Microsoft Word

Microsoft Word is a word processor developed by Microsoft.

This covers how to load Word documents into a document format that we can use downstream.

Using Docx2txt

Load .docx using Docx2txt into a document.

%pip install --upgrade --quiet  docx2txt

from langchain_community.document_loaders import Docx2txtLoader

loader = Docx2txtLoader("./example_data/fake.docx")

data = loader.load()

data

API Reference:Docx2txtLoader

[Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': './example_data/fake.docx'})]

Using Unstructured

Please see this guide for more instructions on setting up Unstructured locally, including setting up required system dependencies.

from langchain_community.document_loaders import UnstructuredWordDocumentLoader

loader = UnstructuredWordDocumentLoader("example_data/fake.docx")

data = loader.load()

data

API Reference:UnstructuredWordDocumentLoader

[Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})]

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 = UnstructuredWordDocumentLoader("./example_data/fake.docx", mode="elements")

data = loader.load()

data[0]

Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': './example_data/fake.docx', 'category_depth': 0, 'file_directory': './example_data', 'filename': 'fake.docx', 'last_modified': '2023-12-19T13:42:18', 'languages': ['por', 'cat'], 'filetype': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'category': 'Title'})

Using Azure AI Document Intelligence

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.

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

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()

API Reference:AzureAIDocumentIntelligenceLoader

Related

• Document loader conceptual guide
• Document loader how-to guides