{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80f6cd99",
   "metadata": {},
   "source": [
    "# Markdown\n",
    "\n",
    ">[Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language for creating formatted text using a plain-text editor.\n",
    "\n",
    "`MarkdownTextSplitter` splits text along Markdown headings, code blocks, or horizontal rules. It's implemented as a simple subclass of `RecursiveCharacterSplitter` with Markdown-specific separators. See the source code to see the Markdown syntax expected by default.\n",
    "\n",
    "1. How the text is split: by list of `markdown` specific separators\n",
    "2. How the chunk size is measured: by number of characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96d64839",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.text_splitter import MarkdownTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfb0da17",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "markdown_text = \"\"\"\n",
    "# 🦜️🔗 LangChain\n",
    "\n",
    "⚡ Building applications with LLMs through composability ⚡\n",
    "\n",
    "## Quick Install\n",
    "\n",
    "```bash\n",
    "# Hopefully this code block isn't split\n",
    "pip install langchain\n",
    "```\n",
    "\n",
    "As an open source project in a rapidly developing field, we are extremely open to contributions.\n",
    "\"\"\"\n",
    "markdown_splitter = MarkdownTextSplitter(chunk_size=100, chunk_overlap=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d59a4fe8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = markdown_splitter.create_documents([markdown_text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbb2e100",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='# 🦜️🔗 LangChain\\n\\n⚡ Building applications with LLMs through composability ⚡', metadata={}),\n",
       " Document(page_content=\"Quick Install\\n\\n```bash\\n# Hopefully this code block isn't split\\npip install langchain\", metadata={}),\n",
       " Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', metadata={})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91b56e7e-b285-4ca4-a786-149544e0e3c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# 🦜️🔗 LangChain\\n\\n⚡ Building applications with LLMs through composability ⚡',\n",
       " \"Quick Install\\n\\n```bash\\n# Hopefully this code block isn't split\\npip install langchain\",\n",
       " 'As an open source project in a rapidly developing field, we are extremely open to contributions.']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markdown_splitter.split_text(markdown_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bee7858-9175-4d99-bd30-68f2dece8601",
   "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}