478 lines
16 KiB
Plaintext
478 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
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"metadata": {},
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"source": [
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"# A full business solution\n",
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"\n",
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"## Now we will take our project from Day 1 to the next level\n",
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"\n",
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"### BUSINESS CHALLENGE:\n",
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"\n",
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"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n",
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"\n",
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"We will be provided a company name and their primary website.\n",
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"\n",
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"See the end of this notebook for examples of real-world business applications.\n",
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"\n",
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"And remember: I'm always available if you have problems or ideas! Please do reach out."
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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": "d5b08506-dc8b-4443-9201-5f1848161363",
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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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"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n",
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"\n",
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"import os\n",
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"import requests\n",
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"import json\n",
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"from typing import List\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, update_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": "fc5d8880-f2ee-4c06-af16-ecbc0262af61",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize and constants\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
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" print(\"API key looks good so far\")\n",
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"else:\n",
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
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" \n",
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"MODEL = 'gpt-4o-mini'\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": "106dd65e-90af-4ca8-86b6-23a41840645b",
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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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"# Some websites need you to use proper headers when fetching them:\n",
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"headers = {\n",
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" \"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",
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"}\n",
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"\n",
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"class Website:\n",
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" \"\"\"\n",
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" A utility class to represent a Website that we have scraped, now with links\n",
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" \"\"\"\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, headers=headers)\n",
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" self.body = response.content\n",
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" soup = BeautifulSoup(self.body, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" if soup.body:\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)\n",
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" else:\n",
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" self.text = \"\"\n",
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" links = [link.get('href') for link in soup.find_all('a')]\n",
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" self.links = [link for link in links if link]\n",
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"\n",
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" def get_contents(self):\n",
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" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\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": null,
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"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a",
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"metadata": {},
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"outputs": [],
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"source": [
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"ed = Website(\"https://edwarddonner.com\")\n",
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"ed.links"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1771af9c-717a-4fca-bbbe-8a95893312c3",
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"metadata": {},
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"source": [
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"## First step: Have GPT-4o-mini figure out which links are relevant\n",
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"\n",
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"### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n",
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"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n",
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"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n",
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"\n",
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"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n",
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"\n",
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"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project."
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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": "6957b079-0d96-45f7-a26a-3487510e9b35",
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"metadata": {},
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"outputs": [],
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"source": [
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"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
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"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
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"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
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"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
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"link_system_prompt += \"\"\"\n",
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"{\n",
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" \"links\": [\n",
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" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
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" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n",
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" ]\n",
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"}\n",
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"\"\"\"\n",
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"link_system_prompt += \"And this example:\"\n",
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"link_system_prompt += \"\"\"\n",
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"{\n",
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" \"links\": [\n",
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" {\"type\": \"for-you page\", \"url\": \"https://full.url/goes/here/services\"},\n",
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" {\"type\": \"speak-to-a-human page\", \"url\": \"https://another.full.url/contact-us\"}\n",
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" ]\n",
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"}\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": "b97e4068-97ed-4120-beae-c42105e4d59a",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(link_system_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": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_links_user_prompt(website):\n",
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" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
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" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
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"Do not include Terms of Service, Privacy, email links.\\n\"\n",
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" user_prompt += \"Links (some might be relative links):\\n\"\n",
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" user_prompt += \"\\n\".join(website.links)\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": "6bcbfa78-6395-4685-b92c-22d592050fd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(get_links_user_prompt(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": "a29aca19-ca13-471c-a4b4-5abbfa813f69",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_links(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=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": link_system_prompt},\n",
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" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
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" ],\n",
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" response_format={\"type\": \"json_object\"}\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" return json.loads(result)"
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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": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
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"\n",
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"# anthropic = Website(\"https://anthropic.com\")\n",
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"# anthropic.links\n",
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"# get_links(\"https://anthropic.com\")\n",
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"huggingface = Website(\"https://huggingface.co\")\n",
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"huggingface.links"
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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": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
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"metadata": {},
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"outputs": [],
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"source": [
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"get_links(\"https://huggingface.co\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d74128e-dfb6-47ec-9549-288b621c838c",
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"metadata": {},
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"source": [
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"## Second step: make the brochure!\n",
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"\n",
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"Assemble all the details into another prompt to GPT4-o"
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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": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_all_details(url):\n",
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" result = \"Landing page:\\n\"\n",
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" result += Website(url).get_contents()\n",
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" links = get_links(url)\n",
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" print(\"Found links:\", links)\n",
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" for link in links[\"links\"]:\n",
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" result += f\"\\n\\n{link['type']}\\n\"\n",
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" result += Website(link[\"url\"]).get_contents()\n",
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" return result"
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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": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(get_all_details(\"https://huggingface.co\"))"
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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": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
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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 several relevant pages from a company website \\\n",
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"# and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
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"# Include details of company culture, customers and careers/jobs if you have the information.\"\n",
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"\n",
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"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
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"\n",
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"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
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"and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
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"Include details of company culture, customers and careers/jobs if you have the information.\"\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": null,
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"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_brochure_user_prompt(company_name, url):\n",
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" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
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" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
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" user_prompt += f\"Keep the details brief or concise, factoring in that they would be printed on a simple hand-out flyer.\\n\"\n",
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" user_prompt += get_all_details(url)\n",
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" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\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": "cd909e0b-1312-4ce2-a553-821e795d7572",
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"metadata": {},
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"outputs": [],
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"source": [
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"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")"
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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": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46",
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_brochure(company_name, url):\n",
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" response = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
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" ],\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" # display(Markdown(result))\n",
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" # print(result)\n",
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" return result"
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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": "0029e063-0c07-4712-82d9-536ec3579e80",
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"metadata": {},
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"outputs": [],
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"source": [
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"def translate_brochure(brochure, language):\n",
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" system_prompt_for_language = \"You're an expert in \" + language + \". Translate the brochure!\"\n",
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" response = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt_for_language},\n",
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" {\"role\": \"user\", \"content\": brochure}\n",
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" ],\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" display(Markdown(result))"
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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": "e093444a-9407-42ae-924a-145730591a39",
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"metadata": {},
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"outputs": [],
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"source": [
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"create_brochure(\"HuggingFace\", \"https://huggingface.co\")"
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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": "f8371bf5-c4c0-4e52-9a2a-066d994b0510",
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"metadata": {},
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"outputs": [],
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"source": [
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"brochure = create_brochure(\"Paint and Sip Uganda\", \"https://paintandsipuganda.com/\")\n",
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"# translate_brochure(brochure, \"Spanish\")\n",
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"translate_brochure(brochure, \"Swahili\")"
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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": "34e03db6-61d0-4fc5-bf66-4f679b9befde",
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"metadata": {},
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"outputs": [],
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"source": [
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"create_brochure(\"Wabeh\", \"https://wabeh.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": "61eaaab7-0b47-4b29-82d4-75d474ad8d18",
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"metadata": {},
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"source": [
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"## Finally - a minor improvement\n",
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"\n",
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"With a small adjustment, we can change this so that the results stream back from OpenAI,\n",
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"with the familiar typewriter animation"
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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": "51db0e49-f261-4137-aabe-92dd601f7725",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_brochure(company_name, url):\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" \n",
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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)"
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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": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d",
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"metadata": {},
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"outputs": [],
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"source": [
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"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
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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": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n",
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"\n",
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"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
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]
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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",
|
|
"name": "python",
|
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"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.7"
|
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}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
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}
|