",
      " -7.44786533e-03  2.94555090e-02  3.53000383e-03 -6.10421747e-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": "markdown",
   "metadata": {},
   "source": [
    "Woah! That's a lot of numbers.\n",
    "\n",
    "How about we do just once sentence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: Yo! How cool are embeddings?\n",
      "Embedding:\n",
      "[-1.97447948e-02 -4.51075844e-03 -4.98485751e-03  6.55444860e-02\n",
      " -9.87673178e-03  2.72836369e-02  3.66426371e-02 -3.30219069e-03\n",
      "  8.50076228e-03  8.24953150e-03 -2.28498019e-02  4.02430296e-02\n",
      " -5.75200543e-02  6.33692220e-02  4.43207324e-02 -4.49506566e-02\n",
      "  1.25284549e-02 -2.52011698e-02 -3.55292819e-02  1.29559152e-02\n",
      "  8.67022853e-03 -1.92917623e-02  3.55634978e-03  1.89505499e-02\n",
      " -1.47128273e-02 -9.39846318e-03  7.64178485e-03  9.62185580e-03\n",
      " -5.98921441e-03 -3.90169099e-02 -5.47824912e-02 -5.67457452e-03\n",
      "  1.11644482e-02  4.08067219e-02  1.76319122e-06  9.15305223e-03\n",
      " -8.77257995e-03  2.39382796e-02 -2.32784394e-02  8.04999769e-02\n",
      "  3.19176950e-02  5.12598967e-03 -1.47708310e-02 -1.62525158e-02\n",
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      " -8.93789623e-03  3.61119881e-02 -5.52125908e-02 -2.89572310e-02]\n",
      "Embedding size: (768,)\n"
     ]
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "Nice! We've now got a way to numerically represent each of our chunks.\n",
    "\n",
    "Our embedding has a shape of `(768,)` meaning it's a vector of 768 numbers which represent our text in high-dimensional space, too many for a human to comprehend but machines love high-dimensional space.\n",
    "\n",
    "> **Note:** No matter the size of the text input to our `all-mpnet-base-v2` model, it will be turned into an embedding size of `(768,)`. This value is fixed. So whether a sentence is 1 token long or 1000 tokens long, it will be truncated/padded with zeros to size 384 and then turned into an embedding vector of size `(768,)`. Of course, other embedding models may have different input/output shapes.\n",
    "\n",
    "How about we add an embedding field to each of our chunk items?\n",
    "\n",
    "Let's start by trying to create embeddings on the CPU, we'll time it with the `%%time` magic to see how long it takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%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": "markdown",
   "metadata": {},
   "source": [
    "Ok not too bad... but this would take a *really* long time if we had a larger dataset.\n",
    "\n",
    "Now let's see how long it takes to create the embeddings with a GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93f0fc4be888494193dc535a676cc431",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1680 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 281 ms\n",
      "Wall time: 12.2 s\n"
     ]
    }
   ],
   "source": [
    "%%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": "markdown",
   "metadata": {},
   "source": [
    "Woah! Looks like the embeddings get created much faster (~10x faster on my machine) on the GPU!\n",
    "\n",
    "You'll likely notice this trend with many of your deep learning workflows. If you have access to a GPU, especially a NVIDIA GPU, you should use one if you can.\n",
    "\n",
    "But what if I told you we could go faster again?\n",
    "\n",
    "You see many modern models can handle batched predictions.\n",
    "\n",
    "This means computing on multiple samples at once.\n",
    "\n",
    "Those are the types of operations where a GPU flourishes!\n",
    "\n",
    "We can perform batched operations by turning our target text samples into a single list and then passing that list to our embedding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1.17 s\n",
      "Wall time: 3.21 s\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": 24,
     "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": [
    "That's what I'm talking about!\n",
    "\n",
    "A ~4x improvement (on my GPU) in speed thanks to batched operations.\n",
    "\n",
    "So the tip here is to use a GPU when you can and use batched operations if you can too.\n",
    "\n",
    "Now let's save our chunks and their embeddings so we could import them later if we wanted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save embeddings to file\n",
    "\n",
    "Since creating embeddings can be a timely process (not so much for our case but it can be for more larger datasets), let's turn our `pages_and_chunks_over_min_token_len` list of dictionaries into a DataFrame and save it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": "markdown",
   "metadata": {},
   "source": [
    "And we can make sure it imports nicely by loading it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>[ 2.79801842e-02  3.39813866e-02 -2.06426680e-...</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  3.81275080e-02 -8.46853852e-...</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 -8.49768054e-03  9.57158953e-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  9.02281553e-02 -5.09548280e-...  \n",
       "1  [ 5.52156307e-02  5.92139475e-02 -1.66167468e-...  \n",
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       "4  [ 3.30264494e-02 -8.49768054e-03  9.57158953e-...  "
      ]
     },
     "execution_count": 26,
     "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\n",
    "\n",
    "> **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",
    "\n",
    "### Document Ingestion and Embedding Creation Extensions\n",
    "\n",
    "One major extension to the workflow above would to functionize it.\n",
    "\n",
    "Or turn it into a script.\n",
    "\n",
    "As in, take all the functionality we've created and package it into a single process (e.g. go from document -> embeddings file).\n",
    "\n",
    "So you could input a document on one end and have embeddings come out the other end. The hardest part of this is knowing what kind of preprocessing your text may need before it's turned into embeddings. Cleaner text generally means better results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. RAG - Search and Answer\n",
    "\n",
    "We discussed RAG briefly in the beginning but let's quickly recap.\n",
    "\n",
    "RAG stands for Retrieval Augmented Generation.\n",
    "\n",
    "Which is another way of saying \"given a query, search for relevant resources and answer based on those resources\".\n",
    "\n",
    "Let's breakdown each step:\n",
    "* **Retrieval** - Get relevant resources given a query. For example, if the query is \"what are the macronutrients?\" the ideal results will contain information about protein, carbohydrates and fats (and possibly alcohol) rather than information about which tractors are the best for farming (though that is also cool information).\n",
    "* **Augmentation** - LLMs are capable of generating text given a prompt. However, this generated text is designed to *look* right. And it often has some correct information, however, they are prone to hallucination (generating a result that *looks* like legit text but is factually wrong). In augmentation, we pass relevant information into the prompt and get an LLM to use that relevant information as the basis of its generation.\n",
