270 lines
7.1 KiB
Plaintext
270 lines
7.1 KiB
Plaintext
{
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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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"Let's build a useful LLM solution - in a matter of minutes.\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, be sure to have followed the instructions in the \"README\" file, including creating your API key with OpenAI and adding it to the `.env` file."
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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": "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"
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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": "7b87cadb-d513-4303-baee-a37b6f938e4d",
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"metadata": {},
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"outputs": [],
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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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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
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"openai = OpenAI()"
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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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"\n",
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"class Website:\n",
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" url: str\n",
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" title: str\n",
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" text: str\n",
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"\n",
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" def __init__(self, url):\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\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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"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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"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 += \"The 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": "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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"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": "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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"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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"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": "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": "49c4315f-340b-4371-b6cd-2a772f4b7bdd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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