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{
"cells": [
{
"cell_type": "markdown",
"id": "80f6cd99",
"metadata": {},
"source": [
"# Markdown Text Splitter\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 characters\n",
"2. How the chunk size is measured: by length function passed in (defaults to number of characters)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96d64839",
"metadata": {},
"outputs": [],
"source": [
"from langchain.text_splitter import MarkdownTextSplitter"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cfb0da17",
"metadata": {},
"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": 6,
"id": "d59a4fe8",
"metadata": {},
"outputs": [],
"source": [
"docs = markdown_splitter.create_documents([markdown_text])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cbb2e100",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='# 🦜️🔗 LangChain\\n\\n⚡ Building applications with LLMs through composability ⚡', lookup_str='', metadata={}, lookup_index=0),\n",
" Document(page_content=\"Quick Install\\n\\n```bash\\n# Hopefully this code block isn't split\\npip install langchain\", lookup_str='', metadata={}, lookup_index=0),\n",
" Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', lookup_str='', metadata={}, lookup_index=0)]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"docs"
]
}
],
"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": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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