    "* **Generation** - This is where the LLM will generate a response that has been flavoured/augmented with the retrieved resources. In turn, this not only gives us a potentially more correct answer, it also gives us resources to investigate more (since we know which resources went into the prompt).\n",
    "\n",
    "The whole idea of RAG is to get an LLM to be more factually correct based on your own input as well as have a reference to where the generated output may have come from.\n",
    "\n",
    "This is an incredibly helpful tool.\n",
    "\n",
    "Let's say you had 1000s of customer support documents.\n",
    "\n",
    "You could use RAG to generate direct answers to questions with links to relevant documentation.\n",
    "\n",
    "Or you were an insurance company with large chains of claims emails.\n",
    "\n",
    "You could use RAG to answer questions about the emails with sources.\n",
    "\n",
    "One helpful analogy is to think of LLMs as calculators for words.\n",
    "\n",
    "With good inputs, the LLM can sort them into helpful outputs.\n",
    "\n",
    "How? \n",
    "\n",
    "It starts with better search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity search\n",
    "\n",
    "Similarity search or semantic search or vector search is the idea of searching on *vibe*.\n",
    "\n",
    "If this sounds like woo, woo. It's not.\n",
    "\n",
    "Perhaps searching via *meaning* is a better analogy.\n",
    "\n",
    "With keyword search, you are trying to match the string \"apple\" with the string \"apple\".\n",
    "\n",
    "Whereas with similarity/semantic search, you may want to search \"macronutrients functions\".\n",
    "\n",
    "And get back results that don't necessarily contain the words \"macronutrients functions\" but get back pieces of text that match that meaning.\n",
    "\n",
    "> **Example:** Using similarity search on our textbook data with the query \"macronutrients function\" returns a paragraph that starts with: \n",
    ">\n",
    ">*There are three classes of macronutrients: carbohydrates, lipids, and proteins. These can be metabolically processed into cellular energy. The energy from macronutrients comes from their chemical bonds. This chemical energy is converted into cellular energy that is then utilized to perform work, allowing our bodies to conduct their basic functions.*\n",
    "> \n",
    "> as the first result. How cool!\n",
    "\n",
    "If you've ever used Google, you know this kind of workflow.\n",
    "\n",
    "But now we'd like to perform that across our own data.\n",
    "\n",
    "Let's import our embeddings we created earlier (tk -link to embedding file) and prepare them for use by turning them into a tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1680, 768])"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   page_number                                     sentence_chunk  \\\n",
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       "   chunk_char_count  chunk_word_count  chunk_token_count  \\\n",
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     "execution_count": 28,
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   ],
   "source": [
    "text_chunks_and_embedding_df.head()"
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "        -5.9322e-02,  2.9802e-03, -3.8935e-02, -3.5005e-03, -6.1465e-02,\n",
       "         2.8272e-02, -3.5795e-02, -2.4533e-02, -6.7367e-02,  9.3984e-02,\n",
       "         2.5381e-02, -8.7608e-03,  9.6247e-03,  1.6570e-02, -5.3413e-02,\n",
       "        -1.8370e-02, -5.4454e-02, -5.1886e-02, -3.9982e-02,  6.0174e-02,\n",
       "        -4.0211e-02,  3.4026e-02,  7.1232e-03, -1.7635e-02,  2.6353e-02,\n",
       "        -8.1668e-02,  4.1597e-02,  1.1054e-02,  7.7330e-03, -3.5506e-02,\n",
       "        -5.8768e-03, -1.4153e-02, -2.7146e-02,  2.3820e-02, -3.4479e-03,\n",
       "         3.2835e-02,  4.4337e-03, -3.0013e-02,  4.7975e-02, -4.0580e-02,\n",
       "        -2.1400e-02,  7.8742e-02, -1.5630e-02,  1.8930e-02,  2.6755e-02,\n",
       "        -4.1777e-02,  6.4979e-03,  1.6041e-03,  6.6808e-02, -5.2895e-02,\n",
       "         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, -3.0507e-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,  3.9962e-04,  6.6996e-02, -1.7236e-02,\n",
       "        -5.3030e-02,  9.9722e-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,  2.7564e-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",
       "        -1.7709e-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, -6.8736e-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, -4.1760e-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,  2.5904e-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, -5.5964e-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, -2.1096e-03, -2.4802e-02,  2.2264e-02,  9.2586e-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, -4.8723e-03,  4.7631e-02,  2.1820e-02,\n",
       "        -7.3083e-03, -2.9660e-02,  3.7068e-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, -6.5991e-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,  3.0125e-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",
       "        -1.9804e-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,  6.9092e-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, -1.0193e-03,\n",
       "        -3.2433e-02,  1.4610e-02,  9.1389e-03, -2.7832e-02,  1.9247e-02,\n",
       "        -2.2115e-02, -2.3214e-02,  1.2569e-02], device='cuda:0')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice!\n",
    "\n",
    "Now let's prepare another instance of our embedding model. Not because we have to but because we'd like to make it so you can start the notebook from the cell above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "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": "markdown",
   "metadata": {},
   "source": [
    "Embedding model ready!\n",
    "\n",
    "Time to perform a semantic search.\n",
    "\n",
    "Let's say you were studying the macronutrients.\n",
    "\n",
    "And wanted to search your textbook for \"macronutrients functions\".\n",
    "\n",
    "Well, we can do so with the following steps:\n",
    "1. Define a query string (e.g. `\"macronutrients functions\"`) - note: this could be anything, specific or not.\n",
    "2. Turn the query string in an embedding with same model we used to embed our text chunks.\n",
    "3. Perform a [dot product](https://pytorch.org/docs/stable/generated/torch.dot.html) or [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) function between the text embeddings and the query embedding (we'll get to what these are shortly) to get similarity scores.\n",
    "4. Sort the results from step 3 in descending order (a higher score means more similarity in the eyes of the model) and use these values to inspect the texts. \n",
    "\n",
    "Easy!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: macronutrients functions\n",
      "Time take to get scores on 1680 embeddings: 0.00008 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": 31,
     "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": "markdown",
   "metadata": {},
   "source": [
    "Woah!! Now that was fast!\n",
