{ "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", "# šŸ¦œļøšŸ”— LangChain\n", "\n", "āš” Building applications with LLMs through composability āš”\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='# šŸ¦œļøšŸ”— LangChain\\n\\nāš” Building applications with LLMs through composability āš”', 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 }