From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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657 lines
30 KiB
657 lines
30 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
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"metadata": {}, |
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"source": [ |
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"# Instant Gratification\n", |
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"\n", |
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"## Your first Frontier LLM Project!\n", |
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"\n", |
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"Let's build a useful LLM solution - in a matter of minutes.\n", |
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"\n", |
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"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
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"\n", |
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"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
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"\n", |
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"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
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"\n", |
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"## If you're new to Jupyter Lab\n", |
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"\n", |
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"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
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"\n", |
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"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
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"\n", |
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"If you prefer to work in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
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"\n", |
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"## If you'd like to brush up your Python\n", |
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"\n", |
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"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
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"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
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"\n", |
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"## I am here to help\n", |
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"\n", |
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"If you have any problems at all, please do reach out. \n", |
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"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n", |
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"\n", |
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"## More troubleshooting\n", |
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"\n", |
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"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
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"\n", |
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"## If this is old hat!\n", |
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"\n", |
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"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
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"\n", |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
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" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you do this with me, either at the same time, or (perhaps better) right afterwards. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>\n", |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
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" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import requests\n", |
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"from dotenv import load_dotenv\n", |
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"from bs4 import BeautifulSoup\n", |
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"from IPython.display import Markdown, display\n", |
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"from openai import OpenAI\n", |
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"from io import BytesIO\n", |
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"from PyPDF2 import PdfReader\n", |
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"\n", |
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
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"metadata": {}, |
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"source": [ |
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"# Connecting to OpenAI\n", |
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"\n", |
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"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
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"\n", |
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"## Troubleshooting if you have problems:\n", |
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"\n", |
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"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
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"\n", |
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"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
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"\n", |
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"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
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"\n", |
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"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"API key found and looks good so far!\n" |
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] |
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} |
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], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"\n", |
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"load_dotenv()\n", |
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"api_key = os.getenv('OPENAI_API_KEY')\n", |
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"\n", |
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"# Check the key\n", |
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"\n", |
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"if not api_key:\n", |
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
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"elif not api_key.startswith(\"sk-proj-\"):\n", |
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" 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", |