    "\n",
    "~0.00008 seconds to perform a dot product comparison across 1680 embeddings on my machine (NVIDIA RTX 4090 GPU).\n",
    "\n",
    "GPUs are optimized for these kinds of operations.\n",
    "\n",
    "So even if you we're to increase our embeddings by 100x (1680 -> 168,000), an exhaustive dot product operation would happen in ~0.008 seconds (assuming linear scaling).\n",
    "\n",
    "Heck, let's try it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings shape: torch.Size([168000, 768])\n",
      "Time take to get scores on 168000 embeddings: 0.00018 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": "markdown",
   "metadata": {},
   "source": [
    "Wow. That's quick!\n",
    "\n",
    "That means we can get pretty far by just storing our embeddings in `torch.tensor` for now.\n",
    "\n",
    "However, for *much* larger datasets, we'd likely look at a dedicated vector database/indexing libraries such as [Faiss](https://github.com/facebookresearch/faiss).\n",
    "\n",
    "Let's check the results of our original similarity search.\n",
    "\n",
    "[`torch.topk`](https://pytorch.org/docs/stable/generated/torch.topk.html) returns a tuple of values (scores) and indicies for those scores.\n",
    "\n",
    "The indicies relate to which indicies in the `embeddings` tensor have what scores in relation to the query embedding (higher is better).\n",
    "\n",
    "We can use those indicies to map back to our text chunks.\n",
    "\n",
    "First, we'll define a small helper function to print out wrapped text (so it doesn't print a whole text chunk as a single line)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Now we can loop through the `top_results_dot_product` tuple and match up the scores and indicies and then use those indicies to index on our `pages_and_chunks` variable to get the relevant text chunk.\n",
    "\n",
    "Sounds like a lot but we can do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": "markdown",
   "metadata": {},
   "source": [
    "The first result looks to have nailed it!\n",
    "\n",
    "We get a very relevant answer to our query `\"macronutrients functions\"` even though its quite vague.\n",
    "\n",
    "That's the power of semantic search!\n",
    "\n",
    "And even better, if we wanted to inspect the result further, we get the page number where the text appears.\n",
    "\n",
    "How about we check the page to verify?\n",
    "\n",
    "We can do so by loading the page number containing the highest result (page 5 but really page 5 + 41 since our PDF page numbers start on page 41)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 = \"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": [
    "Nice!\n",
    "\n",
    "Now we can do extra research if we'd like.\n",
    "\n",
    "We could repeat this workflow for any kind of query we'd like on our textbook.\n",
    "\n",
    "And it would also work for other datatypes too.\n",
    "\n",
    "We could use semantic search on customer support documents.\n",
    "\n",
    "Or email threads.\n",
    "\n",
    "Or company plans.\n",
    "\n",
    "Or our old journal entries.\n",
    "\n",
    "Almost anything!\n",
    "\n",
    "The workflow is the same:\n",
    "\n",
    "`ingest documents -> split into chunks -> embed chunks -> make a query -> embed the query -> compare query embedding to chunk embeddings`\n",
    "\n",
    "And we get relevant resources *along with* the source they came from!\n",
    "\n",
    "That's the **retrieval** part of Retrieval Augmented Generation (RAG).\n",
    "\n",
    "Before we get to the next two steps, let's take a small aside and discuss similarity measures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity measures: dot product and cosine similarity \n",
    "\n",
    "Let's talk similarity measures between vectors.\n",
    "\n",
    "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": 36,
   "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": [
    "Notice for both dot product and cosine similarity the comparisons of `vector1` and `vector2` are the opposite of `vector1` and `vector4`.\n",
    "\n",
    "Comparing `vector1` and `vector2` both equations return positive values (14 for dot product and 1.0 for cosine similarity). \n",
    "\n",
    "But comparing `vector1` and `vector4` the result is in the negative direction.\n",
    "\n",
    "This makes sense because `vector4` is the negative version of `vector1`.\n",
    "\n",
    "Whereas comparing `vector1` and `vector3` shows a different outcome.\n",
    "\n",
    "For the dot product, the value is positive and larger then the comparison of two exactly the same vectors (32 vs 14).\n",
    "\n",
    "However, for the cosine similarity, thanks to the normalization step, comparing `vector1` and `vector3` results in a postive value close to 1 but not exactly 1.\n",
    "\n",
    "It is because of this that when comparing text embeddings, cosine similarity is generally favoured as it measures the difference in direction of a pair of vectors rather than difference in magnitude.\n",
    "\n",
    "And it is this difference in direction that is more generally considered to capture the semantic meaning/vibe of the text.\n",
    "\n",
    "The good news is that as mentioned before, the outputs of our embedding model `all-mpnet-base-v2` are already normalized.\n",
    "\n",
    "So we can continue using the dot product (cosine similarity is dot product + normalization).\n",
    "\n",
    "With similarity measures explained, let's functionize our semantic search steps from above so we can repeat them. "
   ]
  },
  {
   "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": 37,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now let's test our functions out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00002 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": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00002 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 \n",
    "\n",
    "We've covered an exmaple of using embedding vector search to find relevant results based on a query.\n",
    "\n",
    "However, you could also add to this pipeline with traditional keyword search.\n",
    "\n",
    "Many modern search systems use keyword and vector search in tandem.\n",
    "\n",
    "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",
    "For example, it would be inefficient to search every word in the dictionary to find the word \"duck\", instead you'd go straight to the letter D, perhaps even straight to the back half of the letter D, find words close to \"duck\" before finding it.\n",
    "\n",
    "That's how an index can help search through many examples without comprimising too much on speed or quality (for more on this, check out [nearest neighbour search](https://en.wikipedia.org/wiki/Nearest_neighbor_search)).\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\n",
    "\n",
    "We're got our retrieval pipeline ready, let's now get the generation side of things happening.\n",
    "\n",
    "To perform generation, we're going to use a Large Language Model (LLM).\n",
    "\n",
    "LLMs are designed to generate an output given an input.\n",
    "\n",
    "In our case, we want our LLM to generate and output of text given a input of text.\n",
