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This migration guide shows how to migrate from FunctionCallingStepwisePlanner to a new recommended approach for planning capability - Auto Function Calling. The new approach produces the results more reliably and uses fewer tokens compared to FunctionCallingStepwisePlanner.
Plan generation
Following code shows how to generate a new plan with Auto Function Calling by using FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(). After sending a request to AI model, the plan will be located in ChatHistory object where a message with Assistant role will contain a list of functions (steps) to call.
Old approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
FunctionCallingStepwisePlanner planner = new();
FunctionCallingStepwisePlannerResult result = await planner.ExecuteAsync(kernel, "Check current UTC time and return current weather in Boston city.");
ChatHistory generatedPlan = result.ChatHistory;
New approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
IChatCompletionService chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
ChatHistory chatHistory = [];
chatHistory.AddUserMessage("Check current UTC time and return current weather in Boston city.");
OpenAIPromptExecutionSettings executionSettings = new() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() };
await chatCompletionService.GetChatMessageContentAsync(chatHistory, executionSettings, kernel);
ChatHistory generatedPlan = chatHistory;
Execution of the new plan
Following code shows how to execute a new plan with Auto Function Calling by using FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(). This approach is useful when only result is needed without plan steps. In this case, Kernel object can be used to pass a goal to InvokePromptAsync method. The result of plan execution will be located in FunctionResult object.
Old approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
FunctionCallingStepwisePlanner planner = new();
FunctionCallingStepwisePlannerResult result = await planner.ExecuteAsync(kernel, "Check current UTC time and return current weather in Boston city.");
string planResult = result.FinalAnswer;
New approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
OpenAIPromptExecutionSettings executionSettings = new() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() };
FunctionResult result = await kernel.InvokePromptAsync("Check current UTC time and return current weather in Boston city.", new(executionSettings));
string planResult = result.ToString();
Execution of the existing plan
Following code shows how to execute an existing plan with Auto Function Calling by using FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(). This approach is useful when ChatHistory is already present (e.g. stored in cache) and it should be re-executed again and final result should be provided by AI model.
Old approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
FunctionCallingStepwisePlanner planner = new();
ChatHistory existingPlan = GetExistingPlan(); // plan can be stored in database or cache for reusability.
FunctionCallingStepwisePlannerResult result = await planner.ExecuteAsync(kernel, "Check current UTC time and return current weather in Boston city.", existingPlan);
string planResult = result.FinalAnswer;
New approach:
Kernel kernel = Kernel
.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4", Environment.GetEnvironmentVariable("OpenAI__ApiKey"))
.Build();
IChatCompletionService chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
ChatHistory existingPlan = GetExistingPlan(); // plan can be stored in database or cache for reusability.
OpenAIPromptExecutionSettings executionSettings = new() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() };
ChatMessageContent result = await chatCompletionService.GetChatMessageContentAsync(existingPlan, executionSettings, kernel);
string planResult = result.Content;
The following code shows how to generate a new plan with Auto Function Calling by using function_choice_behavior = FunctionChoiceBehavior.Auto(). After sending a request to AI model, the plan will be located in ChatHistory object where a message with Assistant role will contain a list of functions (steps) to call.
Old approach:
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.planners.function_calling_stepwise_planner import (
FunctionCallingStepwisePlanner,
FunctionCallingStepwisePlannerResult,
)
kernel = Kernel()
kernel.add_service(AzureChatCompletion())
# Add any plugins to the kernel that the planner will leverage
kernel.add_plugins(...)
planner = FunctionCallingStepwisePlanner(service_id="service_id")
result: FunctionCallingStepwisePlannerResult = await planner.invoke(
kernel=kernel,
question="Check current UTC time and return current weather in Boston city.",
)
generated_plan = result.chat_history
New approach:
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureChatPromptExecutionSettings
from semantic_kernel.contents import ChatHistory
chat_completion_service = AzureChatCompletion()
chat_history = ChatHistory()
chat_hitory.add_user_message("Check current UTC time and return current weather in Boston city.")
request_settings = AzureChatPromptExecutionSettings(function_choice_behavior=FunctionChoiceBehavior.Auto())
# Add any plugins to the kernel that the planner will leverage
kernel = Kernel()
kernel.add_plugins(...)
response = await chat_completion_service.get_chat_message_content(
chat_history=chat_history,
settings=request_settings,
kernel=kernel,
)
print(response)
# The generated plan is now contained inside of `chat_history`.
Execution of the new plan
Following code shows how to execute a new plan with Auto Function Calling by using function_choice_behavior = FunctionChoiceBehavior.Auto(). This approach is useful when only the result is needed without plan steps. In this case, the Kernel object can be used to pass a goal to the invoke_prompt method. The result of plan execution will be located in a FunctionResult object.
Old approach:
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.planners.function_calling_stepwise_planner import (
FunctionCallingStepwisePlanner,
FunctionCallingStepwisePlannerResult,
)
kernel = Kernel()
kernel.add_service(AzureChatCompletion())
# Add any plugins to the kernel that the planner will leverage
kernel.add_plugins(...)
planner = FunctionCallingStepwisePlanner(service_id="service_id")
result: FunctionCallingStepwisePlannerResult = await planner.invoke(
kernel=kernel,
question="Check current UTC time and return current weather in Boston city.",
)
print(result.final_answer)
New approach:
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureChatPromptExecutionSettings
from semantic_kernel.contents import ChatHistory
from semantic_kernel.functions import KernelArguments
kernel = Kernel()
kernel.add_service(AzureChatCompletion())
# Add any plugins to the kernel that the planner will leverage
kernel.add_plugins(...)
chat_history = ChatHistory()
chat_hitory.add_user_message("Check current UTC time and return current weather in Boston city.")
request_settings = AzureChatPromptExecutionSettings(function_choice_behavior=FunctionChoiceBehavior.Auto())
response = await kernel.invoke_prompt(
"Check current UTC time and return current weather in Boston city.",
arguments=KernelArguments(settings=request_settings),
)
print(response)
Planners were not available in SK Java. Please use function calling directly.
The code snippets above demonstrate how to migrate your code that uses Stepwise Planner to use Auto Function Calling. Learn more about Function Calling with chat completion.