371 lines
14 KiB
Plaintext
371 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "dc8af57c-23a9-452e-9fc3-0e5027edda14",
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"metadata": {},
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"source": [
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"# AI-powered Brochure Generator\n",
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"---\n",
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"- 🌍 Task: Generate a company brochure using its name and website for clients, investors, and recruits.\n",
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"- 🧠 Model: Toggle `USE_OPENAI` to switch between OpenAI and Ollama models\n",
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"- 🕵️♂️ Data Extraction: Scraping website content and filtering key links (About, Products, Careers, Contact).\n",
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"- 📌 Output Format: a Markdown-formatted brochure streamed in real-time.\n",
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"- 🚀 Tools: BeautifulSoup, OpenAI API, and IPython display, ollama.\n",
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"- 🧑💻 Skill Level: Intermediate.\n",
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"\n",
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"🛠️ Requirements\n",
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"- ⚙️ Hardware: ✅ CPU is sufficient — no GPU required\n",
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"- 🔑 OpenAI API Key \n",
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"- Install Ollama and pull llama3.2:3b or another lightweight model\n",
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"---\n",
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"📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec869f2c",
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"metadata": {},
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"source": [
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"## 🧩 System Design Overview\n",
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"\n",
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"### Class Structure\n",
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"\n",
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"\n",
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"\n",
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"This code consists of three main classes:\n",
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"\n",
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"1. **`Website`**: \n",
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" - Scrapes and processes webpage content. \n",
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" - Extracts **text** and **links** from a given URL. \n",
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"\n",
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"2. **`LLMClient`**: \n",
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" - Handles interactions with **OpenAI or Ollama (`llama3`, `deepseek`, `qwen`)**. \n",
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" - Uses `get_relevant_links()` to filter webpage links. \n",
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" - Uses `generate_brochure()` to create and stream a Markdown-formatted brochure. \n",
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"\n",
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"3. **`BrochureGenerator`**: \n",
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" - Uses `Website` to scrape the main webpage and relevant links. \n",
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" - Uses `LLMClient` to filter relevant links and generate a brochure. \n",
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" - Calls `generate()` to run the entire process.\n",
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"\n",
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"### Workflow\n",
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"\n",
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"1. **`main()`** initializes `BrochureGenerator` and calls `generate()`. \n",
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"2. **`generate()`** calls **`LLMClient.get_relevant_links()`** to extract relevant links using **LLM (OpenAI/Ollama)**. \n",
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"3. **`Website` scrapes the webpage**, extracting **text and links** from the given URL. \n",
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"4. **Relevant links are re-scraped** using `Website` to collect additional content. \n",
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"5. **All collected content is passed to `LLMClient.generate_brochure()`**. \n",
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"6. **`LLMClient` streams the generated brochure** using **OpenAI or Ollama**. \n",
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"7. **The final brochure is displayed in Markdown format.**\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"### Intermediate reasoning\n",
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"\n",
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"In this workflow, we have intermediate reasoning because the LLM is called twice:\n",
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"\n",
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"1. **First LLM call**: Takes raw links → filters/selects relevant ones (reasoning step).\n",
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"2. **Second LLM call**: Takes selected content → generates final brochure.\n",
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"\n",
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"🧠 **LLM output becomes LLM input** — that’s intermediate reasoning.\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": "markdown",
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"id": "4b286461-35ee-4bc5-b07d-af554923e36d",
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"metadata": {},
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"source": [
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"## 📦 Import Libraries"
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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": "3fe5670c-5146-474b-9e75-484210533f55",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"import ollama\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 display, Markdown, 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": "markdown",
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"id": "f3e23181-1e66-410d-a910-1fb4230f8088",
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"metadata": {},
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"source": [
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"## 🧠 Define the Model\n",
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"\n",
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"The user can switch between OpenAI and Ollama by changing a single variable (`USE_OPENAI`). The model selection is dynamic."
