Bring Your Organizational Data to Azure AI Services with Microsoft Graph

[ravisha]

Using AI to connect your business data with the AI applications you rely on isn't just a nice-to-have—it's essential in the current landscape.

By linking data from platforms like Microsoft 365 into AI-driven apps, you can simplify the tasks, reduce the need to switch between apps, and boost productivity.

This blog will walk you through how to easily connect your business data to Azure (and extention of that could be integrating it with the OpenAI services ) using Microsoft Graph , showing you just how powerful and straightforward these tools can be.

Why Integrate Your Data?​

Imagine you're deep in a project and need to find a specific document, email, or chat from Microsoft Teams. Normally, you'd have to jump between Outlook, OneDrive, and Teams, disrupting your workflow and wasting time. This is where integrating your business data into your applications becomes incredibly useful.



By using Microsoft Graph and Azure OpenAI services, you can pull all this information directly into your app, keeping everything in one place. This not only saves time but also helps you stay focused on your work. Whether you need to find files, emails, or chat histories, integrating these tools can simplify your day and keep you on track.

Core Use Cases for Microsoft Graph Enhanced by Generative AI​

Microsoft Graph is versatile, and its applications are numerous. Here are some common use cases, now supercharged with generative AI:

Automating Microsoft 365 Workflows with Generative AI​

Use Microsoft Graph in combination with generative AI to automate tasks such as:

• 
  Email Management: Not only can you automatically sort and respond to emails, but generative AI can draft personalized responses, summarize lengthy email threads, and even predict and prioritize emails that require immediate attention.
  
  
• File Operations: Beyond managing files in OneDrive and SharePoint, generative AI can assist in creating content, generating summaries of documents, and suggesting relevant files based on the context of your work.
• User Management: Automate user provisioning and updates, while generative AI can predict user needs, suggest role changes, and provide insights into user behavior and engagement.

Integrating Microsoft Teams to Enhance Productivity with Generative AI​

Microsoft Graph enables deep integrations with Teams, and generative AI takes it a step further by allowing you to:

• Create Teams and Channels: Automate the setup of new teams for projects, and use generative AI to suggest optimal team structures, recommend channels based on project requirements, and even draft initial posts to kickstart discussions.
• Manage Conversations: Archive or monitor conversations for compliance, while generative AI can analyze conversation trends, detect sentiment, and provide insights into team dynamics and areas for improvement.
• Custom Bots: Develop bots that interact with Teams users, enhanced with generative AI to provide more natural and context-aware interactions, answer complex queries, and even assist in decision-making processes.

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By leveraging generative AI, Microsoft Graph can not only automate and streamline workflows but also provide intelligent insights and personalized experiences, significantly boosting productivity and efficiency.

Getting Started with Microsoft Graph​

Microsoft Graph is a powerful API that lets you connect to various data points in Microsoft 365. With it, you can pull in emails, chats, files, and more into your application. To begin, you’ll need to set up something called an "App Registration" in Microsoft Entra ID (formerly Azure Active Directory). This registration allows your app to access the data securely.

​

Step 1: Set Up App Registration​

1. 
   Log in to the Azure Portal and navigate to Microsoft Entra ID.
   
   
2. 
   Create a new app registration by giving it a name.
   
   
3. 
   Select the type of accounts that can access this app—whether it’s just within your organization or available to users in other organizations as well.
   
   
4. 
   Configure the Redirect URI if you're developing a web app. For local development, this might look like [URL]http://localhost:3000[/URL].
   

Here’s a basic example of how your app registration might look in code:

{
  "client_id": "YOUR_CLIENT_ID",
  "tenant_id": "YOUR_TENANT_ID",
  "redirect_uri": "http://localhost:3000"
}

Now that your app is registered, you can start pulling in data using Microsoft Graph. We’ll be using a library called Microsoft Graph Toolkit (MGT), which makes this process much simpler.

​

Step 2: Install Microsoft Graph Toolkit​

First, install the MGT package:

npm install /mgt

In your app, you’ll want to set up a provider that will handle authentication and make it easier to call Microsoft Graph APIs.

​

Step 3: Set Up Authentication​

Create a graphService.js file where you'll configure the provider:

import { Providers, MsalProvider } from '@microsoft/mgt';

export const initGraph = () => {
  if (!Providers.globalProvider) {
    Providers.globalProvider = new MsalProvider({
      clientId: 'YOUR_CLIENT_ID',
      scopes: ['User.Read', 'Files.Read', 'Mail.Read', 'Chat.Read']
    });
  }
};

This snippet sets up the authentication process using your app registration details.

Once authentication is set up, you can start pulling data like files, emails, and chats into your app. Let’s look at a couple of ways to do this.

Step 4: Fetch and Display Files​

You can fetch files related to a specific project or customer. Here’s how you might do that:

import { graph } from '@microsoft/mgt';

const getFiles = async (query) => {
  const response = await graph.api('/me/drive/search(q=\'' + query + '\')')
    .get();
  return response.value;
};

// Example usage:
getFiles('ProjectX').then(files => {
  console.log(files);
});

​

Step 5: Use MGT Components to Simplify​

Instead of writing the above code, you can use MGT’s ready-made components to fetch and display data with minimal code.

<mgt-file-list></mgt-file-list>

This single line of code will automatically pull in and display the user's files. It’s simple, powerful, and easy to implement.

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