    "\n",
    "And more specifically, we want the output of text to be generated based on the context of relevant information to the query.\n",
    "\n",
    "The input to an LLM is often referred to as a prompt.\n",
    "\n",
    "We'll augment our prompt with a query as well as context from our textbook related to that query.\n",
    "\n",
    "> **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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available GPU memory: 24 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": [
    "Ok wonderful!\n",
    "\n",
    "I'm running this notebook with a NVIDIA RTX 4090, so I've got 24GB of VRAM available.\n",
    "\n",
    "However, this may be different on your end.\n",
    "\n",
    "Looking at the table above, it seems we can run a ~7-10B parameter model in float16 precision pretty comfortably.\n",
    "\n",
    "But we could also run a smaller one if we'd like.\n",
    "\n",
    "Let's try out the recently released (at the time of writing, March 2024) LLM from Google, [Gemma](https://huggingface.co/blog/gemma).\n",
    "\n",
    "Specifically, we'll use the `gemma-7b-it` version which stands for Gemma 7B Instruction-Tuned.\n",
    "\n",
    "Instruction tuning is the process of tuning a raw language model to follow instructions.\n",
    "\n",
    "These are the kind of models you'll find in most chat-based assistants such as ChatGPT, Gemini or Claude.\n",
    "\n",
    "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU memory: 24 | Recommend model: Gemma 7B in 4-bit or float16 precision.\n",
      "use_quantization_config set to: False\n",
      "model_id set to: google/gemma-7b-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 = True \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\n",
    "\n",
    "Alright! Looks like `gemma-7b-it` it is (for my local machine with an RTX 4090, change the `model_id` and `use_quantization_config` values to suit your needs)! \n",
    "\n",
    "There are plenty of examples of how to load the model on the `gemma-7b-it` [Hugging Face model card](https://huggingface.co/google/gemma-7b-it).\n",
    "\n",
    "Good news is, the Hugging Face [`transformers`](https://huggingface.co/docs/transformers/) library has all the tools we need.\n",
    "\n",
    "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",
    "\n",
    "> **Note:** To get access to the Gemma models, you will have to [agree to the terms & conditions](https://huggingface.co/google/gemma-7b-it) on the Gemma model page on Hugging Face. You will then have to authorize your local machine via the [Hugging Face CLI/Hugging Face Hub `login()` function](https://huggingface.co/docs/huggingface_hub/en/quick-start#authentication). Once you've done this, you'll be able to download the models. If you're using Google Colab, you can add a [Hugging Face token](https://huggingface.co/docs/hub/en/security-tokens) to the \"Secrets\" tab.\n",
    ">\n",
    "> Downloading an LLM locally can take a fair bit of time depending on your internet connection. Gemma 7B is about a 16GB download and Gemma 2B is about a 6GB download.\n",
    "\n",
    "Let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using attention implementation: flash_attention_2\n",
      "[INFO] Using model_id: google/gemma-7b-it\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60a7efc798a14a2a8c68ce3063368715",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [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": "markdown",
   "metadata": {},
   "source": [
    "We've got an LLM!\n",
    "\n",
    "Let's check it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GemmaForCausalLM(\n",
       "  (model): GemmaModel(\n",
       "    (embed_tokens): Embedding(256000, 3072, padding_idx=0)\n",
       "    (layers): ModuleList(\n",
       "      (0-27): 28 x GemmaDecoderLayer(\n",
       "        (self_attn): GemmaFlashAttention2(\n",
       "          (q_proj): Linear(in_features=3072, out_features=4096, bias=False)\n",
       "          (k_proj): Linear(in_features=3072, out_features=4096, bias=False)\n",
       "          (v_proj): Linear(in_features=3072, out_features=4096, bias=False)\n",
       "          (o_proj): Linear(in_features=4096, out_features=3072, bias=False)\n",
       "          (rotary_emb): GemmaRotaryEmbedding()\n",
       "        )\n",
       "        (mlp): GemmaMLP(\n",
       "          (gate_proj): Linear(in_features=3072, out_features=24576, bias=False)\n",
       "          (up_proj): Linear(in_features=3072, out_features=24576, bias=False)\n",
       "          (down_proj): Linear(in_features=24576, out_features=3072, 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=3072, out_features=256000, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, ok a bunch of layers ranging from embedding layers to attention layers (see the `GemmaFlashAttention2` layers!) to MLP and normalization layers.\n",
    "\n",
    "The good news is that we don't have to know too much about these to use the model.\n",
    "\n",
    "How about we get the number of parameters in our model? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8537680896"
      ]
     },
     "execution_count": 44,
     "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": "markdown",
   "metadata": {},
   "source": [
    "Hmm, turns out that Gemma 7B is really Gemma 8.5B.\n",
    "\n",
    "It pays to do your own investigations!\n",
    "\n",
    "How about we get the models memory requirements?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_mem_bytes': 17142470656,\n",
       " 'model_mem_mb': 16348.33,\n",
       " 'model_mem_gb': 15.97}"
      ]
     },
     "execution_count": 45,
     "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": [
    "Nice, looks like this model takes up 15.97GB of space on the GPU.\n",
    "\n",
    "Plus a little more for the forward pass (due to all the calculations happening between the layers).\n",
    "\n",
    "Hence why I rounded it up to be ~19GB in the table above.\n",
    "\n",
    "Now let's get to the fun part, generating some text!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating text with our LLM\n",
    "\n",
    "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",
    "It's important to note that you should use a tokenizer that has been paired with a model.\n",
    "\n",
    "Otherwise if you try to use a different tokenizer and then pass those inputs to a model, you will likely get errors/strange results.\n",
    "\n",
    "For some LLMs, there's a specific template you should pass to them for ideal outputs.\n",
    "\n",
    "For example, the `gemma-7b-it` model has been trained in a dialogue fashion (instruction tuning).\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.\n",
    "\n",
    "> **Note:** The following demo has been modified from the Hugging Face model card for [Gemma 7B](https://huggingface.co/google/gemma-7b-it). Many similar demos of usage are available on the model cards of similar models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Notice the scaffolding around our input text, this is the kind of turn-by-turn instruction tuning our model has gone through.\n",
    "\n",
    "Our next step is to tokenize this formatted text and pass it to our model's `generate()` method.\n",