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"elif api_key.strip() != api_key:\n", |
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" 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", |
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"else:\n", |
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" print(\"API key found and looks good so far!\")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()\n", |
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"\n", |
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"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
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"# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, or try the below line instead:\n", |
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"# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "c5e793b2-6775-426a-a139-4848291d0463", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A class to represent a Webpage\n", |
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"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
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"\n", |
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"class Website:\n", |
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"\n", |
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" def __init__(self, url):\n", |
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" \"\"\"\n", |
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" Create this Website object from the given url using the BeautifulSoup library\n", |
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" \"\"\"\n", |
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" self.url = url\n", |
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" response = requests.get(url)\n", |
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" soup = BeautifulSoup(response.content, 'html.parser')\n", |
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" self.title = soup.title.string if soup.title else \"No title found\"\n", |
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
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" irrelevant.decompose()\n", |
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's try one out. Change the website and add print statements to follow along.\n", |
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"\n", |
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"ed = Website(\"https://edwarddonner.com\")\n", |
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"print(ed.title)\n", |
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"print(ed.text)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
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"metadata": {}, |
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"source": [ |
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"## Types of prompts\n", |
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"\n", |
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"You may know this already - but if not, you will get very familiar with it!\n", |
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"\n", |
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"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
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"\n", |
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"They expect to receive:\n", |
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"\n", |
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"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
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"\n", |
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"**A user prompt** -- the conversation starter that they should reply to" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
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"\n", |
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"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
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"and provides a short summary, ignoring text that might be navigation related. \\\n", |
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"Respond in markdown.\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A function that writes a User Prompt that asks for summaries of websites:\n", |
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"\n", |
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"def user_prompt_for(website):\n", |
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" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
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" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
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"please provide a short summary of this website in markdown. \\\n", |
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"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
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" user_prompt += website.text\n", |
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" return user_prompt" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"print(user_prompt_for(ed))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "c59ebf9e-f54f-4fc4-a55b-00691f157622", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_prompt" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
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"metadata": {}, |
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"source": [ |
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"## Messages\n", |
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"\n", |
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"The API from OpenAI expects to receive messages in a particular structure.\n", |
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"Many of the other APIs share this structure:\n", |
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"\n", |
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"```\n", |
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"[\n", |
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" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
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" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
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"]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# See how this function creates exactly the format above\n", |
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"\n", |
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"def messages_for(website):\n", |
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" return [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
