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Exploring the Semantic Kernel Chat Completion Agent

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Warning

The Semantic Kernel Agent Framework is in preview and is subject to change.

Detailed API documentation related to this discussion is available at:

Agents are currently unavailable in Java.

Chat Completion in Semantic Kernel

Chat Completion is fundamentally a protocol for a chat-based interaction with an AI model where the chat-history maintained and presented to the model with each request. Semantic Kernel AI services offer a unified framework for integrating the chat-completion capabilities of various AI models.

A chat completion agent can leverage any of these AI services to generate responses, whether directed to a user or another agent.

For .NET, chat-completion AI Services are based on the IChatCompletionService interface.

For .NET, some of AI services that support models with chat-completion include:

Model Semantic Kernel AI Service
Azure Open AI Microsoft.SemanticKernel.Connectors.AzureOpenAI
Gemini Microsoft.SemanticKernel.Connectors.Google
HuggingFace Microsoft.SemanticKernel.Connectors.HuggingFace
Mistral Microsoft.SemanticKernel.Connectors.MistralAI
OpenAI Microsoft.SemanticKernel.Connectors.OpenAI
Onnx Microsoft.SemanticKernel.Connectors.Onnx

Agents are currently unavailable in Java.

Creating a Chat Completion Agent

A chat completion agent is fundamentally based on an AI services. As such, creating an chat completion agent starts with creating a Kernel instance that contains one or more chat-completion services and then instantiating the agent with a reference to that Kernel instance.

// Initialize a Kernel with a chat-completion service
IKernelBuilder builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(/*<...configuration parameters>*/);

Kernel kernel = builder.Build();

// Create the agent
ChatCompletionAgent agent =
    new()
    {
        Name = "SummarizationAgent",
        Instructions = "Summarize user input",
        Kernel = kernel
    };

# Define the Kernel
kernel = Kernel()

# Add the AzureChatCompletion AI Service to the Kernel
kernel.add_service(AzureChatCompletion(service_id="<service_id>"))

# Create the agent
agent = ChatCompletionAgent(
  service_id="agent", 
  kernel=kernel, 
  name="<agent name>", 
  instructions="<agent instructions>",
)

Agents are currently unavailable in Java.

AI Service Selection

No different from using Semantic Kernel AI services directly, a chat completion agent support the specification of a service-selector. A service-selector indentifies which AI service to target when the Kernel contains more than one.

Note: If multiple AI services are present and no service-selector is provided, the same default logic is applied for the agent that you'd find when using an AI services outside of the Agent Framework

IKernelBuilder builder = Kernel.CreateBuilder();

// Initialize multiple chat-completion services.
builder.AddAzureOpenAIChatCompletion(/*<...service configuration>*/, serviceId: "service-1");
builder.AddAzureOpenAIChatCompletion(/*<...service configuration>*/, serviceId: "service-2");

Kernel kernel = builder.Build();

ChatCompletionAgent agent =
    new()
    {
        Name = "<agent name>",
        Instructions = "<agent instructions>",
        Kernel = kernel,
        Arguments = // Specify the service-identifier via the KernelArguments
          new KernelArguments(
            new OpenAIPromptExecutionSettings() 
            { 
              ServiceId = "service-2" // The target service-identifier.
            });
    };

# Define the Kernel
kernel = Kernel()

# Add the AzureChatCompletion AI Service to the Kernel
kernel.add_service(AzureChatCompletion(service_id="<service_id>"))

# Create the agent
agent = ChatCompletionAgent(
  service_id="agent", 
  kernel=kernel, 
  name="<agent name>", 
  instructions="<agent instructions>",
)

Agents are currently unavailable in Java.

Conversing with Chat Completion Agent

Conversing with your Chat Completion Agent is based on a Chat History instance, no different from interacting with a Chat Completion AI service.

// Define agent
ChatCompletionAgent agent = ...;

// Create a ChatHistory object to maintain the conversation state.
ChatHistory chat = [];

// Add a user message to the conversation
chat.Add(new ChatMessageContent(AuthorRole.User, "<user input>"));

// Generate the agent response(s)
await foreach (ChatMessageContent response in agent.InvokeAsync(chat))
{
  // Process agent response(s)...
}

# Define agent
agent = ChatCompletionAgent(...)

# Define the chat history
chat = ChatHistory()

# Add the user message
chat.add_message(ChatMessageContent(role=AuthorRole.USER, content=input))

# Generate the agent response(s)
async for response in agent.invoke(chat):
  # process agent response(s)

Agents are currently unavailable in Java.

How-To:

For an end-to-end example for a Chat Completion Agent, see:

Exploring Open AI Assistant Agent