Azure AI Document Intelligence (formerly known as Azure Form Recognizer) is machine-learning based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from digital or scanned PDFs, images, Office and HTML files. Document Intelligence supports PDF, JPEG/JPG, PNG, BMP, TIFF, HEIF, DOCX, XLSX, PPTX and HTML.
This current implementation of a loader using Document Intelligence can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained with MarkdownHeaderTextSplitter for semantic document chunking. You can also use mode="single" or mode="page" to return pure texts in a single page or document split by page.

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Prerequisite

An Azure AI Document Intelligence resource in one of the 3 preview regions: East US, West US2, West Europe - follow this document to create one if you don’t have. You will be passing <endpoint> and <key> as parameters to the loader. 
%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence

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Example 1

The first example uses a local file which will be sent to Azure AI Document Intelligence. With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader: 
from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader

file_path = "<filepath>"
endpoint = "<endpoint>"
key = "<key>"
loader = AzureAIDocumentIntelligenceLoader(
    api_endpoint=endpoint, api_key=key, file_path=file_path, api_model="prebuilt-layout"
)

documents = loader.load()
The default output contains one LangChain document with markdown format content: 
documents

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Example 2

The input file can also be a public URL path. E.g., https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png. 
url_path = "<url>"
loader = AzureAIDocumentIntelligenceLoader(
    api_endpoint=endpoint, api_key=key, url_path=url_path, api_model="prebuilt-layout"
)

documents = loader.load()

documents

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Example 3

You can also specify mode="page" to load document by pages. 
from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader

file_path = "<filepath>"
endpoint = "<endpoint>"
key = "<key>"
loader = AzureAIDocumentIntelligenceLoader(
    api_endpoint=endpoint,
    api_key=key,
    file_path=file_path,
    api_model="prebuilt-layout",
    mode="page",
)

documents = loader.load()
The output will be each page stored as a separate document in the list: 
for document in documents:
    print(f"Page Content: {document.page_content}")
    print(f"Metadata: {document.metadata}")

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Example 4

You can also specify analysis_feature=["ocrHighResolution"] to enable add-on capabilities. For more information, see: https://aka.ms/azsdk/python/documentintelligence/analysisfeature. 
from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader

file_path = "<filepath>"
endpoint = "<endpoint>"
key = "<key>"
analysis_features = ["ocrHighResolution"]
loader = AzureAIDocumentIntelligenceLoader(
    api_endpoint=endpoint,
    api_key=key,
    file_path=file_path,
    api_model="prebuilt-layout",
    analysis_features=analysis_features,
)

documents = loader.load()
The output contains the LangChain document recognized with high resolution add-on capability: 
documents