## Microsoft

All functionality related to `Microsoft Azure` and other `Microsoft` products.

## LLMs[​](#llms "Direct link to LLMs")

### Azure ML[​](#azure-ml "Direct link to Azure ML")

See a [usage example](https://python.langchain.com/docs/integrations/llms/azure_ml/).

```
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
```

### Azure OpenAI[​](#azure-openai "Direct link to Azure OpenAI")

See a [usage example](https://python.langchain.com/docs/integrations/llms/azure_openai/).

```
from langchain_openai import AzureOpenAI
```

## Chat Models[​](#chat-models "Direct link to Chat Models")

### Azure OpenAI[​](#azure-openai-1 "Direct link to Azure OpenAI")

> [Microsoft Azure](https://en.wikipedia.org/wiki/Microsoft_Azure), often referred to as `Azure` is a cloud computing platform run by `Microsoft`, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). `Microsoft Azure` supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.

> [Azure OpenAI](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) is an `Azure` service with powerful language models from `OpenAI` including the `GPT-3`, `Codex` and `Embeddings model` series for content generation, summarization, semantic search, and natural language to code translation.

```
pip install langchain-openai
```

Set the environment variables to get access to the `Azure OpenAI` service.

```
import osos.environ["AZURE_OPENAI_ENDPOINT"] = "https://<your-endpoint.openai.azure.com/"os.environ["AZURE_OPENAI_API_KEY"] = "your AzureOpenAI key"
```

See a [usage example](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/)

```
from langchain_openai import AzureChatOpenAI
```

## Embedding Models[​](#embedding-models "Direct link to Embedding Models")

### Azure OpenAI[​](#azure-openai-2 "Direct link to Azure OpenAI")

See a [usage example](https://python.langchain.com/docs/integrations/text_embedding/azureopenai/)

```
from langchain_openai import AzureOpenAIEmbeddings
```

## Document loaders[​](#document-loaders "Direct link to Document loaders")

### Azure AI Data[​](#azure-ai-data "Direct link to 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.

```
pip install azureml-fsspec, azure-ai-generative
```

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/azure_ai_data/).

```
from langchain.document_loaders import AzureAIDataLoader
```

### Azure AI Document Intelligence[​](#azure-ai-document-intelligence "Direct link to 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 texts (including handwriting), tables, document structures, 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`.

First, you need to install a python package.

```
pip install azure-ai-documentintelligence
```

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence/).

```
from langchain.document_loaders import AzureAIDocumentIntelligenceLoader
```

### Azure Blob Storage[​](#azure-blob-storage "Direct link to 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.

> [Azure Files](https://learn.microsoft.com/en-us/azure/storage/files/storage-files-introduction) offers fully managed file shares in the cloud that are accessible via the industry standard Server Message Block (`SMB`) protocol, Network File System (`NFS`) protocol, and `Azure Files REST API`. `Azure Files` are based on the `Azure Blob Storage`.

`Azure Blob Storage` is designed for:

*   Serving images or documents directly to a browser.
*   Storing files for distributed access.
*   Streaming video and audio.
*   Writing to log files.
*   Storing data for backup and restore, disaster recovery, and archiving.
*   Storing data for analysis by an on-premises or Azure-hosted service.

```
pip install azure-storage-blob
```

See a [usage example for the Azure Blob Storage](https://python.langchain.com/docs/integrations/document_loaders/azure_blob_storage_container/).

```
from langchain_community.document_loaders import AzureBlobStorageContainerLoader
```

See a [usage example for the Azure Files](https://python.langchain.com/docs/integrations/document_loaders/azure_blob_storage_file/).

```
from langchain_community.document_loaders import AzureBlobStorageFileLoader
```

### Microsoft OneDrive[​](#microsoft-onedrive "Direct link to Microsoft OneDrive")

> [Microsoft OneDrive](https://en.wikipedia.org/wiki/OneDrive) (formerly `SkyDrive`) is a file-hosting service operated by Microsoft.

First, you need to install a python package.

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_onedrive/).

```
from langchain_community.document_loaders import OneDriveLoader
```

### Microsoft Word[​](#microsoft-word "Direct link to Microsoft Word")

> [Microsoft Word](https://www.microsoft.com/en-us/microsoft-365/word) is a word processor developed by Microsoft.

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_word/).

```
from langchain_community.document_loaders import UnstructuredWordDocumentLoader
```

### Microsoft Excel[​](#microsoft-excel "Direct link to Microsoft Excel")

> [Microsoft Excel](https://en.wikipedia.org/wiki/Microsoft_Excel) is a spreadsheet editor developed by Microsoft for Windows, macOS, Android, iOS and iPadOS. It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft 365 suite of software.

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.

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_excel/).

```
from langchain_community.document_loaders import UnstructuredExcelLoader
```

### Microsoft SharePoint[​](#microsoft-sharepoint "Direct link to Microsoft SharePoint")

> [Microsoft SharePoint](https://en.wikipedia.org/wiki/SharePoint) is a website-based collaboration system that uses workflow applications, “list” databases, and other web parts and security features to empower business teams to work together developed by Microsoft.

