{ "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", " \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", "
" ] }, { "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": 1, "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": 2, "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": 4, "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": 5, "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": 6, "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ‹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ™ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ´ 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ´ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â § \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â ‡ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest â � \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest \u001b[K\u001b[?25h\u001b[?2026l\n", "Error: pull model manifest: Get \"https://registry.ollama.ai/v2/library/llama3.2/manifests/latest\": dial tcp 104.21.75.227:443: i/o timeout\n" ] } ], "source": [ "# Let's just make sure the model is loaded\n", "\n", "!ollama pull llama3.2" ] }, { "cell_type": "code", "execution_count": 7, "id": "42b9f644-522d-4e05-a691-56e7658c0ea9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generative AI has numerous business applications across various industries. Here are some examples:\n", "\n", "1. **Content Creation**: Generative AI can be used to generate high-quality content such as articles, social media posts, and product descriptions, reducing the need for human writers and editors.\n", "2. **Digital Marketing**: Generative AI can help with personalized email marketing campaigns, automated social media content generation, and even creating customized ad copy.\n", "3. **Graphic Design and Visual Content**: Generative AI can generate high-quality graphics, logos, and images, making it easier to create visually appealing marketing materials without requiring extensive design expertise.\n", "4. **Music and Audio Production**: Generative AI can be used to create music tracks, sound effects, and even voiceovers for videos and commercials.\n", "5. **Chatbots and Customer Service**: Generative AI-powered chatbots can help businesses provide 24/7 customer support, answer common queries, and route complex issues to human representatives.\n", "6. **Predictive Maintenance and Quality Control**: Generative AI can be used to analyze sensor data from industrial equipment, predict maintenance needs, and detect anomalies in quality control processes.\n", "7. **Financial Modeling and Analysis**: Generative AI can help with financial forecasting, risk analysis, and portfolio optimization by generating complex models and scenarios.\n", "8. **Cybersecurity Threat Detection**: Generative AI-powered systems can detect and respond to cybersecurity threats by analyzing network traffic patterns and identifying potential vulnerabilities.\n", "9. **Product Design and Development**: Generative AI can be used to create prototypes, simulate product behavior, and optimize design parameters for better performance and user experience.\n", "10. **Data Analysis and Visualization**: Generative AI can help with data analysis, visualization, and insights generation by generating dashboards, reports, and predictive models.\n", "\n", "Some specific business applications of Generative AI include:\n", "\n", "* **Automating customer service chatbots**\n", "* **Generating personalized product recommendations**\n", "* **Creating high-quality content without human writers**\n", "* **Optimizing supply chain operations using predictive analytics**\n", "* **Designing new products and prototypes using generative design tools**\n", "\n", "These are just a few examples of the many business applications of Generative AI. As the technology continues to evolve, we can expect to see even more innovative uses in various industries.\n" ] } ], "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": 9, "id": "22d855a6-1c4f-4293-99b5-7ba9a0d99e6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Collecting ollama\n", " Downloading ollama-0.4.7-py3-none-any.whl.metadata (4.7 kB)\n", "Requirement already satisfied: httpx<0.29,>=0.27 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from ollama) (0.28.1)\n", "Requirement already satisfied: pydantic<3.0.0,>=2.9.0 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from ollama) (2.10.6)\n", "Requirement already satisfied: anyio in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (4.8.0)\n", "Requirement already satisfied: certifi in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (2025.1.31)\n", "Requirement already satisfied: httpcore==1.* in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (1.0.7)\n", "Requirement already satisfied: idna in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (3.10)\n", "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpcore==1.