langchain/docs/versioned_docs/version-0.2.x/integrations/document_loaders/microsoft_excel.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Microsoft Excel\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6616e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import UnstructuredExcelLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a654e4d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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'})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader = UnstructuredExcelLoader(\"example_data/stanley-cups.xlsx\", mode=\"elements\")\n",
    "docs = loader.load()\n",
    "docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729ab1a2",
   "metadata": {},
   "source": [
    "## Using Azure AI Document Intelligence\n",
    "\n",
    ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
    ">based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from\n",
    ">digital or scanned PDFs, images, Office and HTML files.\n",
    ">\n",
    ">Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`.\n",
    "\n",
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe5c77d",
   "metadata": {},
   "source": [
    "### Prerequisite\n",
    "\n",
    "An Azure AI Document Intelligence resource in one of the 3 preview regions: **East US**, **West US2**, **West Europe** - follow [this document](https://learn.microsoft.com/azure/ai-services/document-intelligence/create-document-intelligence-resource?view=doc-intel-4.0.0) to create one if you don't have. You will be passing `<endpoint>` and `<key>` as parameters to the loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fda529f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa008547",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
    "\n",
    "file_path = \"<filepath>\"\n",
    "endpoint = \"<endpoint>\"\n",
    "key = \"<key>\"\n",
    "loader = AzureAIDocumentIntelligenceLoader(\n",
    "    api_endpoint=endpoint, api_key=key, file_path=file_path, api_model=\"prebuilt-layout\"\n",
    ")\n",
    "\n",
    "documents = loader.load()"
   ]
  }
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