Skip to main content

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

API Reference:

loader = Docx2txtLoader("example_data/fake.docx")
data = loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})]

Using Unstructured

from langchain_community.document_loaders import UnstructuredWordDocumentLoader
loader = UnstructuredWordDocumentLoader("example_data/fake.docx")
data = loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx'}, lookup_index=0)]

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()
Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx', 'filename': 'fake.docx', 'category': 'Title'}, lookup_index=0)

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.


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()

Help us out by providing feedback on this documentation page: