Sunday 10 March 2024

Create a Microsoft 365 Copilot plugin: Extend Microsoft 365 Copilot's knowledge

Microsoft 365 Copilot is an enterprise AI tool that is already trained on your Microsoft 365 data. If you want to "talk" to data such as your emails, Teams chats or SharePoint documents, then all of it is already available as part of it's "knowledge".

However, not all the data you want to work with will live in Microsoft 365. There will be instances when you want to use Copilot's AI on data residing in external systems. So how do we extend the knowledge of Microsoft 365 Copilot with real time data coming from external systems? The answer is by using plugins! Plugins not only help us do Retrieval Augmented Generation (RAG) with Copilot, but they also provide a framework for writing data to external systems. 

To know more about the different Microsoft 365 Copilot extensibility options, please have a look here:

So in this post, let's have a look at how to build a plugin which talks to an external API and then infuses the real time knowledge into Copilot's AI. At the time of this writing, there is nothing more volatile than Cryptocurrency prices! So, I will be using a cryptocurrency price API and enhance Microsoft 365 Copilot's knowledge with real time Bitcoin and Ethereum rates!

(click to zoom)

So let's see the different moving parts of the plugin. We will be using a Microsoft Teams message extension built on the Bot Framework as a base for our plugin:  

1) App manifest

This is by far the most important part of the plugin. The name and description (both short and long) are what tell Copilot about the nature of the plugin and when to invoke it to get external data. We have to be very descriptive and clear about the features of the plugin here as this is what the Copilot will use to determine whether the plugin is invoked. The parameter descriptions are used to tell Copilot how to create the parameters required by the plugin based on the conversation.

2) Teams messaging extension code

This function does the heavy lifting in our code. It is called with the parameters specified in the app manifest by Copilot. Based on the parameters we can fetch external data and return it as adaptive cards. 

3) Talk to the external system (Cryptocurrency API)

This is helper function which is used to actually talk to the crypto api and return rates. 

Hope you found this post useful! 

The code for this solution is available on GitHub:

Thursday 15 February 2024

Generate images using Azure OpenAI DALL·E 3 in SPFx

Dall E 3 is the latest AI image generation model coming out of OpenAI. It is leaps and bounds ahead of the previous model Dall E 2. Having explored both, the image quality as well as the adherence to text prompts is much better for Dall E 3. It is now available as a preview in Azure OpenAI Service as well.

Given all this, it is safe to say if you are working on the Microsoft stack and want to generate images with AI, using the Azure OpenAI Dall E 3 model would be the recommended option.

In this post, let's explore the image generation API for Dall E 3 and also how to use it from a SharePoint Framework (SPFx) solution. The full code of the solution is available on GitHub:

First, let's build the web api which will wrap the Azure OpenAI API to create images. This will be a simple ASP.NET Core Web API which will accept a text prompt and return the generated image to the client.

To run this code, we will need the following NuGet package:

Now for calling the API, we will use a standard React based SPFx webpart. The webpart will use Fluent UI controls to grab the text prompt from user and send it to our API.

Hope this helps!

Thursday 25 January 2024

Get structured JSON data back from GPT-4-Turbo

With the latest gpt-4-turbo model out recently, there is one very helpful feature which came with it: The JSON mode option. Using JSON mode, we are able to predictably get responses back from OpenAI in structured JSON format. 

This can help immensely when building APIs using Large Language Models (LLMs). Even though the model can be instructed to return JSON in it's system prompt, previously, there was no guarantee that the model would return valid JSON. With the JSON mode option now, we can specify the required format and the model will return data according to it. 

To know more about JSON mode, have a look at the official OpenAI docs:

Now let's look at some code to see how this works in action:

I am using the Azure OpenAI service to host the gpt-4-turbo model and I am also using the v1.0.0.-beta.12 version of the Azure OpenAI .NET SDK found on NuGet here:

What is happening in the code is that in the system message, we are instructing the LLM that analyse the text provided by the user and then extract the cities mentioned in this text and return them in the specified JSON format. 

Also important is line 22 where we explicitly specify to use the response format as JSON.   

Next, we provide the actually text to parse in the user message. 

Once we get the data back in expected JSON schema, we are able to convert it to objects which can be used in code.

And as expected we get the following output:

Hope this helps!