Stepwise Planner 移轉指南

此移轉指南示範如何從 FunctionCallingStepwisePlanner 移轉至新的規劃功能 - 自動函數呼叫的建議方法。 相較於 FunctionCallingStepwisePlanner，新方法能更可靠地產生結果，且使用較少的 token。

計畫生成

以下程式碼示範如何使用 FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() 來產生具有自動函式呼叫的新方案。 將要求傳送至 AI 模型之後，方案會位於物件中 ChatHistory ，其中具有 Assistant 角色的訊息將包含要呼叫的函式清單（步驟）。

舊方法：

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;

新方法：

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;

執行新計劃

下列程式代碼示範如何使用 自動函數呼叫 FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()來執行新的計劃。 當只需要結果而不需要計畫步驟時，這種方法很有用。 在此情況下， Kernel 物件可用來將目標傳遞至 InvokePromptAsync 方法。 計劃執行的結果會位於物件中 FunctionResult 。

舊方法：

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;

新方法：

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();

執行現有的計劃

以下程式碼示範如何透過自動函式呼叫，使用 FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() 執行現有方案。 當 ChatHistory 已存在（例如儲存在快取中），且應再次執行，並由 AI 模型提供最終結果時，此方法很有用。

舊方法：

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;

新方法：

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;

下列程式碼示範如何使用 function_choice_behavior = FunctionChoiceBehavior.Auto() 來產生一個新的方案，其中包含自動函數呼叫功能。 將要求傳送至 AI 模型之後，方案會位於物件中 ChatHistory ，其中具有 Assistant 角色的訊息將包含要呼叫的函式清單（步驟）。

舊方法：

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

新方法：

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`.

執行新計劃

下列程式代碼示範如何使用 自動函數呼叫 function_choice_behavior = FunctionChoiceBehavior.Auto()來執行新的計劃。 當只需結果而不需要計劃步驟時，這個方法就很有用。 在此情況下， Kernel 物件可用來將目標傳遞至 invoke_prompt 方法。 計劃執行的結果會位於物件中 FunctionResult 。

舊方法：

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)

新方法：

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)

規劃工具無法在 SK Java 中使用。 請直接使用函式呼叫。

上述代碼段示範如何移轉使用 Stepwise Planner 使用自動函數呼叫的程式代碼。 深入瞭解 使用聊天補全進行函式呼叫。