How to load Markdown

Markdown is a lightweight markup language for creating formatted text using a plain-text editor.

Here we cover how to load Markdown documents into LangChain Document objects that we can use downstream.

We will cover:

• Basic usage;
• Parsing of Markdown into elements such as titles, list items, and text.

LangChain implements an UnstructuredMarkdownLoader object which requires the Unstructured package. First we install it:

%pip install "unstructured[md]" nltk

Basic usage will ingest a Markdown file to a single document. Here we demonstrate on LangChain's readme:

from langchain_community.document_loaders import UnstructuredMarkdownLoader
from langchain_core.documents import Document

markdown_path = "../../../README.md"
loader = UnstructuredMarkdownLoader(markdown_path)

data = loader.load()
assert len(data) == 1
assert isinstance(data[0], Document)
readme_content = data[0].page_content
print(readme_content[:250])

API Reference:UnstructuredMarkdownLoader | Document

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Retain Elements

Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".

loader = UnstructuredMarkdownLoader(markdown_path, mode="elements")

data = loader.load()
print(f"Number of documents: {len(data)}\n")

for document in data[:2]:
    print(f"{document}\n")

Number of documents: 66

page_content='🦜️🔗 LangChain' metadata={'source': '../../../README.md', 'category_depth': 0, 'last_modified': '2024-06-28T15:20:01', 'languages': ['eng'], 'filetype': 'text/markdown', 'file_directory': '../../..', 'filename': 'README.md', 'category': 'Title'}

page_content='⚡ Build context-aware reasoning applications ⚡' metadata={'source': '../../../README.md', 'last_modified': '2024-06-28T15:20:01', 'languages': ['eng'], 'parent_id': '200b8a7d0dd03f66e4f13456566d2b3a', 'filetype': 'text/markdown', 'file_directory': '../../..', 'filename': 'README.md', 'category': 'NarrativeText'}

Note that in this case we recover three distinct element types:

print(set(document.metadata["category"] for document in data))

{'ListItem', 'NarrativeText', 'Title'}