*->httpx<0.29,>=0.27->ollama) (0.14.0)\n", "Requirement already satisfied: annotated-types>=0.6.0 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (0.7.0)\n", "Requirement already satisfied: pydantic-core==2.27.2 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (2.27.2)\n", "Requirement already satisfied: typing-extensions>=4.12.2 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (4.12.2)\n", "Requirement already satisfied: sniffio>=1.1 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from anyio->httpx<0.29,>=0.27->ollama) (1.3.1)\n", "Downloading ollama-0.4.7-py3-none-any.whl (13 kB)\n", "Installing collected packages: ollama\n", "Successfully installed ollama-0.4.7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "!pip install ollama" ] }, { "cell_type": "code", "execution_count": 10, "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generative AI has numerous business applications across various industries, including:\n", "\n", "1. **Content Creation**: Generative AI can be used to create content such as images, videos, and text based on a given prompt or dataset. This can be useful for businesses that need to generate large amounts of content quickly, such as marketing agencies, e-commerce sites, and news organizations.\n", "2. **Product Design**: Generative AI can be used to design new products, such as fashion designs, interior designs, and product prototypes. This can help businesses reduce the time and cost associated with traditional design methods.\n", "3. **Customer Service Chatbots**: Generative AI can be used to create chatbots that can respond to customer inquiries and provide personalized support. This can improve customer experience and reduce the workload of human customer service representatives.\n", "4. **Marketing Automation**: Generative AI can be used to generate targeted marketing campaigns, such as personalized emails, social media posts, and ad copy. This can help businesses increase their ROI and improve their marketing efficiency.\n", "5. **Data Analysis**: Generative AI can be used to analyze large datasets and identify patterns and trends that may not be visible to human analysts. This can help businesses make data-driven decisions and gain a competitive advantage.\n", "6. **Predictive Maintenance**: Generative AI can be used to predict equipment failures and schedule maintenance, reducing downtime and increasing efficiency for industries such as manufacturing and healthcare.\n", "7. **Cybersecurity**: Generative AI can be used to detect and respond to cyber threats in real-time, improving the security posture of businesses and protecting sensitive data.\n", "8. **Supply Chain Optimization**: Generative AI can be used to optimize supply chain operations, including demand forecasting, inventory management, and logistics planning.\n", "9. **Financial Analysis**: Generative AI can be used to analyze financial data and identify trends and patterns that may not be visible to human analysts. This can help businesses make informed investment decisions and improve their financial performance.\n", "10. **Creative Writing and Copywriting**: Generative AI can be used to generate high-quality written content, such as blog posts, articles, and copy for marketing campaigns.\n", "\n", "Some specific examples of businesses that are using generative AI include:\n", "\n", "* **Google** uses generative AI to create personalized search results and recommend products to users.\n", "* **Amazon** uses generative AI to personalize product recommendations and create targeted advertising campaigns.\n", "* **Dell** uses generative AI to design new products and optimize supply chain operations.\n", "* **McKinsey** uses generative AI to analyze complex data sets and identify trends and patterns that may not be visible to human analysts.\n", "\n", "Overall, generative AI has the potential to transform various business processes and improve efficiency, productivity, and decision-making.\n" ] } ], "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": "9f9e22da-b891-41f6-9ac9-bd0c0a5f4f44", "metadata": {}, "source": [ "## Are you confused about why that works?\n", "\n", "It seems strange, right? We just used OpenAI code to call Ollama?? What's going on?!\n", "\n", "Here's the scoop:\n", "\n", "The python class `OpenAI` is simply code written by OpenAI engineers that makes calls over the internet to an endpoint. \n", "\n", "When you call `openai.chat.completions.create()`, this python code just makes a web request to the following url: \"https://api.openai.com/v1/chat/completions\"\n", "\n", "Code like this is known as a \"client library\" - it's just wrapper code that runs on your machine to make web requests. The actual power of GPT is running on OpenAI's cloud behind this API, not on your computer!\n", "\n", "OpenAI was so popular, that lots of other AI providers provided identical web endpoints, so you could use the same approach.\n", "\n", "So Ollama has an endpoint running on your local box at http://localhost:11434/v1/chat/completions \n", "And in week 2 we'll discover that lots of other providers do this too, including Gemini and DeepSeek.\n", "\n", "And then the team at OpenAI had a great idea: they can extend their client library so you can specify a different 'base url', and use their library to call any compatible API.\n", "\n", "That's it!