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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+ "metadata": {},
+ "source": [
+ "# Welcome to your first assignment!\n",
+ "\n",
+ "Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
+ "metadata": {},
+ "source": [
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " Just before we get to the assignment --\n",
+ " I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides. \n",
+ " https://edwarddonner.com/2024/11/13/llm-engineering-resources/ \n",
+ " Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9cc85216-f6e4-436e-b6c1-976c8f2d1152",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install webdriver-manager\n",
+ "!pip install selenium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports\n",
+ "\n",
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "from IPython.display import Markdown, display\n",
+ "import ollama\n",
+ "from openai import OpenAI\n",
+ "from selenium import webdriver\n",
+ "from selenium.webdriver.chrome.options import Options\n",
+ "from selenium.webdriver.chrome.service import Service\n",
+ "from webdriver_manager.chrome import ChromeDriverManager\n",
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Constants\n",
+ "MODEL = \"llama3.2\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's just make sure the model is loaded\n",
+ "\n",
+ "!ollama pull llama3.2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
+ "metadata": {},
+ "source": [
+ "# Introducing the ollama package\n",
+ "\n",
+ "And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
+ "\n",
+ "Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
+ "metadata": {},
+ "source": [
+ "## Alternative approach - using OpenAI python library to connect to Ollama"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# There's actually an alternative approach that some people might prefer\n",
+ "# You can use the OpenAI client python library to call Ollama:\n",
+ "\n",
+ "\n",
+ "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
+ "metadata": {},
+ "source": [
+ "# NOW the exercise for you\n",
+ "\n",
+ "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8251b6a5-7b43-42b9-84a9-4a94b6bdb933",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A class to represent a Webpage\n",
+ "class ScrapeWebsite:\n",
+ " def __init__(self, url):\n",
+ " \"\"\"\n",
+ " Create this Website object from the given URL using Selenium + BeautifulSoup\n",
+ " Supports JavaScript-heavy and normal websites uniformly.\n",
+ " \"\"\"\n",
+ " self.url = url\n",
+ "\n",
+ " # Configure headless Chrome\n",
+ " options = Options()\n",
+ " options.add_argument('--headless')\n",
+ " options.add_argument('--no-sandbox')\n",
+ " options.add_argument('--disable-dev-shm-usage')\n",
+ "\n",
+ " # Use webdriver-manager to manage ChromeDriver\n",
+ " service = Service(ChromeDriverManager().install())\n",
+ "\n",
+ " # Initialize the Chrome WebDriver with the service and options\n",
+ " driver = webdriver.Chrome(service=service, options=options)\n",
+ "\n",
+ " # Start Selenium WebDriver\n",
+ " driver.get(url)\n",
+ "\n",
+ " # Wait for JS to load (adjust as needed)\n",
+ " time.sleep(3)\n",
+ "\n",
+ " # Fetch the page source after JS execution\n",
+ " page_source = driver.page_source\n",
+ " driver.quit()\n",
+ "\n",
+ " # Parse the HTML content with BeautifulSoup\n",
+ " soup = BeautifulSoup(page_source, 'html.parser')\n",
+ "\n",
+ " # Extract title\n",
+ " self.title = soup.title.string if soup.title else \"No title found\"\n",
+ "\n",
+ " # Remove unnecessary elements\n",
+ " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
+ " irrelevant.decompose()\n",
+ "\n",
+ " # Extract the main text\n",
+ " self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
+ "\n",
+ "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
+ "and provides a short summary, ignoring text that might be navigation related. \\\n",
+ "Respond in markdown.\"\n",
+ "\n",
+ "# A function that writes a User Prompt that asks for summaries of websites:\n",
+ "\n",
+ "def user_prompt_for(website):\n",
+ " user_prompt = f\"You are looking at a website titled {website.title}\"\n",
+ " user_prompt += \"\\nThe contents of this website is as follows; \\\n",
+ "please provide a short summary of this website in markdown. \\\n",
+ "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
+ " user_prompt += website.text\n",
+ " return user_prompt\n",
+ "\n",
+ "def messages_for(website):\n",
+ " return [\n",
+ " {\"role\": \"system\", \"content\": system_prompt},\n",
+ " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
+ " ]\n",
+ "\n",
+ "# And now: call the OpenAI API. You will get very familiar with this!\n",
+ "\n",
+ "def summarize(url):\n",
+ " website = ScrapeWebsite(url)\n",
+ " response = ollama_via_openai.chat.completions.create(\n",
+ " model = MODEL,\n",
+ " messages = messages_for(website)\n",
+ " )\n",
+ " return response.choices[0].message.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5dbf8d5c-a42a-4a72-b3a4-c75865b841bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "summary = summarize(\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\")\n",
+ "display(Markdown(summary))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ddfacdc-b16a-4999-9ff2-93ed19600d24",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.12"
+ }
+ },
+ "nbformat": 4,
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+}