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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": "markdown",
+ "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
+ "metadata": {},
+ "source": [
+ "# HOMEWORK EXERCISE ASSIGNMENT\n",
+ "\n",
+ "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
+ "\n",
+ "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
+ "\n",
+ "**Benefits:**\n",
+ "1. No API charges - open-source\n",
+ "2. Data doesn't leave your box\n",
+ "\n",
+ "**Disadvantages:**\n",
+ "1. Significantly less power than Frontier Model\n",
+ "\n",
+ "## Recap on installation of Ollama\n",
+ "\n",
+ "Simply visit [ollama.com](https://ollama.com) and install!\n",
+ "\n",
+ "Once complete, the ollama server should already be running locally. \n",
+ "If you visit: \n",
+ "[http://localhost:11434/](http://localhost:11434/)\n",
+ "\n",
+ "You should see the message `Ollama is running`. \n",
+ "\n",
+ "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n",
+ "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n",
+ "Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
+ "\n",
+ "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
+ ]
+ },
+ {
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Constants\n",
+ "\n",
+ "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
+ "HEADERS = {\"Content-Type\": \"application/json\"}\n",
+ "MODEL = \"llama3.2\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a messages list using the same format that we used for OpenAI\n",
+ "\n",
+ "messages = [\n",
+ " {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "payload = {\n",
+ " \"model\": MODEL,\n",
+ " \"messages\": messages,\n",
+ " \"stream\": False\n",
+ " }"
+ ]
+ },
+ {
+ "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": "code",
+ "execution_count": null,
+ "id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If this doesn't work for any reason, try the 2 versions in the following cells\n",
+ "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
+ "# And if none of that works - contact me!\n",
+ "\n",
+ "response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
+ "print(response.json()['message']['content'])"
+ ]
+ },
+ {
+ "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": "code",
+ "execution_count": null,
+ "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ollama\n",
+ "\n",
+ "response = ollama.chat(model=MODEL, messages=messages)\n",
+ "print(response['message']['content'])"
+ ]
+ },
+ {
+ "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",
+ "from openai import OpenAI\n",
+ "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
+ "\n",
+ "response = ollama_via_openai.chat.completions.create(\n",
+ " model=MODEL,\n",
+ " messages=messages\n",
+ ")\n",
+ "\n",
+ "print(response.choices[0].message.content)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
+ "metadata": {},
+ "source": [
+ "## Also trying the amazing reasoning model DeepSeek\n",
+ "\n",
+ "Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
+ "This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
+ "\n",
+ "Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "!ollama pull deepseek-r1:1.5b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d3d554b-e00d-4c08-9300-45e073950a76",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside tags, followed by some decent definitions\n",
+ "\n",
+ "response = ollama_via_openai.chat.completions.create(\n",
+ " model=\"deepseek-r1:1.5b\",\n",
+ " messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
+ ")\n",
+ "\n",
+ "print(response.choices[0].message.content)"
+ ]
+ },
+ {
+ "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": "markdown",
+ "id": "ffaa3470-884c-467e-b4ce-c1b8d39294da",
+ "metadata": {},
+ "source": [
+ "This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A class to represent a Webpage\n",
+ "# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
+ "\n",
+ "# Some websites need you to use proper headers when fetching them:\n",
+ "headers = {\n",
+ " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
+ "}\n",
+ "\n",
+ "class Website:\n",
+ "\n",
+ " def __init__(self, url):\n",
+ " \"\"\"\n",
+ " Create this Website object from the given url using the BeautifulSoup library\n",
+ " \"\"\"\n",
+ " self.url = url\n",
+ " response = requests.get(url, headers=headers)\n",
+ " soup = BeautifulSoup(response.content, 'html.parser')\n",
+ " self.title = soup.title.string if soup.title else \"No title found\"\n",
+ " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
+ " irrelevant.decompose()\n",
+ " self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6fd198df-bac5-42c5-83a0-06c5f71fb76a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's try one out. Change the website and add print statements to follow along.\n",
+ "\n",
+ "ed = Website(\"https://edwarddonner.com\")\n",
+ "print(ed.title)\n",
+ "print(ed.text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "995b637d-a5db-4ad9-ac78-5980fd7ef112",
+ "metadata": {},
+ "source": [
+ "#### Define the system prompt, to instruct the model how we want to respond to our query. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee810d49-e88a-4137-a4be-98812e0d0748",
+ "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.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482b5d4c-69ed-4332-abb5-8b0986dcf368",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d966cb09-3ca2-49f7-8462-f6ef26c01159",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(user_prompt_for(ed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For test purpose\n",
+ "\n",
+ "messages = [\n",
+ " {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
+ " {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f5cb0e9f-eb56-4633-ba4c-76817be98856",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# To give you a preview -- calling ollama with system and user messages:\n",
+ "\n",
+ "import ollama\n",
+ "\n",
+ "response = ollama.chat(model=MODEL, messages=messages)\n",
+ "print(response['message']['content'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# See how this function creates exactly the format above\n",
+ "\n",
+ "def messages_for(website):\n",
+ " return [\n",
+ " {\"role\": \"system\", \"content\": system_prompt},\n",
+ " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
+ " ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b64b814-123f-436d-9366-4c762ac4b89a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this out, and then try for a few more websites\n",
+ "\n",
+ "messages_for(ed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93",
+ "metadata": {},
+ "source": [
+ "### So, here let's run the summarize by using ollama and see how appears."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# And now: call the ollama API. \n",
+ "\n",
+ "def summarize(url):\n",
+ " website = Website(url)\n",
+ " response = ollama.chat(\n",
+ " model = MODEL,\n",
+ " messages = messages_for(website)\n",
+ " )\n",
+ " return response['message']['content']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "summarize(\"https://edwarddonner.com\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A function to display this nicely in the Jupyter output, using markdown\n",
+ "\n",
+ "def display_summary(url):\n",
+ " summary = summarize(url)\n",
+ " display(Markdown(summary))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1cedc9d9-6a76-4225-82c1-82240da16260",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_summary(\"https://edwarddonner.com\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82c48586-33c8-4797-a24f-41602c1297b3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llms",
+ "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": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}