    "\n",
    "We'll make sure our tokenized text is on the same device as our model (GPU) using `to(\"cuda\")`.\n",
    "\n",
    "Let's generate some text! \n",
    "\n",
    "We'll time it for fun with the `%%time` magic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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,    708,    573, 186809, 184592,    578,   1024,\n",
      "         16065,    575,    573,   3515,   2971, 235292,    109,    688,  12298,\n",
      "          1695, 184592,  66058,    109,    688, 235274, 235265, 110165,  56227,\n",
      "         66058,    109, 235290,   5231,  11071,  66058,    108,    139, 235290,\n",
      "         34428,   4134,    604,    573,   2971, 235265,    108,    139, 235290,\n",
      "         13332,  41748,   5330,   9347,   5902, 235265,    108,    139, 235290,\n",
      "         34428,  20149,    604,  51536,    578,  12792,   2962, 235265,    109,\n",
      "           688, 235284, 235265,  96084,  66058,    109, 235290,   5231,  11071,\n",
      "         66058,    108,    139, 235290,   8108,    578,  12158,  29703, 235265,\n",
      "           108,    139, 235290,   7319,  44760,    578,   1156,  26039, 235265,\n",
      "           108,    139, 235290,  13332,  41748,   5330,   9347,   5902, 235265,\n",
      "           109,    688, 235304, 235265, 178190,  66058,    109, 235290,   5231,\n",
      "         11071,  66058,    108,    139, 235290,  34428,   4134,   7387, 235265,\n",
      "           108,    139, 235290,  13332,  33398,  48765,    578,  31740, 235265,\n",
      "           108,    139, 235290,  13332,  41748,   5330,   9347,   5902, 235265,\n",
      "           109,    688,   6273,  29038,  66058,    109, 235290,  97586, 184592,\n",
      "          1554,    476,   4731,    575,  61779,    573,   2971, 235303, 235256,\n",
      "         34883, 235269,    948,    603,    573,   2185,    576,  17704,   1706,\n",
      "          2960,   1280,   4134, 235265,    108, 235290,   2365,   1170,   1707,\n",
      "           577,   2500,    578,  12158,  29703, 235269,    578,    577,   7872,\n",
      "          4134,    604,    573,   2971, 235265,    108, 235290,  97586, 184592,\n",
      "           708,   8727,    604,   8691,   2962,    578,   1578, 235290,  16978,\n",
      "        235265,      1], device='cuda:0')\n",
      "\n",
      "CPU times: total: 1.36 s\n",
      "Wall time: 5.01 s\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": "markdown",
   "metadata": {},
   "source": [
    "Woohoo! We just generated some text on our local GPU!\n",
    "\n",
    "Well not just yet...\n",
    "\n",
    "Our LLM accepts tokens in and sends tokens back out.\n",
    "\n",
    "We can conver the output tokens to text using [`tokenizer.decode()`](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizer.decode)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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 are the macronutrients and their roles in the human body:\n",
      "\n",
      "**Macronutrients:**\n",
      "\n",
      "**1. Carbohydrates:**\n",
      "\n",
      "- **Role:**\n",
      "  - Provide energy for the body.\n",
      "  - Help regulate blood sugar levels.\n",
      "  - Provide fiber for digestion and gut health.\n",
      "\n",
      "**2. Proteins:**\n",
      "\n",
      "- **Role:**\n",
      "  - Build and repair tissues.\n",
      "  - Create enzymes and other chemicals.\n",
      "  - Help regulate blood sugar levels.\n",
      "\n",
      "**3. Fats:**\n",
      "\n",
      "- **Role:**\n",
      "  - Provide energy storage.\n",
      "  - Help absorb vitamins and minerals.\n",
      "  - Help regulate blood sugar levels.\n",
      "\n",
      "**Other Functions:**\n",
      "\n",
      "- Macronutrients play a role in regulating the body's metabolism, which is the process of breaking down food into energy.\n",
      "- They also help to build and repair tissues, and to produce energy for the body.\n",
      "- Macronutrients are essential for overall health and well-being.<eos>\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": "markdown",
   "metadata": {},
   "source": [
    "Woah! That looks like a pretty good answer.\n",
    "\n",
    "But notice how the output contains the prompt text as well?\n",
    "\n",
    "How about we do a little formatting to replace the prompt in the output text?\n",
    "\n",
    "> **Note:** `\"<bos>\"` and `\"<eos>\"` are special tokens to denote \"beginning of sentence\" and \"end of sentence\" respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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 are the macronutrients and their roles in the human body:\n",
      "\n",
      "**Macronutrients:**\n",
      "\n",
      "**1. Carbohydrates:**\n",
      "\n",
      "- **Role:**\n",
      "  - Provide energy for the body.\n",
      "  - Help regulate blood sugar levels.\n",
      "  - Provide fiber for digestion and gut health.\n",
      "\n",
      "**2. Proteins:**\n",
      "\n",
      "- **Role:**\n",
      "  - Build and repair tissues.\n",
      "  - Create enzymes and other chemicals.\n",
      "  - Help regulate blood sugar levels.\n",
      "\n",
      "**3. Fats:**\n",
      "\n",
      "- **Role:**\n",
      "  - Provide energy storage.\n",
      "  - Help absorb vitamins and minerals.\n",
      "  - Help regulate blood sugar levels.\n",
      "\n",
      "**Other Functions:**\n",
      "\n",
      "- Macronutrients play a role in regulating the body's metabolism, which is the process of breaking down food into energy.\n",
      "- They also help to build and repair tissues, and to produce energy for the body.\n",
      "- Macronutrients are essential for overall health and well-being.\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": "markdown",
   "metadata": {},
   "source": [
    "How cool is that!\n",
    "\n",
    "We just officially generated text from an LLM running locally.\n",
    "\n",
    "So we've covered the R (retrieval) and G (generation) of RAG.\n",
    "\n",
    "How about we check out the last step?\n",
    "\n",
    "Augmentation.\n",
    "\n",
    "First, let's put together a list of queries we can try out with our pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": "markdown",
   "metadata": {},
   "source": [
    "And now let's check if our `retrieve_relevant_resources()` function works with our list of queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: How do vitamins and minerals differ in their roles and importance for health?\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00004 seconds.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([0.6322, 0.6220, 0.6187, 0.6178, 0.6128], device='cuda:0'),\n",
       " tensor([ 51,  47, 874,  41, 927], device='cuda:0'))"
      ]
     },
     "execution_count": 51,
     "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": [
    "Beautiful!\n",
    "\n",
    "Let's augment!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Augmenting our prompt with context items\n",
    "\n",
    "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",
