{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🚀 Crawl4AI v0.3.72 Release Announcement\n",
    "\n",
    "Welcome to the new release of **Crawl4AI v0.3.72**! This notebook highlights the latest features and demonstrates how they work in real-time. Follow along to see each feature in action!\n",
    "\n",
    "### What’s New?\n",
    "- ✨ `Fit Markdown`: Extracts only the main content from articles and blogs\n",
    "- 🛡️ **Magic Mode**: Comprehensive anti-bot detection bypass\n",
    "- 🌐 **Multi-browser support**: Switch between Chromium, Firefox, WebKit\n",
    "- 🔍 **Knowledge Graph Extraction**: Generate structured graphs of entities & relationships from any URL\n",
    "- 🤖 **Crawl4AI GPT Assistant**: Chat directly with our AI assistant for help, code generation, and faster learning (available [here](https://tinyurl.com/your-gpt-assistant-link))\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📥 Setup\n",
    "To start, we'll install `Crawl4AI` along with Playwright and `nest_asyncio` to ensure compatibility with Colab’s asynchronous environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install Crawl4AI and dependencies\n",
    "!pip install crawl4ai\n",
    "!playwright install\n",
    "!pip install nest_asyncio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import nest_asyncio and apply it to allow asyncio in Colab\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "print('Setup complete!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## ✨ Feature 1: `Fit Markdown`\n",
    "Extracts only the main content from articles and blog pages, removing sidebars, ads, and other distractions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "from crawl4ai import AsyncWebCrawler\n",
    "\n",
    "async def fit_markdown_demo():\n",
    "    async with AsyncWebCrawler() as crawler:\n",
    "        result = await crawler.arun(url=\"https://janineintheworld.com/places-to-visit-in-central-mexico\")\n",
    "        print(result.fit_markdown)  # Shows main content in Markdown format\n",
    "\n",
    "# Run the demo\n",
    "await fit_markdown_demo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 🛡️ Feature 2: Magic Mode\n",
    "Magic Mode bypasses anti-bot detection to make crawling more reliable on protected websites.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def magic_mode_demo():\n",
    "    async with AsyncWebCrawler() as crawler:  # Enables anti-bot detection bypass\n",
    "        result = await crawler.arun(\n",
    "            url=\"https://www.reuters.com/markets/us/global-markets-view-usa-pix-2024-08-29/\",\n",
    "            magic=True  # Enables magic mode\n",
    "        )\n",
    "        print(result.markdown)  # Shows the full content in Markdown format\n",
    "\n",
    "# Run the demo\n",
    "await magic_mode_demo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 🌐 Feature 3: Multi-Browser Support\n",
    "Crawl4AI now supports Chromium, Firefox, and WebKit. Here’s how to specify Firefox for a crawl.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def multi_browser_demo():\n",
    "    async with AsyncWebCrawler(browser_type=\"firefox\") as crawler:  # Using Firefox instead of default Chromium\n",
    "        result = await crawler.arun(url=\"https://crawl4i.com\")\n",
    "        print(result.markdown)  # Shows content extracted using Firefox\n",
    "\n",
    "# Run the demo\n",
    "await multi_browser_demo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## ✨ Put them all together\n",
    "\n",
    "Let's combine all the features to extract the main content from a blog post, bypass anti-bot detection, and generate a knowledge graph from the extracted content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from crawl4ai.extraction_strategy import LLMExtractionStrategy\n",
    "from pydantic import BaseModel\n",
    "import json, os\n",
    "from typing import List\n",
    "\n",
    "# Define classes for the knowledge graph structure\n",
    "class Landmark(BaseModel):\n",
    "    name: str\n",
    "    description: str\n",
    "    activities: list[str]  # E.g., visiting, sightseeing, relaxing\n",
    "\n",
    "class City(BaseModel):\n",
    "    name: str\n",
    "    description: str\n",
    "    landmarks: list[Landmark]\n",
    "    cultural_highlights: list[str]  # E.g., food, music, traditional crafts\n",
    "\n",
    "class TravelKnowledgeGraph(BaseModel):\n",
    "    cities: list[City]  # Central Mexican cities to visit\n",
    "\n",
    "async def combined_demo():\n",
    "    # Define the knowledge graph extraction strategy\n",
    "    strategy = LLMExtractionStrategy(\n",
    "        # provider=\"ollama/nemotron\",\n",
    "        provider='openai/gpt-4o-mini', # Or any other provider, including Ollama and open source models\n",
    "        pi_token=os.getenv('OPENAI_API_KEY'), # In case of Ollama just pass \"no-token\"\n",
    "        schema=TravelKnowledgeGraph.schema(),\n",
    "        instruction=(\n",
    "            \"Extract cities, landmarks, and cultural highlights for places to visit in Central Mexico. \"\n",
    "            \"For each city, list main landmarks with descriptions and activities, as well as cultural highlights.\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Set up the AsyncWebCrawler with multi-browser support, Magic Mode, and Fit Markdown\n",
    "    async with AsyncWebCrawler(browser_type=\"firefox\") as crawler:\n",
    "        result = await crawler.arun(\n",
    "            url=\"https://janineintheworld.com/places-to-visit-in-central-mexico\",\n",
    "            extraction_strategy=strategy,\n",
    "            bypass_cache=True,\n",
    "            magic=True\n",
    "        )\n",
    "        \n",
    "        # Display main article content in Fit Markdown format\n",
    "        print(\"Extracted Main Content:\\n\", result.fit_markdown)\n",
    "        \n",
    "        # Display extracted knowledge graph of cities, landmarks, and cultural highlights\n",
    "        if result.extracted_content:\n",
    "            travel_graph = json.loads(result.extracted_content)\n",
    "            print(\"\\nExtracted Knowledge Graph:\\n\", json.dumps(travel_graph, indent=2))\n",
    "\n",
    "# Run the combined demo\n",
    "await combined_demo()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 🤖 Crawl4AI GPT Assistant\n",
    "Chat with the Crawl4AI GPT Assistant for code generation, support, and learning Crawl4AI faster. Try it out [here](https://tinyurl.com/crawl4ai-gpt)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}