Microsoft Excel
The UnstructuredExcelLoader
is used to load Microsoft Excel
files. The loader works with both .xlsx
and .xls
files. The page content will be the raw text of the Excel file. If you use the loader in "elements"
mode, an HTML representation of the Excel file will be available in the document metadata under the text_as_html
key.
from langchain_community.document_loaders import UnstructuredExcelLoader
API Reference:
loader = UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements")
docs = loader.load()
docs[0]
Document(page_content='\n \n \n Team\n Location\n Stanley Cups\n \n \n Blues\n STL\n 1\n \n \n Flyers\n PHI\n 2\n \n \n Maple Leafs\n TOR\n 13\n \n \n', metadata={'source': 'example_data/stanley-cups.xlsx', 'filename': 'stanley-cups.xlsx', 'file_directory': 'example_data', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border="1" class="dataframe">\n <tbody>\n <tr>\n <td>Team</td>\n <td>Location</td>\n <td>Stanley Cups</td>\n </tr>\n <tr>\n <td>Blues</td>\n <td>STL</td>\n <td>1</td>\n </tr>\n <tr>\n <td>Flyers</td>\n <td>PHI</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Maple Leafs</td>\n <td>TOR</td>\n <td>13</td>\n </tr>\n </tbody>\n</table>', 'category': 'Table'})
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
JPEG/JPG
,PNG
,BMP
,TIFF
,HEIF
,DOCX
,XLSX
,PPTX
andHTML
.
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()