    "Let's write a function called `prompt_formatter` that takes in a query and our list of context items (in our case it'll be select indices from our list of dictionaries inside `pages_and_chunks`) and then formats the query with text from the context items.\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": 52,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Looking good! Let's try our function out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: How often should infants be breastfed?\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00003 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",
      "- milk is the best source to fulfill nutritional requirements. An exclusively breastfed infant does not even need extra water, including in hot climates. A newborn infant (birth to 28 days) requires feedings eight to twelve times a day or more. Between 1 and 3 months of age, the breastfed infant becomes more efficient, and the number of feedings per day often become fewer even though the amount of milk consumed stays the same. After about six months, infants can gradually begin to consume solid foods to help meet nutrient needs. Foods that are added in addition to breastmilk are called complementary foods. Complementary foods should be nutrient dense to provide optimal nutrition. Complementary foods include baby meats, vegetables, fruits, infant cereal, and dairy products such as yogurt, but not infant formula. Infant formula is a substitute, not a complement to breastmilk. In addition to complementary foods, the World Health Organization recommends that breastfeeding continue up to 2 years of age or beyond, and the American Academy of Pediatrics recommends at least one year of breastfeeding, or longer.\n",
      "- Most mothers can make enough milk for their babies whether they have small or large breasts. It is normal for a woman to have one breast that is different than the other in size or shape. During pregnancy, the breasts will normally increase in size. If a woman does not notice any changes to her breast during pregnancy, she should discuss this with her healthcare provider or a lactation consultant. After the birth of the baby, nutritional needs must be met to ensure that an infant not only survives, but thrives from infancy into childhood. Breastfeeding provides the fuel a newborn needs for rapid growth and development. As a result, the WHO recommends that breastfeeding is exclusive (no other food or drink) for the first six months of an infant’s life. Exclusive breastfeeding is one of the best ways a mother can support the growth and protect the health of her infant child. Breast milk contains nearly all of the nutrients that a newborn requires and gives a child the best start to a healthy life. Most women want to breastfeed their babies; in the US, over 80% of women start to breastfeed their infants.\n",
      "- child is 1 year old.7 See: https://wicworks.fns.usda.gov/resources/ 50-state-summary-breastfeeding-laws Everyday Connection In the Pacific, the state of Hawai‘i and and the territory of Guam are mandated to provide several accommodations within the workplace for new mothers. All employers are to required to allow breastfeeding mothers adequate break time to pump or nurse in location other than a bathroom. To learn more about Hawai‘i’s laws visit http://health.hawaii.gov/wic/files/2013/04/ WorkplaceBreastfeeding_02_2014.pdf and for Guam’s laws visit http://www.guamcourts.org/CompilerofLaws/GCA/ 10gca/10gc092A.pdf. Members of a community can also promote and support breastfeeding. New mothers can join peer counseling groups or turn to other women within their community who have previous experience with breastfeeding. In addition, community-based programs can provide education and support. The US Department of Agriculture’s Women, Infants, and Children program provides information on breastfeeding and both professional and peer support for women and their babies from low-income families. Launched in 2004, the Loving Support program combines peer counseling with breastfeeding promotion efforts to increase 7. Hawaiʻi State Judiciary. (2020).\n",
      "- is making sure that children receive an adequate amount of needed nutrients to provide a strong foundation for the rest of their lives. Most expectant mothers begin thinking about how they will feed their baby early in their pregnancy. Therefore, it is important to offer breastfeeding education and support starting with the first prenatal appointment. Support from family members, especially the baby’s father and grandmother, plus friends, employers, and others can greatly help with both the decision-making process during pregnancy and the beginning and maintenance of breastfeeding after the baby’s birth. In the United States, about 83.2 percent of babies start out being breastfed.1 Yet by the age of six months, when solid foods should begin to be introduced into a child’s diet along with breast milk, only about 25 percent of infants in the United States were still breastfed exclusively, according to the Centers for Disease Control and Prevention (CDC).2 The approval and assistance of family members, friends, employers, health-care providers, and policymakers can make an enormous difference and provide the needed promotion and support for mothers who wish to breastfeed their children. Education about breastfeeding typically begins with health-care providers. During prenatal care and often soon after a woman has given birth, doctors, nurses, and other clinicians can explain the benefits of breastfeeding and describe the proper technique. Nearly all births in the United States and Canada occur in hospital settings, and hospital practices in labor, delivery, postpartum care, and discharge planning can inform and support women who want to 1. U. S. Centers for Disease Control. (2018).\n",
      "- caloric content in breast milk is made up of fat. A high-fat diet that includes cholesterol is necessary to support the development of neural pathways in the brain and throughout the body. However, saturated fats and trans fatty acids inhibit this growth. Infants who are over the age of six months, which means they are receiving complementary foods, should not consume foods that are high in these types of fats. The RDA (AI) for total fat is 30 grams per day for infants 0-6 months old and 31 grams per day for infants 7-12 months old. Micronutrients Almost all of the nutrients that infants require during the first 6 months can be met if they consume an adequate amount of breast milk. There are a few exceptions, though. Unless the mother is taking a large dose of Vitamin D, human milk will be low in vitamin D, which is needed for calcium absorption and building bone, among other things. Therefore, breastfed children often need to take a vitamin D supplement in the form of drops. Infants at the highest risk for vitamin D deficiency are those with darker skin and little to no exposure to sunlight, and infants born prematurely.\n",
      "\n",
      "Relevant passages: <extract relevant passages from the context here>\n",
      "User query: How often should infants be breastfed?\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": "markdown",
   "metadata": {},
   "source": [
    "What a good looking prompt!\n",
    "\n",
    "We can tokenize this and pass it straight to our LLM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: How often should infants be breastfed?\n",
