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This guide provides a quick overview for getting started with UnstructuredMarkdown document loader. For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializableJS support
UnstructuredMarkdownLoaderlangchain_community❌❌✅

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Loader features

SourceDocument Lazy LoadingNative Async Support
UnstructuredMarkdownLoader✅❌

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Setup

To access UnstructuredMarkdownLoader document loader you’ll need to install the langchain-community integration package and the unstructured python package.

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Credentials

No credentials are needed to use this loader. To enable automated tracing of your model calls, set your LangSmith API key: 
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

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Installation

Install langchain_community and unstructured 
pip install -qU langchain_community unstructured

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Initialization

Now we can instantiate our model object and load documents. You can run the loader in one of two modes: “single” and “elements”. If you use “single” mode, the document will be returned as a single Document object. If you use “elements” mode, the unstructured library will split the document into elements such as Title and NarrativeText. You can pass in additional unstructured kwargs after mode to apply different unstructured settings. 
from langchain_community.document_loaders import UnstructuredMarkdownLoader

loader = UnstructuredMarkdownLoader(
    "./example_data/example.md",
    mode="single",
    strategy="fast",
)

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Load

docs = loader.load()
docs[0]

Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\n\nIntroduction\n\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\'s widely used for documentation, readme files, and more.\n\nFeatures\n\nHeaders\n\nMarkdown supports multiple levels of headers:\n\nHeader 1: # Header 1\n\nHeader 2: ## Header 2\n\nHeader 3: ### Header 3\n\nLists\n\nUnordered List\n\nItem 1\n\nItem 2\n\nSubitem 2.1\n\nSubitem 2.2\n\nOrdered List\n\nFirst item\n\nSecond item\n\nThird item\n\nLinks\n\nOpenAI is an AI research organization.\n\nImages\n\nHere\'s an example image:\n\nCode\n\nInline Code\n\nUse code for inline code snippets.\n\nCode Block\n\n\`\`\`python def greet(name): return f"Hello, {name}!"\n\nprint(greet("World")) \`\`\`')

print(docs[0].metadata)

{'source': './example_data/example.md'}

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Lazy Load

page = []
for doc in loader.lazy_load():
    page.append(doc)
    if len(page) >= 10:
        # do some paged operation, e.g.
        # index.upsert(page)

        page = []
page[0]

Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')

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Load Elements

In this example we will load in the elements mode, which will return a list of the different elements in the markdown document: 
from langchain_community.document_loaders import UnstructuredMarkdownLoader

loader = UnstructuredMarkdownLoader(
    "./example_data/example.md",
    mode="elements",
    strategy="fast",
)

docs = loader.load()
len(docs)

29
As you see there are 29 elements that were pulled from the example.md file. The first element is the title of the document as expected: 
docs[0].page_content

'Sample Markdown Document'

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API reference

For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html
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