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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 langchain-openai

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_openai import AzureChatOpenAI
API Reference:AzureChatOpenAI

LLMs​

Azure ML​

See a usage example.

from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
API Reference:AzureMLOnlineEndpoint

Azure OpenAI​

See a usage example.

from langchain_openai import AzureOpenAI
API Reference:AzureOpenAI

Embedding Models​

Azure OpenAI​

See a usage example

from langchain_openai import AzureOpenAIEmbeddings
API Reference:AzureOpenAIEmbeddings

Document loaders​

Azure AI Data​

Azure AI Studio 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.

from langchain.document_loaders import AzureAIDataLoader
API Reference:AzureAIDataLoader

Azure AI Document Intelligence​

Azure AI Document 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.

from langchain.document_loaders import AzureAIDocumentIntelligenceLoader

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_community.document_loaders import AzureBlobStorageContainerLoader

See a usage example for the Azure Files.

from langchain_community.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_community.document_loaders import OneDriveLoader
API Reference:OneDriveLoader

Microsoft Word​

Microsoft Word is a word processor developed by Microsoft.

See a usage example.

from langchain_community.document_loaders import UnstructuredWordDocumentLoader

Microsoft Excel​

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.

from langchain_community.document_loaders import UnstructuredExcelLoader

Microsoft SharePoint​

Microsoft 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.

from langchain_community.document_loaders.sharepoint import SharePointLoader
API Reference:SharePointLoader

Microsoft PowerPoint​

Microsoft PowerPoint is a presentation program by Microsoft.

See a usage example.

from langchain_community.document_loaders import UnstructuredPowerPointLoader

Microsoft OneNote​

First, let's install dependencies:

pip install bs4 msal

See a usage example.

from langchain_community.document_loaders.onenote import OneNoteLoader
API Reference:OneNoteLoader

AI Agent Memory System​

AI agent needs robust memory systems that support multi-modality, offer strong operational performance, and enable agent memory sharing as well as separation.

AI agents can rely on Azure Cosmos DB as a unified memory system solution, enjoying speed, scale, and simplicity. This service successfully enabled OpenAI's ChatGPT service to scale dynamically with high reliability and low maintenance. Powered by an atom-record-sequence engine, it is the world's first globally distributed NoSQL, relational, and vector database service that offers a serverless mode.

Below are two available Azure Cosmos DB APIs that can provide vector store functionalities.

Azure Cosmos DB for MongoDB (vCore)​

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_community.vectorstores import AzureCosmosDBVectorSearch

Azure Cosmos DB NoSQL​

Azure Cosmos DB for NoSQL now offers vector indexing and search in preview. This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors directly in the documents alongside your data. This means that each document in your database can contain not only traditional schema-free data, but also high-dimensional vectors as other properties of the documents. This colocation of data and vectors allows for efficient indexing and searching, as the vectors are stored in the same logical unit as the data they represent. This simplifies data management, AI application architectures, and the efficiency of vector-based operations.

Installation and Setup​

See detail configuration instructions.

We need to install azure-cosmos python package.

pip install azure-cosmos

Deploy Azure Cosmos DB on Microsoft Azure​

Azure Cosmos DB offers a solution for modern apps and intelligent workloads by being very responsive with dynamic and elastic autoscale. It is available in every Azure region and can automatically replicate data closer to users. It has SLA guaranteed low-latency and high availability.

Sign Up for free to get started today.

See a usage example.

from langchain_community.vectorstores import AzureCosmosDBNoSQLVectorSearch

Retrievers​

Azure AI 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.

See a usage example.

from langchain.retrievers import AzureAISearchRetriever

Tools​

Azure Container Apps dynamic sessions​

We need to get the POOL_MANAGEMENT_ENDPOINT environment variable from the Azure Container Apps service. See the instructions here.

We need to install a python package.

pip install langchain-azure-dynamic-sessions

See a usage example.

from langchain_azure_dynamic_sessions import SessionsPythonREPLTool

Follow the documentation here to get a detail explanations and instructions of this tool.

The environment variable BING_SUBSCRIPTION_KEY and BING_SEARCH_URL are required from Bing Search resource.

from langchain_community.tools.bing_search import BingSearchResults
from langchain_community.utilities import BingSearchAPIWrapper

api_wrapper = BingSearchAPIWrapper()
tool = BingSearchResults(api_wrapper=api_wrapper)

Toolkits​

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.

from langchain_community.agent_toolkits import azure_ai_services
API Reference:azure_ai_services

Microsoft Office 365 email and calendar​

We need to install O365 python package.

pip install O365

See a usage example.

from langchain_community.agent_toolkits import O365Toolkit
API Reference:O365Toolkit

Microsoft Azure PowerBI​

We need to install azure-identity python package.

pip install azure-identity

See a usage example.

from langchain_community.agent_toolkits import PowerBIToolkit
from langchain_community.utilities.powerbi import PowerBIDataset

Graphs​

Azure Cosmos DB for Apache Gremlin​

We need to install a python package.

pip install gremlinpython

See a usage example.

from langchain_community.graphs import GremlinGraph
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship

Utilities​

Bing Search API​

Microsoft Bing, commonly referred to as Bing or Bing Search, is a web search engine owned and operated by Microsoft.

See a usage example.

from langchain_community.utilities import BingSearchAPIWrapper
API Reference:BingSearchAPIWrapper

More​

Microsoft Presidio​

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 Faker
python -m spacy download en_core_web_lg

See usage examples.

from langchain_experimental.data_anonymizer import PresidioAnonymizer, PresidioReversibleAnonymizer

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