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Microsoft PowerPoint

Microsoft PowerPoint is a presentation program by Microsoft.

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

# Install packages
%pip install unstructured
%pip install python-magic
%pip install python-pptx
from langchain_community.document_loaders import UnstructuredPowerPointLoader
loader = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx")
data = loader.load()
data
[Document(page_content='Adding a Bullet Slide\n\nFind the bullet slide layout\n\nUse _TextFrame.text for first bullet\n\nUse _TextFrame.add_paragraph() for subsequent bullets\n\nHere is a lot of text!\n\nHere is some text in a text box!', metadata={'source': 'example_data/fake-power-point.pptx'})]

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 = UnstructuredPowerPointLoader(
    "example_data/fake-power-point.pptx", mode="elements"
)
data = loader.load()
data[0]
Document(page_content='Adding a Bullet Slide', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, 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.

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()

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