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" ]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "376da664-9a58-41c2-aecd-13fc6b74861b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"messages_for(ed)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Try this out, and then try for a few more websites\n", |
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"\n", |
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"messages_for(ed)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
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"metadata": {}, |
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"source": [ |
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"## Time to bring it together - the API for OpenAI is very simple!" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# And now: call the OpenAI API. You will get very familiar with this!\n", |
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"\n", |
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"def summarize(url):\n", |
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" website = Website(url)\n", |
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" response = openai.chat.completions.create(\n", |
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" model = \"gpt-4o-mini\",\n", |
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" messages = messages_for(website)\n", |
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" )\n", |
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" return response.choices[0].message.content" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"summarize(\"https://edwarddonner.com\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A function to display this nicely in the Jupyter output, using markdown\n", |
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"\n", |
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"def display_summary(url):\n", |
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" summary = summarize(url)\n", |
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" display(Markdown(summary))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://edwarddonner.com\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
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"metadata": {}, |
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"source": [ |
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"# Let's try more websites\n", |
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"\n", |
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"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
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"\n", |
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"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
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"\n", |
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"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
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"\n", |
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"But many websites will work just fine!" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://cnn.com\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://anthropic.com\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "bcb67f86-0c99-4172-abd0-1a4fd2edb77a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://twitch.tv/emikosaitou\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#181;\">Business applications</h2>\n", |
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" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
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"\n", |
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"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>\n", |
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"\n", |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
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" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"Certainly! Here are some practical use cases for practicing summarization:\n", |
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"\n", |
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"- **News Article Summarizer**: Create an application that automatically summarizes news articles from different sources. Users can input a URL or the text of an article, and the tool generates a concise summary.\n", |
|
"\n", |
|
"- **Meeting Notes Aggregator**: Develop a tool that takes meeting transcripts or recordings, processes the text, and then summarizes key points, decisions made, and action items. This can help teams quickly review important discussions.\n", |
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"\n", |
|
"- **Book Summary Generator**: Build a tool that allows users to input chapters or excerpts from books and receive a summarized version. This can be helpful for students or busy professionals looking to grasp content quickly.\n", |
|
"\n", |
|
"- **Research Paper Summarizer**: Create a program that summarizes academic papers. You can input the abstract and main sections, and the tool generates a comprehensive yet brief overview of the research findings.\n", |
|
"\n", |
|
"- **Social Media Content Summarization**: Develop a summarization tool that condenses long threads or posts from platforms like Twitter or Reddit, helping users catch up on long discussions without reading every comment.\n", |
|
"\n", |
|
"These projects can enhance your understanding of summarization techniques and improve your programming skills." |
|
], |
|
"text/plain": [ |
|
"<IPython.core.display.Markdown object>" |
|
] |
|
}, |
|
"metadata": {}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"# Step 1: Create your prompts\n", |
|
"\n", |
|
"system_prompt = \"You are an assistant that helps a programmer become an AI Engineer. Answer the question of the user in 200 words or less. Use bulletpoints to list 3 to 5 recommendations.\"\n", |
|
"user_prompt = \"I want you to recommend me some ideas to practice summorization by giving me possible use cases that I can code.\"\n", |
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"\n", |
|
"# Step 2: Make the messages list\n", |
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"\n", |
|
"messages = [\n", |
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
|
"] # fill this in\n", |
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"\n", |
|
"# Step 3: Call OpenAI\n", |
|
"\n", |
|
"response = openai.chat.completions.create(model = \"gpt-4o-mini\", messages = messages)\n", |
|
"\n", |
|
"# Step 4: print the result\n", |
|
"\n", |
|
"# print(response.choices[0].message.content)\n", |
|
"display(Markdown(response.choices[0].message.content))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 8, |
|