\n", "\n", "So when you say: `ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')` \n", "Then this will make the same endpoint calls, but to Ollama instead of OpenAI." ] }, { "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": {}, "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": "code", "execution_count": 12, "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "from dotenv import load_dotenv\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display\n", "from openai import OpenAI\n", "\n", "# If you get an error running this cell, then please head over to the troubleshooting notebook!" ] }, { "cell_type": "code", "execution_count": 13, "id": "bc659c20-7915-42c3-8b83-e5654ab66f04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "API key found and looks good so far!\n" ] } ], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv(override=True)\n", "api_key = os.getenv('OPENAI_API_KEY')\n", "\n", "# Check the key\n", "\n", "if not api_key:\n", " print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", "elif not api_key.startswith(\"sk-proj-\"):\n", " print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", "elif api_key.strip() != api_key:\n", " print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", "else:\n", " print(\"API key found and looks good so far!\")\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "09b8d98a-028d-4553-b259-3099e91b2204", "metadata": {}, "outputs": [], "source": [ "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": 15, "id": "ee71fce2-d9a5-4f77-afc7-abfcee78b0ad", "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": 17, "id": "f1a37654-9cc8-4e6c-8fbd-f64654870e05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Home - Edward Donner\n", "Home\n", "Connect Four\n", "Outsmart\n", "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n", "About\n", "Posts\n", "Well, hi there.\n", "I’m Ed. I like writing code and experimenting with LLMs, and hopefully you’re here because you do too. I also enjoy DJing (but I’m badly out of practice), amateur electronic music production (\n", "very\n", "amateur) and losing myself in\n", "Hacker News\n", ", nodding my head sagely to things I only half understand.\n", "I’m the co-founder and CTO of\n", "Nebula.io\n", ". We’re applying AI to a field where it can make a massive, positive impact: helping people discover their potential and pursue their reason for being. Recruiters use our product today to source, understand, engage and manage talent. I’m previously the founder and CEO of AI startup untapt,\n", "acquired in 2021\n", ".\n", "We work with groundbreaking, proprietary LLMs verticalized for talent, we’ve\n", "patented\n", "our matching model, and our award-winning platform has happy customers and tons of press coverage.\n", "Connect\n", "with me for more!\n", "January 23, 2025\n", "LLM Workshop – Hands-on with Agents – resources\n", "December 21, 2024\n", "Welcome, SuperDataScientists!\n", "November 13, 2024\n", "Mastering AI and LLM Engineering – Resources\n", "October 16, 2024\n", "From Software Engineer to AI Data Scientist – resources\n", "Navigation\n", "Home\n", "Connect Four\n", "Outsmart\n", "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n", "About\n", "Posts\n", "Get in touch\n", "ed [at] edwarddonner [dot] com\n", "www.edwarddonner.com\n", "Follow me\n", "LinkedIn\n", "Twitter\n", "Facebook\n", "Subscribe to newsletter\n", "Type your email…\n", "Subscribe\n" ] } ], "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": "code", "execution_count": 21, "id": "19a139ae-23d1-4857-8de5-d6d8eb699d62", "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", "# 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": 23, "id": "2de423cf-86bc-4f35-acd9-0ef821bbee05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### Website Summary\n", "\n", "#### Overview\n", "The website is dedicated to Edward Donner, the co-founder and CTO of Nebula.io, a company that applies AI to help people discover their potential. The site features various sections, including an arena called \"Outsmart\" where LLMs compete with each other, as well as a blog section.\n", "\n", "#### News/Announcements\n", "- **December 21, 2024**: LLM Workshop – Hands-on with Agents – resources\n", "- **November 13, 2024**: Welcome, SuperDataScientists!\n", "- **October 16, 2024**: From Software Engineer to AI Data Scientist – resources\n", "- **January 23, 2025**: LLM Workshop – Hands-on with Agents – resources (upcoming event)\n", "\n", "#### Social Media Links\n", "The website includes links to Edward Donner's profiles on LinkedIn, Twitter, Facebook, and allows users to subscribe to a newsletter.\n" ] } ], "source": [ "# Constants\n", "\n", "OLLAMA_API = \"http://localhost:11434/api/chat\"\n", "HEADERS = {\"Content-Type\": \"application/json\"}\n", "MODEL = \"llama3.2\"\n", "\n", "import ollama\n", "\n", "website = Website(\"https://edwarddonner.com\")\n", "\n", "response = ollama.chat(model=MODEL, messages=messages_for(website))\n", "print(response['message']['content'])" ] }, { "cell_type": "code", "execution_count": null, "id": "4500144f-dd2c-41d5-8225-3163edf1db2d", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }