Skip to main content

Microsoft

All functionality related to Microsoft Azure and other Microsoft products.

Chat Models​

Azure OpenAI​

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 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 openai tiktoken

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

import os

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

See a usage example

from langchain.chat_models import AzureChatOpenAI

Text Embedding Models​

Azure OpenAI​

See a usage example

from langchain.embeddings import AzureOpenAIEmbeddings

LLMs​

Azure OpenAI​

See a usage example.

from langchain.llms import AzureOpenAI

Document loaders​

Azure Blob Storage​

Azure Blob Storage 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 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.

from langchain.document_loaders import AzureBlobStorageContainerLoader

See a usage example for the Azure Files.

from langchain.document_loaders import AzureBlobStorageFileLoader

Microsoft OneDrive​

Microsoft OneDrive (formerly SkyDrive) is a file-hosting service operated by Microsoft.

First, you need to install a python package.

pip install o365

See a usage example.

from langchain.document_loaders import OneDriveLoader

Microsoft Word​

Microsoft Word is a word processor developed by Microsoft.

See a usage example.

from langchain.document_loaders import UnstructuredWordDocumentLoader

Vector stores​

Azure Cosmos DB​

Azure Cosmos DB for 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​

See detail configuration instructions.

We need to install pymongo python package.

pip install pymongo

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 for free to get started today.

See a usage example.

from langchain.vectorstores import AzureCosmosDBVectorSearch

Retrievers​

Azure Cognitive Search (formerly known as Azure 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 (Cognitive Services)

See set up instructions.

See a usage example.

from langchain.retrievers import AzureCognitiveSearchRetriever

Utilities​

Bing Search API​

See a usage example.

from langchain.utilities import BingSearchAPIWrapper

Toolkits​

Azure Cognitive Services​

We need to install several python packages.

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

See a usage example.

from langchain.agents.agent_toolkits import O365Toolkit

Microsoft Office 365 email and calendar​

We need to install O365 python package.

pip install O365

See a usage example.

from langchain.agents.agent_toolkits import O365Toolkit

Microsoft Azure PowerBI​

We need to install azure-identity python package.

pip install azure-identity

See a usage example.

from langchain.agents.agent_toolkits import PowerBIToolkit
from langchain.utilities.powerbi import PowerBIDataset