"id": "7a599b00-c676-47b3-b728-2daa75a02b7a", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"ChatCompletion(id='chatcmpl-AZlyoklFxr67K57aWzkigVIXoB45d', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\"### How are micro RNAs produced in the cell? \\n\\nMicroRNAs (miRNAs) are produced through a multi-step process primarily involving the canonical miRNA biogenesis pathway. This process begins with the transcription of primary miRNA transcripts (pri-miRNAs) by RNA polymerase II (Pol II). The Drosha/DGCR complex then precisely cleaves these pri-miRNAs into precursor miRNAs (pre-miRNAs), which contain a stem-loop (hairpin) structure. Lastly, these pre-miRNAs are transported into the cytosol by the Exportin-5/RAN-GTP complex and further processed into mature miRNA duplexes by the Dicer/TRBP complex, which entails removing the loop structure from pre-miRNAs.\\n\\n### What are the functions of the involved proteins?\\n\\n1. **Drosha/DGCR Complex**: This complex is responsible for the initial cleavage of pri-miRNAs into pre-miRNAs in the nucleus.\\n2. **Exportin-5/RAN-GTP Complex**: It facilitates the transport of the pre-miRNAs from the nucleus into the cytoplasm.\\n3. **Dicer/TRBP Complex**: In the cytoplasm, Dicer processes pre-miRNAs into mature miRNA duplexes, essential for the next steps in gene regulation.\\n4. **RNA-Induced Silencing Complex (RISC)**: Formed by Agonaute proteins (like Ago2), which bind to the mature miRNA strand and target mRNAs for silencing, functioning through interactions with the 3' untranslated region (UTR) of these mRNAs. \\n\\nTogether, these proteins coordinate the production and function of miRNAs, which play crucial roles in regulating gene expression and influencing various cellular processes.\", refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None))], created=1733089278, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_0705bf87c0', usage=CompletionUsage(completion_tokens=345, prompt_tokens=15752, total_tokens=16097, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=15616)))\n" |
|
] |
|
}, |
|
{ |
|
"data": { |
|
"text/markdown": [ |
|
"### How are micro RNAs produced in the cell? \n", |
|
"\n", |
|
"MicroRNAs (miRNAs) are produced through a multi-step process primarily involving the canonical miRNA biogenesis pathway. This process begins with the transcription of primary miRNA transcripts (pri-miRNAs) by RNA polymerase II (Pol II). The Drosha/DGCR complex then precisely cleaves these pri-miRNAs into precursor miRNAs (pre-miRNAs), which contain a stem-loop (hairpin) structure. Lastly, these pre-miRNAs are transported into the cytosol by the Exportin-5/RAN-GTP complex and further processed into mature miRNA duplexes by the Dicer/TRBP complex, which entails removing the loop structure from pre-miRNAs.\n", |
|
"\n", |
|
"### What are the functions of the involved proteins?\n", |
|
"\n", |
|
"1. **Drosha/DGCR Complex**: This complex is responsible for the initial cleavage of pri-miRNAs into pre-miRNAs in the nucleus.\n", |
|
"2. **Exportin-5/RAN-GTP Complex**: It facilitates the transport of the pre-miRNAs from the nucleus into the cytoplasm.\n", |
|
"3. **Dicer/TRBP Complex**: In the cytoplasm, Dicer processes pre-miRNAs into mature miRNA duplexes, essential for the next steps in gene regulation.\n", |
|
"4. **RNA-Induced Silencing Complex (RISC)**: Formed by Agonaute proteins (like Ago2), which bind to the mature miRNA strand and target mRNAs for silencing, functioning through interactions with the 3' untranslated region (UTR) of these mRNAs. \n", |
|
"\n", |
|
"Together, these proteins coordinate the production and function of miRNAs, which play crucial roles in regulating gene expression and influencing various cellular processes." |
|
], |
|
"text/plain": [ |
|
"<IPython.core.display.Markdown object>" |
|
] |
|
}, |
|
"metadata": {}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"# Let's make a web scraper that takes in a pdf file hosted on the internet and returns us a summary (or abstract) of the research paper\n", |
|
"\n", |
|
"# Step 0: Create article class\n", |
|
"class Article:\n", |
|
"\n", |
|
" def __init__(self, url):\n", |
|
" \"\"\"\n", |
|
" Create this Article object from the given url using the BeautifulSoup library\n", |
|
" \"\"\"\n", |
|
" self.url = url \n", |
|
" response = requests.get(self.url)\n", |
|
" if response.status_code == 200:\n", |
|
" pdf_bytes = BytesIO(response.content)\n", |
|
" reader = PdfReader(pdf_bytes)\n", |
|
" \n", |
|
" # Step 2: Extract text from each page of the PDF\n", |
|
" text = \"\"\n", |
|
" for page in reader.pages:\n", |
|
" text += page.extract_text()\n", |
|
" \n", |
|
" self.text = text\n", |
|
" self.title = reader.metadata.get(\"/Title\", \"No title found\")\n", |
|
" else:\n", |
|
" print(f\"Failed to fetch PDF. Status code: {response.status_code}\")\n", |
|
" self.text = \"No text found\"\n", |
|
" self.title = \"No title found\"\n", |
|
"\n", |
|
"# Step 1: Create your prompts\n", |
|
"\n", |
|
"def craft_user_prompt(article):\n", |
|
" user_prompt = f\"You are looking at a research article titled {article.title}\\n Based on the body of the article, how are micro RNAs produced in the cell? State the function of the proteins \\\n", |
|
" involved. The body of the article is as follows.\"\n", |
|
" user_prompt += article.text\n", |
|
" return user_prompt\n", |
|
"\n", |
|
"# Step 2: Make the messages list\n", |
|
"def craft_messages(article):\n", |
|
" system_prompt = \"You are an assistant that analyses the contents of a research article and provide answers to the question asked by the user in 250 words or less. \\\n", |
|
" Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.\"\n", |
|
" return [\n", |
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
|
" {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n", |
|
" ]\n", |
|
"\n", |
|
"\n", |
|
"# Step 3: Call OpenAI\n", |
|
"def summarize(url):\n", |
|
" article = Article(url)\n", |
|
" response = openai.chat.completions.create(\n", |
|
" model = \"gpt-4o-mini\",\n", |
|
" messages = craft_messages(article)\n", |
|
" )\n", |
|
" print(response)\n", |
|
" return response.choices[0].message.content\n", |
|
" \n", |
|
"# Step 4: Print the result of an example pdf\n", |
|
"summary = summarize(\"https://www.nature.com/articles/s12276-023-01050-9.pdf\")\n", |
|
"display(Markdown(summary))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
|
"metadata": {}, |
|
"source": [ |
|
"## An extra exercise for those who enjoy web scraping\n", |
|
"\n", |
|
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
|
"metadata": {}, |
|
"source": [ |
|
"# Sharing your code\n", |
|
"\n", |
|
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
|
"\n", |
|
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
|
"\n", |
|
"PR instructions courtesy of an AI friend: https://chatgpt.com/share/670145d5-e8a8-8012-8f93-39ee4e248b4c" |
|
] |
|
} |
|
], |
|
"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": "3.11.10" |
|
} |
|
}, |
|
"nbformat": 4, |
|
"nbformat_minor": 5 |
|
}
|
|
|