      "RAG answer:\n",
      "<bos>**Relevant passages:**\n",
      "\n",
      "\"A newborn infant (birth to 28 days) requires feedings eight to twelve times a day or more. Between 1 and 3 months of age, the breastfed infant becomes more efficient, and the number of feedings per day often become fewer even though the amount of milk consumed stays the same. After about six months, infants can gradually begin to consume solid foods to help meet nutrient needs.\"\n",
      "\n",
      "\"The US Department of Agriculture’s Women, Infants, and Children program provides information on breastfeeding and both professional and peer support for women and their babies from low-income families.\"\n",
      "\n",
      "**Answer:**\n",
      "\n",
      "Infants should be breastfed eight to twelve times a day or more when they are newborns and as often as they need when they are between 1 and 3 months old. After about six months, infants can gradually begin to consume solid foods to help meet nutrient needs.<eos>\n",
      "CPU times: total: 266 ms\n",
      "Wall time: 6.11 s\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": [
    "Yesssssss!!!\n",
    "\n",
    "Our RAG pipeline is complete!\n",
    "\n",
    "We just Retrieved, Augmented and Generated!\n",
    "\n",
    "And all on our own local GPU!\n",
    "\n",
    "How about we functionize the generation step to make it easier to use?\n",
    "\n",
    "We can put a little formatting on the text being returned to make it look nice too.\n",
    "\n",
    "And we'll make an option to return the context items if needed as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": "markdown",
   "metadata": {},
   "source": [
    "What a good looking function!\n",
    "\n",
    "The workflow could probably be a little refined but this should work!\n",
    "\n",
    "Let's try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: What are the macronutrients, and what roles do they play in the human body?\n",
      "[INFO] Time taken to get scores on 1680 embeddings: 0.00003 seconds.\n",
      "Answer:\n",
      "\n",
      "Sure, here is the answer to the query:  Macronutrients are the nutrients that\n",
      "are needed in large amounts by the human body. There are three classes of\n",
      "macronutrients: carbohydrates, lipids, and proteins. These macronutrients can be\n",
      "metabolically processed into cellular energy.  Carbohydrates are molecules\n",
      "composed of carbon, hydrogen, and oxygen. Proteins are macromolecules composed\n",
      "of chains of subunits called amino acids. Lipids are molecules that are composed\n",
      "of fatty acids and glycerol. Water is also a macronutrient in the sense that you\n",
      "require a large amount of it, but unlike the other macronutrients, it does not\n",
      "yield calories.\n",
      "Context items:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'page_number': 5,\n",
       "  'sentence_chunk': 'Macronutrients Nutrients that are needed in large amounts are called macronutrients. There are three classes of macronutrients: carbohydrates, lipids, and proteins. These can be metabolically processed into cellular energy. The energy from macronutrients comes from their chemical bonds. This chemical energy is converted into cellular energy that is then utilized to perform work, allowing our bodies to conduct their basic functions. A unit of measurement of food energy is the calorie. On nutrition food labels the amount given for “calories” is actually equivalent to each calorie multiplied by one thousand. A kilocalorie (one thousand calories, denoted with a small “c”) is synonymous with the “Calorie” (with a capital “C”) on nutrition food labels. Water is also a macronutrient in the sense that you require a large amount of it, but unlike the other macronutrients, it does not yield calories. Carbohydrates Carbohydrates are molecules composed of carbon, hydrogen, and oxygen.',\n",
       "  'chunk_char_count': 987,\n",
       "  'chunk_word_count': 149,\n",
       "  'chunk_token_count': 246.75,\n",
       "  'embedding': array([ 5.12206331e-02, -4.26196903e-02,  1.97356213e-02,  1.30613428e-02,\n",
       "          5.76598793e-02,  1.50817726e-02, -8.98823887e-02,  3.10131032e-02,\n",
       "         -2.98853926e-02, -3.47163007e-02,  3.21013033e-02,  1.07060699e-02,\n",
       "          2.06893142e-02,  3.23249958e-02,  3.62949930e-02, -3.53821479e-02,\n",
       "          6.14871196e-02, -4.20648381e-02, -3.95431370e-02,  3.16182971e-02,\n",
       "          5.24955743e-04,  5.43849403e-03,  3.73274758e-02, -9.44860559e-03,\n",
       "         -1.07091658e-01,  5.05331196e-02,  2.96340678e-02,  1.15391016e-02,\n",
       "         -2.46291282e-03, -5.12202345e-02, -8.93950555e-03, -1.50747655e-03,\n",
       "         -4.07980531e-02, -3.03628184e-02,  2.09010773e-06, -4.28524837e-02,\n",
       "         -3.43207270e-02,  6.94920262e-03, -7.17835575e-02,  1.22952107e-02,\n",
       "         -4.46248613e-03, -5.22793680e-02,  2.00275928e-02, -1.34435901e-02,\n",
       "          4.98107076e-02,  3.58145088e-02,  4.80722301e-02, -3.26666087e-02,\n",
       "         -3.76311690e-02, -7.63266021e-03,  6.88403193e-03, -5.60148805e-03,\n",
       "          2.25823149e-02, -1.74587201e-02,  3.06603722e-02,  4.68475819e-02,\n",
       "          1.86912157e-02,  7.59700388e-02, -1.06622213e-02,  4.57863361e-02,\n",
       "          2.90246867e-02,  1.99847333e-02,  9.43733100e-03, -1.29955215e-02,\n",
       "          5.31571172e-02,  6.15917407e-02, -5.04084006e-02, -2.54436601e-02,\n",
       "         -3.56743694e-04,  5.59728332e-02, -2.37430725e-02,  1.07695553e-02,\n",
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       "  'sentence_chunk': 'Water There is one other nutrient that we must have in large quantities: water. Water does not contain carbon, but is composed of two hydrogens and one oxygen per molecule of water. More than 60 percent of your total body weight is water. Without it, nothing could be transported in or out of the body, chemical reactions would not occur, organs would not be cushioned, and body temperature would fluctuate widely. On average, an adult consumes just over two liters of water per day from food and drink combined. Since water is so critical for life’s basic processes, the amount of water input and output is supremely important, a topic we will explore in detail in Chapter 4. Micronutrients Micronutrients are nutrients required by the body in lesser amounts, but are still essential for carrying out bodily functions. Micronutrients include all the essential minerals and vitamins. There are sixteen essential minerals and thirteen vitamins (See Table 1.1 “Minerals and Their Major Functions” and Table 1.2 “Vitamins and Their Major Functions” for a complete list and their major functions). In contrast to carbohydrates, lipids, and proteins, micronutrients are not sources of energy (calories), but they assist in the process as cofactors or components of enzymes (i.e., coenzymes).',\n",
