{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe6e5c82",
   "metadata": {},
   "source": [
    "# html2text\n",
    "\n",
    "[html2text](https://github.com/Alir3z4/html2text/) is a Python script that converts a page of HTML into clean, easy-to-read plain ASCII text. \n",
    "\n",
    "The ASCII also happens to be valid Markdown (a text-to-HTML format)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce77e0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install html2text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ca0974b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching pages: 100%|############| 2/2 [00:00<00:00, 10.75it/s]\n"
     ]
    }
   ],
   "source": [
    "from langchain.document_loaders import AsyncHtmlLoader\n",
    "\n",
    "urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
    "loader = AsyncHtmlLoader(urls)\n",
    "docs = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddf2be97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_transformers import Html2TextTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a95a928c",
   "metadata": {},
   "outputs": [],
   "source": [
    "urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
    "html2text = Html2TextTransformer()\n",
    "docs_transformed = html2text.transform_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18ef9fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"  * ESPNFC\\n\\n  * X Games\\n\\n  * SEC Network\\n\\n## ESPN Apps\\n\\n  * ESPN\\n\\n  * ESPN Fantasy\\n\\n## Follow ESPN\\n\\n  * Facebook\\n\\n  * Twitter\\n\\n  * Instagram\\n\\n  * Snapchat\\n\\n  * YouTube\\n\\n  * The ESPN Daily Podcast\\n\\n2023 FIFA Women's World Cup\\n\\n## Follow live: Canada takes on Nigeria in group stage of Women's World Cup\\n\\n2m\\n\\nEPA/Morgan Hancock\\n\\n## TOP HEADLINES\\n\\n  * Snyder fined $60M over findings in investigation\\n  * NFL owners approve $6.05B sale of Commanders\\n  * Jags assistant comes out as gay in NFL milestone\\n  * O's alone atop East after topping slumping Rays\\n  * ACC's Phillips: Never condoned hazing at NU\\n\\n  * Vikings WR Addison cited for driving 140 mph\\n  * 'Taking his time': Patient QB Rodgers wows Jets\\n  * Reyna got U.S. assurances after Berhalter rehire\\n  * NFL Future Power Rankings\\n\\n## USWNT AT THE WORLD CUP\\n\\n### USA VS. VIETNAM: 9 P.M. ET FRIDAY\\n\\n## How do you defend against Alex Morgan? Former opponents sound off\\n\\nThe U.S. forward is unstoppable at this level, scoring 121 goals and adding 49\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_transformed[0].page_content[1000:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6045d660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"t's brain,\\ncomplemented by several key components:\\n\\n  * **Planning**\\n    * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\n    * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n  * **Memory**\\n    * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\n    * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n  * **Tool use**\\n    * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution c\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_transformed[1].page_content[1000:2000]"
   ]
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}