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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": "fa2bd452-0cf4-4fec-9542-e1c86584c23f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load API key\n",
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"load_dotenv()\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"if not api_key or not api_key.startswith('sk-'):\n",
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" raise ValueError(\"Invalid OpenAI API key. Check your .env file.\")\n",
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"\n",
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"# Define the model dynamically\n",
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"USE_OPENAI = True # True to use openai and False to use Ollama\n",
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"MODEL = 'gpt-4o-mini' if USE_OPENAI else 'llama3.2:3b'\n",
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"\n",
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"openai_client = OpenAI() if USE_OPENAI else None"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4fd997b7-1b89-4817-b53a-078164f5f71f",
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"metadata": {},
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"source": [
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"## 🏗️ Define Classes"
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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": "aed1af59-8b8f-4add-98dc-a9f1b5b511a5",
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"metadata": {},
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"outputs": [],
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"source": [
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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 scrape and process website content.\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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" 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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" self.text = self.extract_text(soup)\n",
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" self.links = self.extract_links(soup)\n",
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"\n",
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" def extract_text(self, soup):\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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" return soup.body.get_text(separator=\"\\n\", strip=True)\n",
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" return \"\"\n",
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"\n",
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" def extract_links(self, soup):\n",
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" links = [link.get('href') for link in soup.find_all('a')]\n",
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" return [link for link in links if link and 'http' in 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": "ea04dc7e-ff4c-4113-83b7-0bddcf5072b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"class LLMClient:\n",
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" def __init__(self, model=MODEL):\n",
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" self.model = model\n",
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"\n",
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" def get_relevant_links(self, website):\n",
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" link_system_prompt = \"\"\"\n",
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" You are given a list of links from a company website.\n",
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" Select only relevant links for a brochure (About, Company, Careers, Products, Contact).\n",
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" Exclude login, terms, privacy, and emails.\n",
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"\n",
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" ### **Instructions**\n",
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" - Return **only valid JSON**.\n",
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" - **Do not** include explanations, comments, or Markdown.\n",
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" - Example output:\n",
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" {\n",
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" \"links\": [\n",
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" {\"type\": \"about\", \"url\": \"https://company.com/about\"},\n",
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" {\"type\": \"contact\", \"url\": \"https://company.com/contact\"},\n",
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" {\"type\": \"product\", \"url\": \"https://company.com/products\"}\n",
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" ]\n",
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" }\n",
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" \"\"\"\n",
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"\n",
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" user_prompt = f\"\"\"\n",
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" Here is the list of links on the website of {website.url}:\n",
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" Please identify the relevant web links for a company brochure. Respond in JSON format.\n",
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" Do not include login, terms of service, privacy, or email links.\n",
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" Links (some might be relative links):\n",
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" {', '.join(website.links)}\n",
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" \"\"\"\n",
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"\n",
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" if USE_OPENAI:\n",
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" response = openai_client.chat.completions.create(\n",
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" model=self.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\": user_prompt}\n",
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" ]\n",
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" )\n",
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" return json.loads(response.choices[0].message.content.strip())\n",
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" else:\n",
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" response = ollama.chat(\n",
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" model=self.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\": user_prompt}\n",
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" ]\n",
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" )\n",
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" result = response.get(\"message\", {}).get(\"content\", \"\").strip()\n",
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" try:\n",
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" return json.loads(result) # Attempt to parse JSON\n",
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" except json.JSONDecodeError:\n",
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" print(\"Error: Response is not valid JSON\")\n",
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" return {\"links\": []} # Return empty list if parsing fails\n",
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"\n",
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"\n",
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" def generate_brochure(self, company_name, content, language):\n",
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" system_prompt = \"\"\"\n",
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" You are a professional translator and writer who creates fun and engaging brochures.\n",
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" Your task is to read content from a company’s website and write a short, humorous, joky,\n",
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" and entertaining brochure for potential customers, investors, and job seekers.\n",
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" Include details about the company’s culture, customers, and career opportunities if available.\n",
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" Respond in Markdown format.\n",
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" \"\"\"\n",
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"\n",
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" user_prompt = f\"\"\"\n",
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" Create a fun brochure for '{company_name}' using the following content:\n",
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" {content[:5000]}\n",
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" Respond in {language} only, and format your response correctly in Markdown.\n",
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" Do NOT escape characters or return extra backslashes.\n",
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" \"\"\"\n",
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"\n",
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" if USE_OPENAI:\n",
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" response_stream = openai_client.chat.completions.create(\n",
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" model=self.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\": user_prompt}\n",
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" ],\n",
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" stream=True\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 response_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)\n",
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" else:\n",
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" response_stream = ollama.chat(\n",
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" model=self.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\": user_prompt}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" full_text = \"\"\n",
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" for chunk in response_stream:\n",
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" if \"message\" in chunk:\n",
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" content = chunk[\"message\"][\"content\"] or \"\"\n",
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" full_text += content\n",
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" update_display(Markdown(full_text), display_id=display_handle.display_id)\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": "1c69651f-e004-421e-acc5-c439e57a8762",
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"metadata": {},
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"outputs": [],
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"source": [
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"class BrochureGenerator:\n",
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" \"\"\"\n",
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" Main class to generate a company brochure.\n",
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" \"\"\"\n",
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" def __init__(self, company_name, url, language='English'):\n",
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" self.company_name = company_name\n",
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" self.url = url\n",
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" self.language = language\n",
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" self.website = Website(url)\n",
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" self.llm_client = LLMClient()\n",
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"\n",
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" def generate(self):\n",
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" links = self.llm_client.get_relevant_links(self.website)\n",
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" content = self.website.get_contents()\n",
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"\n",
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" for link in links['links']:\n",
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" linked_website = Website(link['url'])\n",
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" content += f\"\\n\\n{link['type']}:\\n\"\n",
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" content += linked_website.get_contents()\n",
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"\n",
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" self.llm_client.generate_brochure(self.company_name, content, self.language)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1379d39d",
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"metadata": {},
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"source": [
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"## 📝 Generate Brochure"
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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": "1a63519a-1981-477b-9de1-f1ff9be94201",
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"metadata": {},
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"outputs": [],
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"source": [
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"def main():\n",
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" company_name = \"Tour Eiffel\"\n",
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" url = \"https://www.toureiffel.paris/fr\"\n",
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" language = \"French\"\n",
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"\n",
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" generator = BrochureGenerator(company_name, url, language)\n",
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" generator.generate()\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" main()"
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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": ".venv",
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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": "3.12.9"
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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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