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       "  'sentence_chunk': 'Figure 1.1 The Macronutrie nts: Carbohydrat es, Lipids, Protein, and Water Proteins Proteins are macromolecules composed of chains of subunits called amino acids. Amino acids are simple subunits composed of carbon, oxygen, hydrogen, and nitrogen. Food sources of proteins include meats, dairy products, seafood, and a variety of different plant- based foods, most notably soy. The word protein comes from a Greek word meaning “of primary importance,” which is an apt description of these macronutrients; they are also known colloquially as the “workhorses” of life. Proteins provide four kilocalories of energy per gram; however providing energy is not protein’s most important function. Proteins provide structure to bones, muscles and skin, and play a role in conducting most of the chemical reactions that take place in the body. Scientists estimate that greater than one-hundred thousand different proteins exist within the human body. The genetic codes in DNA are basically protein recipes that determine the order in which 20 different amino acids are bound together to make thousands of specific proteins. Figure 1.1 The Macronutrients: Carbohydrates, Lipids, Protein, and Water Introduction | 7',\n",
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       "  'sentence_chunk': 'Learning Objectives By the end of this chapter, you will be able to: • Describe basic concepts in nutrition • Describe factors that affect your nutritional needs • Describe the importance of research and scientific methods to understanding nutrition What are Nutrients? The foods we eat contain nutrients. Nutrients are substances required by the body to perform its basic functions. Nutrients must be obtained from our diet, since the human body does not synthesize or produce them. Nutrients have one or more of three basic functions: they provide energy, contribute to body structure, and/or regulate chemical processes in the body. These basic functions allow us to detect and respond to environmental surroundings, move, excrete wastes, respire (breathe), grow, and reproduce. There are six classes of nutrients required for the body to function and maintain overall health. These are carbohydrates, lipids, proteins, water, vitamins, and minerals. Foods also contain non-nutrients that may be harmful (such as natural toxins common in plant foods and additives like some dyes and preservatives) or beneficial (such as antioxidants). 4 | Introduction',\n",
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       "  'sentence_chunk': 'Protein Necessary for tissue formation, cell reparation, and hormone and enzyme production. It is essential for building strong muscles and a healthy immune system. Carbohydrates Provide a ready source of energy for the body and provide structural constituents for the formation of cells. Fat Provides stored energy for the body, functions as structural components of cells and also as signaling molecules for proper cellular communication. It provides insulation to vital organs and works to maintain body temperature. Vitamins Regulate body processes and promote normal body-system functions. Minerals Regulate body processes, are necessary for proper cellular function, and comprise body tissue. Water Transports essential nutrients to all body parts, transports waste products for disposal, and aids with body temperature maintenance.  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).',\n",
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       "         -6.48769503e-03, -1.65818874e-02,  2.36221552e-02,  1.68092102e-02,\n",
       "         -5.17339446e-02,  7.89103732e-02, -1.94502510e-02,  4.24890034e-02,\n",
       "          3.03313639e-02,  2.41035651e-02, -3.09278853e-02, -3.26755866e-02,\n",
       "         -5.68696819e-02,  9.01831314e-03,  9.88617633e-03,  1.29628936e-02,\n",
       "          9.28403717e-03, -2.62818262e-02,  2.61376016e-02, -2.02571484e-03,\n",
       "          8.24925955e-03, -4.66814488e-02, -5.95781840e-02, -1.79754812e-02,\n",
       "         -1.77205820e-02,  2.22428422e-02,  2.36387867e-02, -2.27594767e-02,\n",
       "          5.10682836e-02, -3.73370275e-02,  1.34610590e-02, -2.65712999e-02,\n",
       "          5.57758939e-03,  1.95519123e-02,  1.00956103e-02,  2.46272329e-02,\n",
       "          2.41943691e-02, -2.27064989e-03,  1.46995177e-02, -1.12190535e-02,\n",
       "         -1.28618022e-02,  4.30749990e-02,  4.11585011e-02, -6.21539773e-03,\n",
       "          2.87308222e-34, -2.69641019e-02, -1.73765011e-02, -8.63781665e-03,\n",
       "         -3.49883968e-03, -1.30337933e-02, -2.41319882e-03, -3.32984775e-02,\n",
       "          1.29248472e-02, -2.80584451e-02, -2.85856631e-02, -9.89954825e-03]),\n",
       "  'score': tensor(0.6416)}]"
      ]
     },
     "execution_count": 56,
     "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.\n",
    "\n",
    "However, I'll leave this and a few more interesting ideas as extensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensions\n",
    "\n",
    "* May want to improve text extraction with something like Marker - https://github.com/VikParuchuri/marker\n",
    "* Guide to more advanced PDF extraction - https://towardsdatascience.com/extracting-text-from-pdf-files-with-python-a-comprehensive-guide-9fc4003d517 \n",
    "* See the following prompt engineering resources for more prompting techniques - promptinguide.ai, Brex's Prompt Engineering Guide \n",
    "* What happens when a query comes through that there isn't any context in the textbook on?\n",
    "* Try another embedding model (e.g. Mixed Bread AI large, `mixedbread-ai/mxbai-embed-large-v1`, see: https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)\n",
    "* Try another LLM... (e.g. Mistral-Instruct)\n",
    "* Try different prompts (e.g. see prompting techniques online)\n",
    "* Our example only focuses on text from a PDF, however, we could extend it to include figures and images \n",
    "* Evaluate the answers -> could use another LLM to rate our answers (e.g. use GPT-4 to make)\n",
    "* Vector database/index for larger setup (e.g. 100,000+ chunks)\n",
    "* Libraries/frameworks such as LangChain / LlamaIndex can help do many of the steps for you - so it's worth looking into those next, wanted to recreate a workflow with lower-level tools to show the principles\n",
    "* Optimizations for speed\n",
    "    * See Hugging Face docs for recommended speed ups on GPU - https://huggingface.co/docs/transformers/perf_infer_gpu_one \n",
    "    * Optimum NVIDIA - https://huggingface.co/blog/optimum-nvidia, GitHub: https://github.com/huggingface/optimum-nvidia \n",
    "    * See NVIDIA TensorRT-LLM - https://github.com/NVIDIA/TensorRT-LLM \n",
    "    * See GPT-Fast for PyTorch-based optimizations - https://github.com/pytorch-labs/gpt-fast \n",
    "    * Flash attention 2 (requires Ampere GPUs or newer) - https://github.com/Dao-AILab/flash-attention\n",
    "* Stream text output so it looks prettier (e.g. each token appears as it gets output from the model)\n",
    "* Turn the workflow into an app, see Gradio type chatbots for this - https://www.gradio.app/guides/creating-a-chatbot-fast, see local example: https://www.gradio.app/guides/creating-a-chatbot-fast#example-using-a-local-open-source-llm-with-hugging-face "
   ]
  }
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