docs Microsoft platform page update (#15420)

Added two new document_loader references. Improved the format
consistency of the example pages

docs/docs/integrations/document_loaders/azure_ai_data.ipynb

@@ -8,10 +8,10 @@
     "# Azure AI Data\n",
     "\n",
     ">[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets to cloud storage and register existing data assets from the following sources:\n",
-    "\n",
-    "- Microsoft OneLake\n",
-    "- Azure Blob Storage\n",
-    "- Azure Data Lake gen 2\n",
+    ">\n",
+    ">- `Microsoft OneLake`\n",
+    ">- `Azure Blob Storage`\n",
+    ">- `Azure Data Lake gen 2`\n",
     "\n",
     "The benefit of this approach over `AzureBlobStorageContainerLoader` and `AzureBlobStorageFileLoader` is that authentication is handled seamlessly to cloud storage. You can use either *identity-based* data access control to the data or *credential-based* (e.g. SAS token, account key). In the case of credential-based data access you do not need to specify secrets in your code or set up key vaults - the system handles that for you.\n",
     "\n",
@@ -166,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/azure_document_intelligence.ipynb

@@ -13,22 +13,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Azure AI Document Intelligence (formerly known as Azure Form Recognizer) is machine-learning \n",
-    "based service that extracts text (including handwriting), tables or key-value-pairs from\n",
-    "scanned documents or images.\n",
+    ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
+    ">based service that extracts text (including handwriting), tables or key-value-pairs from\n",
+    ">scanned documents or images.\n",
+    ">\n",
+    ">Document Intelligence supports `PDF`, `JPEG`, `PNG`, `BMP`, or `TIFF`.\n",
     "\n",
-    "This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.\n",
-    "\n",
-    "Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.\n",
-    "\n",
-    "Further documentation is available at https://aka.ms/doc-intelligence.\n"
+    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install langchain langchain-community azure-ai-documentintelligence -q"
    ]
@@ -126,7 +135,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -140,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/platforms/microsoft.mdx

@@ -49,6 +49,50 @@ from langchain_community.llms import AzureOpenAI
 
 ## Document loaders
 
+### Azure AI Data
+
+>[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets 
+> to cloud storage and register existing data assets from the following sources:
+>
+>- `Microsoft OneLake`
+>- `Azure Blob Storage`
+>- `Azure Data Lake gen 2`
+
+First, you need to install several python packages.
+
+```bash
+pip install azureml-fsspec, azure-ai-generative
+```
+
+See a [usage example](/docs/integrations/document_loaders/azure_ai_data).
+
+```python
+from langchain.document_loaders import AzureAIDataLoader
+```
+
+
+### Azure AI Document Intelligence
+
+>[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known
+> as `Azure Form Recognizer`) is machine-learning
+> based service that extracts text (including handwriting), tables or key-value-pairs
+> from scanned documents or images.
+>
+>Document Intelligence supports `PDF`, `JPEG`, `PNG`, `BMP`, or `TIFF`.
+
+First, you need to install a python package.
+
+```bash
+pip install azure-ai-documentintelligence
+```
+
+See a [usage example](/docs/integrations/document_loaders/azure_document_intelligence).
+
+```python
+from langchain.document_loaders import AzureAIDocumentIntelligenceLoader
+```
+
+
 ### Azure Blob Storage
 
 >[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.