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"# YOUR FIRST LAB\n",
"### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n",
"\n",
"## Your first Frontier LLM Project\n",
"\n"
]
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"API key found and looks good so far!\n",
"Hello! It’s great to hear from you! How can I assist you today?\n"
]
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"text/markdown": [
"# Edward Donner Website Summary\n",
"\n",
"Edward Donner's website serves as a personal platform where he shares insights related to code, LLMs (Large Language Models), and his interests in music production and technology. \n",
"\n",
"## About Ed\n",
"- Ed is the co-founder and CTO of Nebula.io, an AI-driven company focused on talent discovery and engagement.\n",
"- He previously founded untapt, an AI startup that was acquired in 2021.\n",
"\n",
"## Features\n",
"- **Connect Four**: A unique arena designed for LLMs to engage in simulated diplomacy and strategy.\n",
"- **Outsmart**: Another interactive feature related to LLMs.\n",
"\n",
"## News and Announcements\n",
"- **January 23, 2025**: Announcement of a workshop titled \"LLM Workshop – Hands-on with Agents\" providing resources for participants.\n",
"- **December 21, 2024**: Welcoming message for \"SuperDataScientists.\"\n",
"- **November 13, 2024**: Resources available for the topic \"Mastering AI and LLM Engineering.\"\n",
"- **October 16, 2024**: Resources provided for transitioning from a software engineer to an AI data scientist.\n",
"\n",
"The website emphasizes Ed's passion for technology and AI, highlighting his professional achievements and ongoing projects."
],
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"source": [
"#!/usr/bin/env python\n",
"# coding: utf-8\n",
"\n",
"# # YOUR FIRST LAB\n",
"# ## Your first Frontier LLM Project\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",
"# # Connecting to OpenAI (or Ollama)\n",
"# Load environment variables in a file called .env\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\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",
"\n",
"# Open API connection\n",
"openai = OpenAI()\n",
"\n",
"# To give you a preview -- calling OpenAI with these messages is this easy: openai.chat.completions.create(model, messages)\n",
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\", \n",
" messages=[\n",
" {\"role\":\"user\", \"content\":message}\n",
" ]\n",
")\n",
"print(response.choices[0].message.content)\n",
"\n",
"# A class to represent a Webpage\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)\n",
"\n",
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\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",
"user_prompt_content = \"\\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",
"\n",
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += user_prompt_content\n",
" user_prompt += website.text\n",
" return user_prompt\n",
"\n",
"# See how this function creates exactly the format above\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]\n",
"\n",
"# Try this out, and then try for a few more websites\n",
"# messages_for(ed)\n",
"\n",
"# And now: call the OpenAI API. You will get very familiar with this!\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content\n",
"\n",
"# A function to display this nicely in the Jupyter output, using markdown\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))\n",
"\n",
"website_url = \"https://edwarddonner.com\"\n",
"\n",
"# Let's try one out. Change the website and add print statements to follow along.\n",
"\n",
"ed = Website(website_url)\n",
"display_summary(website_url)"
]
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