Skip to main content
Open In ColabOpen on GitHub

Microsoft Excel

UnstructuredExcelLoader 用于加载 Microsoft Excel 文件。该加载器同时支持 .xlsx 和 .xls 文件。页面内容将为 Excel 文件的原始文本。如果您在 "elements" 模式下使用此加载器，Excel 文件的 HTML 表示形式将作为文档元数据的一部分，存储在 text_as_html 键下。

请参阅本指南，了解在本地设置 Unstructured 的更多说明，包括设置所需的系统依赖项。

%pip install --upgrade --quiet langchain-community unstructured openpyxl
from langchain_community.document_loaders import 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'})]

使用 Azure AI 文档智能​

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.

当前使用Document Intelligence的加载器实现可以按页整合内容，并将其转换为 LangChain 文档。默认输出格式为 Markdown，可轻松与MarkdownHeaderTextSplitter链接以实现语义文档分块。您也可以使用mode="single"或mode="page"返回单页纯文本，或返回按页分割的文档。

前置条件​

在以下三个预览区域之一的 Azure AI 文档智能资源：美国东部、美国西部2、欧洲西部 - 如果您还没有，请按照此文档创建一个。您将向加载器传递 <endpoint> 和 <key> 作为参数。

%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()