langchain/docs/docs/integrations/document_loaders/microsoft_powerpoint.ipynb

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   "source": [
    "# Microsoft PowerPoint\n",
    "\n",
    ">[Microsoft PowerPoint](https://en.wikipedia.org/wiki/Microsoft_PowerPoint) is a presentation program by Microsoft.\n",
    "\n",
    "This covers how to load `Microsoft PowerPoint` documents into a document format that we can use downstream."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "721c48aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import UnstructuredPowerPointLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d3d0e35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loader = UnstructuredPowerPointLoader(\"example_data/fake-power-point.pptx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06073f91",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9adc5cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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'})]"
      ]
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     "execution_count": 4,
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    "data"
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   "metadata": {},
   "source": [
    "### Retain Elements\n",
    "\n",
    "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\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "064f9162",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = UnstructuredPowerPointLoader(\n",
    "    \"example_data/fake-power-point.pptx\", mode=\"elements\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abefbbdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a547c534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='Adding a Bullet Slide', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, lookup_index=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
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  {
   "cell_type": "markdown",
   "id": "b97180c2",
   "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": "11851fd0",
   "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": "381d4139",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "077525b8",
   "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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