Microsoft Excel
TheUnstructuredExcelLoader is used to loadMicrosoft 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 thetext_as_html key.
Please seethis guide for more instructions on setting up Unstructured locally, including setting up required system dependencies.
%pip install--upgrade--quiet langchain-community unstructured openpyxl
from langchain_community.document_loadersimport UnstructuredExcelLoader
loader= UnstructuredExcelLoader("./example_data/stanley-cups.xlsx", mode="elements")
docs= loader.load()
print(len(docs))
docs
4
[Document(page_content='Stanley Cups', metadata={'source': './example_data/stanley-cups.xlsx', 'file_directory': './example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups', 'page_number': 1, 'languages': ['eng'], 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Title'}),
Document(page_content='\n\n\nTeam\nLocation\nStanley Cups\n\n\nBlues\nSTL\n1\n\n\nFlyers\nPHI\n2\n\n\nMaple Leafs\nTOR\n13\n\n\n', metadata={'source': './example_data/stanley-cups.xlsx', 'file_directory': './example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups', 'page_number': 1, '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>', 'languages': ['eng'], 'parent_id': '17e9a90f9616f2abed8cf32b5bd3810d', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Table'}),
Document(page_content='Stanley Cups Since 67', metadata={'source': './example_data/stanley-cups.xlsx', 'file_directory': './example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups Since 67', 'page_number': 2, 'languages': ['eng'], 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Title'}),
Document(page_content='\n\n\nTeam\nLocation\nStanley Cups\n\n\nBlues\nSTL\n1\n\n\nFlyers\nPHI\n2\n\n\nMaple Leafs\nTOR\n0\n\n\n', metadata={'source': './example_data/stanley-cups.xlsx', 'file_directory': './example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups Since 67', 'page_number': 2, '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>0</td>\n </tr>\n </tbody>\n</table>', 'languages': ['eng'], 'parent_id': 'ee34bd8c186b57e3530d5443ffa58122', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Table'})]
Using Azure AI Document Intelligence
Azure AI Document Intelligence (formerly known as
Azure Form Recognizer) is machine-learningbased service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs fromdigital or scanned PDFs, images, Office and HTML files.Document Intelligence supports
JPEG/JPG,PNG,BMP,TIFF,HEIF,DOCX,XLSX,PPTXandHTML.
This current implementation of a loader usingDocument Intelligence can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained withMarkdownHeaderTextSplitter for semantic document chunking. You can also usemode="single" ormode="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 - followthis 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_loadersimport 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()
Related
- Document loaderconceptual guide
- Document loaderhow-to guides