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Microsoft.Extensions.AI–Part III–Tool calling

I'm on a journey discovering what is possible with the Microsoft.Extensions.AI library and you are free to join. Yesterday I looked at how to integrate the library in an ASP.NET Core application. Today I want to dive into a specific feature; tool calling.

This post is part of a blog series. Other posts so far:

What is tool calling?

With tool calling you are providing your LLM with a set of tools (typically .NET methods) that it can call. This allows your LLM to interact with the outside world in a controlled way. In Semantic Kernel these tools were called ‘plugins’ but the concept is the same.

To be 100% correct it is not the LLM itself that is calling these tools but the model can request to invoke a tool with specific arguments (for example a weather tool with the location as a parameter). It is up to the client to invoke the tool and pass the result back to the LLM.

Remark: You maybe wonder, what about MCP? You can see MCP as a kind of standardized way to do tool calling. But I have planned a separate post where I specifically dive into MCP integration with Microsoft.Extensions.AI.

Integrating tool calling in Microsoft.Extensions.AI

Microsoft.Extensions.AI provides 3 building blocks to add tool calling:

  • AIFunction: The .NET method(aka Tool) that can be described to an AI model and invoked.
  • AIFunctionFactory: A factory class that helps you create AIFunction instances based on .NET methods.
  • FunctionInvokingChatClient: A wrapper for IChatClient that adds automatic function-invocation capabilities.

Let’s put these building blocks in action.

We take our application from last post and continue there:

  • Let us start by creating our .NET method that will be exposed as a tool to our LLM:
  • Now we take our original IChatClient and wrap it in a FunctionInvokingChatClient:
    • Remark: Notice that I’m using Ollama in this example as I couldn’t get Tool calling working when using AI Foundry Local.
  • The last step required is to construct a ChatOptions object and pass our tools:
    • Remark: notice that we provided extra information about the tool when creating the AIFunction.
  • Don’t forget to pass our a ChatOptions object  when calling the GetStreamingResponseAsync method:

Remark: Be aware that not every model supports tool calling. You can try different models to find one that works or use an OpenAI model.

More information

Semantic Kernel – Auto function calling

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