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_sharepoint/).

```
from langchain_community.document_loaders.sharepoint import SharePointLoader
```

### Microsoft PowerPoint[​](#microsoft-powerpoint "Direct link to Microsoft PowerPoint")

> [Microsoft PowerPoint](https://en.wikipedia.org/wiki/Microsoft_PowerPoint) is a presentation program by Microsoft.

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_powerpoint/).

```
from langchain_community.document_loaders import UnstructuredPowerPointLoader
```

### Microsoft OneNote[​](#microsoft-onenote "Direct link to Microsoft OneNote")

First, let's install dependencies:

See a [usage example](https://python.langchain.com/docs/integrations/document_loaders/microsoft_onenote/).

```
from langchain_community.document_loaders.onenote import OneNoteLoader
```

## Vector stores[​](#vector-stores "Direct link to Vector stores")

### Azure Cosmos DB[​](#azure-cosmos-db "Direct link to Azure Cosmos DB")

> [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) makes it easy to create a database with full native MongoDB support. You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account's connection string. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based applications with your data that's stored in Azure Cosmos DB.

#### Installation and Setup[​](#installation-and-setup "Direct link to Installation and Setup")

See [detail configuration instructions](https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db/).

We need to install `pymongo` python package.

#### Deploy Azure Cosmos DB on Microsoft Azure[​](#deploy-azure-cosmos-db-on-microsoft-azure "Direct link to Deploy Azure Cosmos DB on Microsoft Azure")

Azure Cosmos DB for MongoDB vCore provides developers with a fully managed MongoDB-compatible database service for building modern applications with a familiar architecture.

With Cosmos DB for MongoDB vCore, developers can enjoy the benefits of native Azure integrations, low total cost of ownership (TCO), and the familiar vCore architecture when migrating existing applications or building new ones.

[Sign Up](https://azure.microsoft.com/en-us/free/) for free to get started today.

See a [usage example](https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db/).

```
from langchain_community.vectorstores import AzureCosmosDBVectorSearch
```

## Retrievers[​](#retrievers "Direct link to Retrievers")

### Azure AI Search[​](#azure-ai-search "Direct link to Azure AI Search")

> [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` or `Azure Cognitive Search` ) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.

> Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
> 
> *   A search engine for full text search over a search index containing user-owned content
> *   Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
> *   Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more
> *   Programmability through REST APIs and client libraries in Azure SDKs
> *   Azure integration at the data layer, machine learning layer, and AI (AI Services)

See [set up instructions](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).

See a [usage example](https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/).

```
from langchain.retrievers import AzureAISearchRetriever
```

### Azure AI Services[​](#azure-ai-services "Direct link to Azure AI Services")

We need to install several python packages.

```
pip install azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis
```

See a [usage example](https://python.langchain.com/docs/integrations/toolkits/azure_ai_services/).

```
from langchain_community.agent_toolkits import azure_ai_services
```

### Microsoft Office 365 email and calendar[​](#microsoft-office-365-email-and-calendar "Direct link to Microsoft Office 365 email and calendar")

We need to install `O365` python package.

See a [usage example](https://python.langchain.com/docs/integrations/toolkits/office365/).

```
from langchain_community.agent_toolkits import O365Toolkit
```

### Microsoft Azure PowerBI[​](#microsoft-azure-powerbi "Direct link to Microsoft Azure PowerBI")

We need to install `azure-identity` python package.

```
pip install azure-identity
```

See a [usage example](https://python.langchain.com/docs/integrations/toolkits/powerbi/).

```
from langchain_community.agent_toolkits import PowerBIToolkitfrom langchain_community.utilities.powerbi import PowerBIDataset
```

## Utilities[​](#utilities "Direct link to Utilities")

### Bing Search API[​](#bing-search-api "Direct link to Bing Search API")

> [Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`, is a web search engine owned and operated by `Microsoft`.

See a [usage example](https://python.langchain.com/docs/integrations/tools/bing_search/).

```
from langchain_community.utilities import BingSearchAPIWrapper
```

## More[​](#more "Direct link to More")

### Microsoft Presidio[​](#microsoft-presidio "Direct link to Microsoft Presidio")

> [Presidio](https://microsoft.github.io/presidio/) (Origin from Latin praesidium ‘protection, garrison’) helps to ensure sensitive data is properly managed and governed. It provides fast identification and anonymization modules for private entities in text and images such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more.

First, you need to install several python packages and download a `SpaCy` model.

```
pip install langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Fakerpython -m spacy download en_core_web_lg
```

See [usage examples](https://python.langchain.com/docs/guides/productionization/safety/presidio_data_anonymization/).

```
from langchain_experimental.data_anonymizer import PresidioAnonymizer, PresidioReversibleAnonymizer
```