{ "cells": [ { "cell_type": "markdown", "id": "a98030af-fcd1-4d63-a36e-38ba053498fa", "metadata": {}, "source": [ "# A full business solution\n", "\n", "Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", "\n", "We will be provided a company name and their primary website." ] }, { "cell_type": "code", "execution_count": null, "id": "d5b08506-dc8b-4443-9201-5f1848161363", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "import json\n", "from typing import List\n", "from dotenv import load_dotenv\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display, update_display\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": null, "id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", "metadata": {}, "outputs": [], "source": [ "# Initialize and constants\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", "MODEL = 'gpt-4o-mini'\n", "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, "id": "106dd65e-90af-4ca8-86b6-23a41840645b", "metadata": {}, "outputs": [], "source": [ "# A class to represent a Webpage\n", "\n", "class Website:\n", " url: str\n", " title: str\n", " body: str\n", " links: List[str]\n", "\n", " def __init__(self, url):\n", " self.url = url\n", " response = requests.get(url)\n", " self.body = response.content\n", " soup = BeautifulSoup(self.body, 'html.parser')\n", " self.title = soup.title.string if soup.title else \"No title found\"\n", " if soup.body:\n", " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", " irrelevant.decompose()\n", " self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", " else:\n", " self.text = \"\"\n", " links = [link.get('href') for link in soup.find_all('a')]\n", " self.links = [link for link in links if link]\n", "\n", " def get_contents(self):\n", " return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" ] }, { "cell_type": "code", "execution_count": null, "id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", "metadata": {}, "outputs": [], "source": [ "ed = Website(\"https://edwarddonner.com\")\n", "print(ed.get_contents())" ] }, { "cell_type": "markdown", "id": "1771af9c-717a-4fca-bbbe-8a95893312c3", "metadata": {}, "source": [ "## First step: Have GPT-4o-mini figure out which links are relevant\n", "\n", "### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n", "It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n", "We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt." ] }, { "cell_type": "code", "execution_count": null, "id": "6957b079-0d96-45f7-a26a-3487510e9b35", "metadata": {}, "outputs": [], "source": [ "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", "link_system_prompt += \"You should respond in JSON as in this example:\"\n", "link_system_prompt += \"\"\"\n", "{\n", " \"links\": [\n", " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", " {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", " ]\n", "}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", "metadata": {}, "outputs": [], "source": [ "def get_links_user_prompt(website):\n", " user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", " 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", "Do not include Terms of Service, Privacy, email links.\\n\"\n", " user_prompt += \"Links (some might be relative links):\\n\"\n", " user_prompt += \"\\n\".join(website.links)\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "6bcbfa78-6395-4685-b92c-22d592050fd7", "metadata": {}, "outputs": [], "source": [ "print(get_links_user_prompt(ed))" ] }, { "cell_type": "code", "execution_count": null, "id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", "metadata": {}, "outputs": [], "source": [ "def get_links(url):\n", " website = Website(url)\n", " completion = openai.chat.completions.create(\n", " model=MODEL,\n", " messages=[\n", " {\"role\": \"system\", \"content\": link_system_prompt},\n", " {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", " ],\n", " response_format={\"type\": \"json_object\"}\n", " )\n", " result = completion.choices[0].message.content\n", " return json.loads(result)" ] }, { "cell_type": "code", "execution_count": null, "id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", "metadata": {}, "outputs": [], "source": [ "get_links(\"https://anthropic.com\")" ] }, { "cell_type": "markdown", "id": "0d74128e-dfb6-47ec-9549-288b621c838c", "metadata": {}, "source": [ "## Second step: make the brochure!\n", "\n", "Assemble all the details into another prompt to GPT4-o" ] }, { "cell_type": "code", "execution_count": null, "id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", "metadata": {}, "outputs": [], "source": [ "def get_all_details(url):\n", " result = \"Landing page:\\n\"\n", " result += Website(url).get_contents()\n", " links = get_links(url)\n", " print(\"Found links:\", links)\n", " for link in links[\"links\"]:\n", " result += f\"\\n\\n{link['type']}\\n\"\n", " result += Website(link[\"url\"]).get_contents()\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", "metadata": {}, "outputs": [], "source": [ "print(get_all_details(\"https://anthropic.com\"))" ] }, { "cell_type": "code", "execution_count": null, "id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", "metadata": {}, "outputs": [], "source": [ "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", "and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", "Include details of company culture, customers and careers/jobs if you have the information.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", "metadata": {}, "outputs": [], "source": [ "def get_brochure_user_prompt(company_name, url):\n", " user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", " 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", " user_prompt += get_all_details(url)\n", " user_prompt = user_prompt[:20_000] # Truncate if more than 20,000 characters\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", "metadata": {}, "outputs": [], "source": [ "def create_brochure(company_name, url):\n", " response = openai.chat.completions.create(\n", " model=MODEL,\n", " messages=[\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", " ],\n", " )\n", " result = response.choices[0].message.content\n", " display(Markdown(result))" ] }, { "cell_type": "code", "execution_count": null, "id": "e093444a-9407-42ae-924a-145730591a39", "metadata": {}, "outputs": [], "source": [ "create_brochure(\"Anthropic\", \"https://anthropic.com\")" ] }, { "cell_type": "markdown", "id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", "metadata": {}, "source": [ "## Finally - a minor improvement\n", "\n", "With a small adjustment, we can change this so that the results stream back from OpenAI,\n", "with the familiar typewriter animation" ] }, { "cell_type": "code", "execution_count": null, "id": "bcb358a4-aa7f-47ec-b2bc-67768783dfe1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "51db0e49-f261-4137-aabe-92dd601f7725", "metadata": {}, "outputs": [], "source": [ "def stream_brochure(company_name, url):\n", " stream = openai.chat.completions.create(\n", " model=MODEL,\n", " messages=[\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", " ],\n", " stream=True\n", " )\n", " \n", " response = \"\"\n", " display_handle = display(Markdown(\"\"), display_id=True)\n", " for chunk in stream:\n", " response += chunk.choices[0].delta.content or ''\n", " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", " update_display(Markdown(response), display_id=display_handle.display_id)" ] }, { "cell_type": "code", "execution_count": null, "id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", "metadata": {}, "outputs": [], "source": [ "stream_brochure(\"Anthropic\", \"https://anthropic.com\")" ] }, { "cell_type": "code", "execution_count": null, "id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", "metadata": {}, "outputs": [], "source": [ "stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" ] }, { "cell_type": "code", "execution_count": null, "id": "bcf5168e-f1d9-4fa7-b372-daf16358e93c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }