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Chat completion

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With chat completion, you can simulate a back-and-forth conversation with an AI agent. This is of course useful for creating chat bots, but it can also be used for creating autonomous agents that can complete business processes, generate code, and more. As the primary model type provided by OpenAI, Google, Mistral, Facebook, and others, chat completion is the most common AI service that you will add to your Semantic Kernel project.

When picking out a chat completion model, you will need to consider the following:

  • What modalities does the model support (e.g., text, image, audio, etc.)?
  • Does it support function calling?
  • How fast does it receive and generate tokens?
  • How much does each token cost?

Important

Of all the above questions, the most important is whether the model supports function calling. If it does not, you will not be able to use the model to call your existing code. Most of the latest models from OpenAI, Google, Mistral, and Amazon all support function calling. Support from small language models, however, is still limited.

Installing the necessary packages

Before adding chat completion to your kernel, you will need to install the necessary packages. Below are the packages you will need to install for each AI service provider.

dotnet add package Microsoft.SemanticKernel.Connectors.OpenAI

Creating chat completion services

Now that you've installed the necessary packages, you can create chat completion services. Below are the several ways you can create chat completion services using Semantic Kernel.

Adding directly to the kernel

To add a chat completion service, you can use the following code to add it to the kernel's inner service provider.

dotnet add package Microsoft.SemanticKernel.Connectors.OpenAI

using Microsoft.SemanticKernel;

IKernelBuilder kernelBuilder = Kernel.CreateBuilder();
kernelBuilder.AddAzureOpenAIChatCompletion(
    deploymentName: "NAME_OF_YOUR_DEPLOYMENT",
    apiKey: "YOUR_API_KEY",
    endpoint: "YOUR_AZURE_ENDPOINT",
    modelId: "gpt-4", // Optional name of the underlying model if the deployment name doesn't match the model name
    serviceId: "YOUR_SERVICE_ID", // Optional; for targeting specific services within Semantic Kernel
    httpClient: new HttpClient() // Optional; if not provided, the HttpClient from the kernel will be used
);
Kernel kernel = kernelBuilder.Build();

Using dependency injection

If you're using dependency injection, you'll likely want to add your AI services directly to the service provider. This is helpful if you want to create singletons of your AI services and reuse them in transient kernels.

using Microsoft.SemanticKernel;

var builder = Host.CreateApplicationBuilder(args);

builder.Services.AddAzureOpenAIChatCompletion(
    deploymentName: "NAME_OF_YOUR_DEPLOYMENT",
    apiKey: "YOUR_API_KEY",
    endpoint: "YOUR_AZURE_ENDPOINT",
    modelId: "gpt-4", // Optional name of the underlying model if the deployment name doesn't match the model name
    serviceId: "YOUR_SERVICE_ID" // Optional; for targeting specific services within Semantic Kernel
);

builder.Services.AddTransient((serviceProvider)=> {
    return new Kernel(serviceProvider);
});

Creating standalone instances

Lastly, you can create instances of the service directly so that you can either add them to a kernel later or use them directly in your code without ever injecting them into the kernel or in a service provider.

using Microsoft.SemanticKernel.Connectors.OpenAI;

AzureOpenAIChatCompletionService chatCompletionService = new (
    deploymentName: "NAME_OF_YOUR_DEPLOYMENT",
    apiKey: "YOUR_API_KEY",
    endpoint: "YOUR_AZURE_ENDPOINT",
    modelId: "gpt-4", // Optional name of the underlying model if the deployment name doesn't match the model name
    httpClient: new HttpClient() // Optional; if not provided, the HttpClient from the kernel will be used
);

To add a chat completion service, you can use the following code to add it to the kernel.

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

# Initialize the kernel
kernel = Kernel()

# Add the Azure OpenAI chat completion service
kernel.add_service(AzureChatCompletion(
    deployment_name="my-deployment",
    api_key="my-api-key",
    base_url="https://my-deployment.azurewebsites.net", # Used to point to your service
    service_id="my-service-id", # Optional; for targeting specific services within Semantic Kernel
))

You can also create instances of the service directly so that you can either add them to a kernel later or use them directly in your code without injecting them into the kernel.

from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

chat_completion_service = AzureChatCompletion(
    deployment_name="my-deployment",
    api_key="my-api-key",
    base_url="https://my-deployment.azurewebsites.net", # Used to point to your service
    service_id="my-service-id", # Optional; for targeting specific services within Semantic Kernel
)

Retrieving chat completion services

Once you've added chat completion services to your kernel, you can retrieve them using the get service method. Below is an example of how you can retrieve a chat completion service from the kernel.

var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();

from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase

chat_completion_service = kernel.get_service(type=ChatCompletionClientBase)

Using chat completion services

Now that you have a chat completion service, you can use it to generate responses from an AI agent. There are two main ways to use a chat completion service:

  • Non-streaming: You wait for the service to generate an entire response before returning it to the user.
  • Streaming: Individual chunks of the response are generated and returned to the user as they are created.

Below are the two ways you can use a chat completion service to generate responses.

Non-streaming chat completion

To use non-streaming chat completion, you can use the following code to generate a response from the AI agent.

ChatHistory history = [];
history.AddUserMessage("Hello, how are you?");

var response = await chatCompletionService.GetChatMessageContentAsync(
    history,
    kernel: kernel
);

chat_history = ChatHistory()
chat_history.add_user_message("Hello, how are you?")

response = (await chat_completion.get_chat_message_contents(
    chat_history=history,
    kernel=kernel,
))[0]

Streaming chat completion

To use streaming chat completion, you can use the following code to generate a response from the AI agent.

ChatHistory history = [];
history.AddUserMessage("Hello, how are you?");

var response = chatCompletionService.GetStreamingChatMessageContentsAsync(
    chatHistory: history,
    kernel: kernel
);

await foreach (var chunk in response)
{
    Console.Write(chunk);
}

chat_history = ChatHistory()
chat_history.add_user_message("Hello, how are you?")

response = chat_completion.get_streaming_chat_message_contents(
    chat_history=history,
    kernel=kernel,
)

async for chunk in response:
    print(chunk)

Next steps

Now that you've added chat completion services to your Semantic Kernel project, you can start creating conversations with your AI agent. To learn more about using a chat completion service, check out the following articles: