Tag: Dataverse

Adding intelligence to Dataverse using Dataverse AI functions

Hi Folks,

While intelligence with the use of AI is being embedded into each and every part of the Microsoft ecosystem, it is good to know the features coming in the Power Platform space.

In this blog post, let’s see how we can use Dataverse AI Functions, their usage, advantages which can greatly ease summarizing, classifying, extracting, translating, assessing sentiment, or drafting a reply for common business scenarios.

To illustrate it better, I used a different AI Function for Canvas App, Model Driven App and Power Automate, hope you can follow the same for others as well.

What are Dataverse AI Functions?

Think of Dataverse AI Functions as prebuilt AI Functions which will add intelligence in your Apps and Flows without need to collect, build and train the models. They can be used in many places such as AI Builder, Power Automate, Power Apps, Low Code Plugins. Following are the AI Functions available…

  1. AIReply – Drafts a reply to the message you provide.
  2. AISentiment – Detects sentiment for the text you provide
  3. AISummarize – Summarizes the text you provide
  4. AIClassify – Classifies the text into one or more categories, you can use this from custom copilot
  5. AIExtract – Extracts specified entities like Names of people, phone numbers, places etc.
  6. AITranslate – Translate text from another language

Let’s see their usage in our favorite Canvas Apps first as illustration is easy and later in the post, I will mention how you can call the Dataverse AI Functions from Model Driven Apps, Power Automate so that you can get the real essence of it….

Utilizing ‘Dataverse AI Functions’ in a Canvas App

Create a new Canvas App and add ‘Environment’ Datasource as shown below.

All the ‘Dataverse AI functions’ can be accessed by ‘Environment‘ as shown below.

Let’s try the AIReply function in Canvas Apps

Add a textbox for storing the prompt or input string and a button control.

On the ‘OnSelect‘ event of the button, use the following formula to store the response in the AIResponse context variable, make sure you name the variables appropriately in your formula as per your naming defined in canvas apps.

UpdateContext({AIReplyResponse:Environment.AIReply({Text:AIInput.Text})})
view raw Dataverse AI Function hosted with ❤ by GitHub

Now create one more text variable to store the response and change the Default value to AIReplyResponse.PreparedResponse.

Try testing the app by providing inputs as below…

You should get a response from AIReply in the response field, you can try out other functions providing the necessary parameters required.

Utilizing ‘Dataverse AI Functions’ in a Power Automate

In Power Automate, all you can do to call Dataverse AI Functions is call the Unbound Action as below.

Passing the relevant input parameters is enough to get the output from these functions.

Let’s try AISentiment

Click on test, you should a response from Power Automate with the sentiment

Utilizing ‘Dataverse AI Functions’ in a Model Driven Apps

Do you want to utilize the similar capabilities of Dataverse AI Functions inside your custom code like in Plugins, Actions etc..

Let’s try AIClassify

var request = new OrganizationRequest("AIClassify")
{
["AllowMultipleCategories"] = false,
["Categories"] = titles,
["Text"] = classifyText
};
var result = service.Execute(request);
view raw DataverseFunctions.cs hosted with ❤ by GitHub

It was pretty much similar in AIBuilder as well…

Please do note that there are quotas to use these AI Functions at tenant level, else you might get similar error like below, while I didn’t get any information regarding this from Microsoft, so I am unsure about this as of writing this post, I will keep this updated if I get to know.

Using Dataverse AI functions needs a bit of Prompt Engineering knowledge, you were looking to learn more about Prompt engineering, then check it out here.

References:

https://learn.microsoft.com/en-us/power-platform/power-fx/reference/function-ai

Cheers,

PMDY

Dataverse – Git Integration – Preview – Quick Review

Hi Folks,

This post is about Dataverse and Git Integration which is the most sought after feature in the todays automation Era. This is a preview feature, you would need to create a new environment with Early Access enabled to test this feature or you can use an existing US Preview environment for testing this out.

