Loading HTML Documents with LangChain HTML Loader 🦜️

What is LangChain?

Understanding HTML Documents

Why Use LangChain HTML Loader?

• Simplicity: It abstracts lots of low-level details, letting you focus on the learner experience.
• Flexibility: Supports loading multiple types of HTML formats seamlessly.
• Integration: Works smoothly with other components in the LangChain ecosystem, like document loaders and transformers.
• Performance: Fast and efficient for large datasets.

Getting Started with LangChain HTML Loader

1
pip install langchain langchain-community

Loading HTML with UnstructuredHTMLLoader

Example Usage

1. First, import the necessary module from LangChain:
   1 2 python from langchain_community.document_loaders import UnstructuredHTMLLoader
2. Next, load the HTML file:
   1 2 3 python loader = UnstructuredHTMLLoader("example_data/fake-content.html") data = loader.load()
3. You can then view how your HTML content is structured:
   1 2 python print(data)
   Expected output would look something like this:
   1 2 python [Document(page_content='My First Heading\n\nMy first paragraph.', metadata={'source': 'example_data/fake-content.html'})]

More Complex HTML Loading with BSHTMLLoader

Steps to Load HTML Using BSHTMLLoader:

1. Make sure to install BeautifulSoup4 first:
   1 2 bash pip install beautifulsoup4
2. Import the BSHTMLLoader:
   1 2 python from langchain_community.document_loaders import BSHTMLLoader
3. Initialize and load your HTML document:
   1 2 3 python loader = BSHTMLLoader("example_data/fake-content.html") data = loader.load()
4. Display the output:
   1 2 python print(data)
   Output will show the parsed content as well as additional metadata:
   1 2 python [Document(page_content='\n\nTest Title\n\n\nMy First Heading\nMy first paragraph.\n\n\n', metadata={'source': 'example_data/fake-content.html', 'title': 'Test Title'})]

Advanced HTML Loading with SpiderLoader

Getting Started with SpiderLoader:

1. To use SpiderLoader, you’ll need an API key from Spider.
2. Install the required packages:
   1 2 bash pip install --upgrade --quiet langchain langchain-community spider-client
3. Import and utilize the
   1 SpiderLoader
   :
   1 2 3 4 5 6 7 8 python from langchain_community.document_loaders import SpiderLoader loader = SpiderLoader( api_key="YOUR_API_KEY", url="https://example.com", mode="crawl" ) data = loader.load()
4. Review your loaded data:
   1 2 python print(data)

Additional Loading Methods

• FireCrawlLoader: Great for websites with accessible subpages and generates clean Markdown with metadata.
• AzureAIDocumentIntelligenceLoader: Integrates with Azure's machine-learning service for comprehensive document structure extraction.

Example with AzureAIDocumentIntelligenceLoader

1. Set up an Azure account and create relevant resources as per the official documentation.
2. Load the necessary module:
   1 2 python from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader
3. Load your document:
   1 2 3 4 5 6 7 8 python loader = AzureAIDocumentIntelligenceLoader( api_endpoint="<endpoint>", api_key="<key>", file_path="<filepath>", api_model="prebuilt-layout" ) documents = loader.load()

Best Practices for HTML Loading

• Keep Your HTML Clean: Properly structured HTML will result in better parsing and extraction.
• Use Metadata: Always consider loading relevant metadata along with the document’s content for further processing.
• Batch Your Loads: If your application can handle it, consider loading multiple HTML documents in one go to maximize performance.

Conclusion: Why You Should Use LangChain HTML Loader

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