While every MDA(Model Driven Application) and it’s components can be safely and moved across the environments using Solutions with the help of Azure DevOps Pipelines. However when coming to integrating Power Platform Solutions to Azure DevOps, we had to manually export the solution and download them each and every time when we would like to commit the Solution Artifacts to Azure DevOps Repo.

With this new Preview feature we can directly integrate the Power Platform Solutions to Azure DevOps.

Let’s see this action…wait a moment, there were some prerequisites to be considered…

  1. Environment should be a Managed Environment to start using this and you need to be an Admin for the environment
  2. Azure DevOps subscription and license should be available to set this up, also permission to read source files and commits(should be a member of contributor group in Azure DevOps) from a Repo
  3. Your email address used for Azure DevOps and Power Platform Solutions should be the same

Setup:

Connecting Dataverse with Azure DevOps is easy but requires a bit of understanding of the Binding options available.

Well, there were two types of Binding options

  1. Environment Binding – Single root folder binds to all the unmanaged solutions in the environment
  2. Solution Binding – Different solutions uses a different root folder in Azure DevOps for binding

Note: Once the binding is setup, there isn’t away to change, so set this up carefully, else you may need to delete the folder and create a new one in Azure DevOps.

Let’s see one by one…for demoing purpose, I have created two projects in Azure DevOps Instance

  1. Solution Binding: When we use this, all the components will be available as pending changes
  2. Environment Binding: When we use this, all the unmanaged solution components will be mapped to one Azure DevOps root folder. Let’s set this up.

We are currently able to use only Solution binding, as Environment Binding doesn’t show up any changes to be committed, but there is a catch here.

We can set up for Environment binding and verify if the Solution components are getting marked as pending changes or not. Do note that Setting up the Binding is a one time activity for environment, once setup, it can’t be changed from one type to another.

Open https://make.powerapps.com and navigate to solutions and click on ellipses as below

Once clicked on Connect to Git

Since we were currently using Environment binding, let’s select the Connection Type as Environment

Then click on Connect, once connected, you should a alert message in power apps maker portal at the top.

Now create a new solution as below named ecellors Solution

Verify the integration by clicking on Git Integration as below

It should show as below

Now let’s add few components to the solution we created

Once added, let’s publish the unmanaged solution and verify it..

Do look closely, you should see a Source Control icon highlighted in yellow color for illustration.

Also, you should see a commit option available at the top

You should now be able to commit the solution components as if you are committing the code changes.

It also specifies the branch to which we were commiting…

While it takes few minutes unlike pushing the code to Azure DevOps to push the changes, however this would depend based on the number of solution components you were pushing..once it is done, it will show a commit message like below…

Now let’s verify our Azure DevOps Repo..for this you can go back to the main solutions page, click on Git Connection at the top..

After clicking on Git Connection, click on the link to Microsoft Azure DevOps as below

Then you should be navigated to Azure DevOps folder as below where all the solution files will be tracked component wise.

Now we will move back to Power Apps maker portal and make some changes to any of the components inside the solution…

Let’s say, I just edited the flow name and created a new connection reference, saved and published the customizations.

If you did some changes at the Azure DevOps repo level, you can come back and click on Check for updates, if there were any conflicts between changes done in Azure DevOps and component in solution, it will be shown as conflict.

We now have 3 component changes and all were listed here…you can click on Commit.

As soon as the changes are committed, you should see a message saying Commit Successful and 0 Changes, 0 Updates, 0 Conflicts.

Now you successfully integrated Dataverse Solution components with Azure DevOps without any manual intervention required while deploying solutions using Azure DevOps Pipelines.

Hope you learned something new today…while feature is still in Preview and only available for early release, while couple of issues still need to fixed by Microsoft.

I have tested this feature by creating an environment in US Preview region and this feature will be a good value to projects using Automation and this solution repository can be further deployed to other environments using Azure DevOps Pipelines.

This will be rolled out soon next year, hope you learned something new today…

Cheers,

PMDY