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使い方: サイクルの使い方

警告

セマンティック カーネル プロセス フレームワーク は、まだ開発中であり、変更される可能性があります。

概要

前のセクションでは、新しい製品のドキュメントの作成を自動化するのに役立つ簡単なプロセスを構築しました。 このセクションでは、校正手順を追加することで、そのプロセスを改善します。 この手順では、LLM を使用して、生成されたドキュメントを合格/失敗として評価し、必要に応じて推奨される変更を提供します。 プロセス フレームワークのサイクルのサポートを利用することで、さらに 1 歩進み、推奨される変更 (ある場合) を自動的に適用し、サイクルをやり直して、コンテンツが品質バーを満たすまで繰り返すことができます。 更新されたプロセスは次のようになります。

我々のプロセスのフローチャートに、著者-評論家パターンのサイクルを組み込んだもの。

プロセスの更新

新しい校正者ステップを作成し、必要に応じて提案を適用できるようにするドキュメント生成ステップをいくつかの変更を加える必要があります。

校正手順を追加する

// A process step to proofread documentation
public class ProofreadStep : KernelProcessStep
{
    [KernelFunction]
    public async Task ProofreadDocumentationAsync(Kernel kernel, KernelProcessStepContext context, string documentation)
    {
        Console.WriteLine($"{nameof(ProofreadDocumentationAsync)}:\n\tProofreading documentation...");

        var systemPrompt =
            """
        Your job is to proofread customer facing documentation for a new product from Contoso. You will be provide with proposed documentation
        for a product and you must do the following things:

        1. Determine if the documentation is passes the following criteria:
            1. Documentation must use a professional tone.
            1. Documentation should be free of spelling or grammar mistakes.
            1. Documentation should be free of any offensive or inappropriate language.
            1. Documentation should be technically accurate.
        2. If the documentation does not pass 1, you must write detailed feedback of the changes that are needed to improve the documentation. 
        """;

        ChatHistory chatHistory = new ChatHistory(systemPrompt);
        chatHistory.AddUserMessage(documentation);

        // Use structured output to ensure the response format is easily parsable
        OpenAIPromptExecutionSettings settings = new OpenAIPromptExecutionSettings();
        settings.ResponseFormat = typeof(ProofreadingResponse);

        IChatCompletionService chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
        var proofreadResponse = await chatCompletionService.GetChatMessageContentAsync(chatHistory, executionSettings: settings);
        var formattedResponse = JsonSerializer.Deserialize<ProofreadingResponse>(proofreadResponse.Content!.ToString());

        Console.WriteLine($"\n\tGrade: {(formattedResponse!.MeetsExpectations ? "Pass" : "Fail")}\n\tExplanation: {formattedResponse.Explanation}\n\tSuggestions: {string.Join("\n\t\t", formattedResponse.Suggestions)}");

        if (formattedResponse.MeetsExpectations)
        {
            await context.EmitEventAsync("DocumentationApproved", data: documentation);
        }
        else
        {
            await context.EmitEventAsync("DocumentationRejected", data: new { Explanation = formattedResponse.Explanation, Suggestions = formattedResponse.Suggestions});
        }
    }

    // A class 
    private class ProofreadingResponse
    {
        [Description("Specifies if the proposed documentation meets the expected standards for publishing.")]
        public bool MeetsExpectations { get; set; }

        [Description("An explanation of why the documentation does or does not meet expectations.")]
        public string Explanation { get; set; } = "";

        [Description("A lis of suggestions, may be empty if there no suggestions for improvement.")]
        public List<string> Suggestions { get; set; } = new();
    }
}

ProofreadStep という名前の新しいステップが作成されました。 この手順では、LLM を使用して、上で説明したように生成されたドキュメントを評価します。 この手順では、LLM からの応答に基づいて、DocumentationApproved イベントまたは DocumentationRejected イベントが条件付きで出力されます。 DocumentationApprovedの場合、イベントにはペイロードとして承認済みのドキュメントが含まれます。DocumentationRejected の場合は、校正者からの提案が含まれます。

# A sample response model for the ProofreadingStep structured output
class ProofreadingResponse(BaseModel):
    """A class to represent the response from the proofreading step."""

    meets_expectations: bool = Field(description="Specifies if the proposed docs meets the standards for publishing.")
    explanation: str = Field(description="An explanation of why the documentation does or does not meet expectations.")
    suggestions: list[str] = Field(description="List of suggestions, empty if there are no suggestions for improvement.")

# A process step to proofread documentation
class ProofreadStep(KernelProcessStep):
    @kernel_function
    async def proofread_documentation(self, docs: str, context: KernelProcessStepContext, kernel: Kernel) -> None:
        print(f"{ProofreadStep.__name__}\n\t Proofreading product documentation...")

        system_prompt = """
        Your job is to proofread customer facing documentation for a new product from Contoso. You will be provided with 
        proposed documentation for a product and you must do the following things:

        1. Determine if the documentation passes the following criteria:
            1. Documentation must use a professional tone.
            1. Documentation should be free of spelling or grammar mistakes.
            1. Documentation should be free of any offensive or inappropriate language.
            1. Documentation should be technically accurate.
        2. If the documentation does not pass 1, you must write detailed feedback of the changes that are needed to 
            improve the documentation. 
        """

        chat_history = ChatHistory(system_message=system_prompt)
        chat_history.add_user_message(docs)

        # Use structured output to ensure the response format is easily parsable
        chat_service, settings = kernel.select_ai_service(type=ChatCompletionClientBase)
        assert isinstance(chat_service, ChatCompletionClientBase)  # nosec
        assert isinstance(settings, OpenAIChatPromptExecutionSettings)  # nosec

        settings.response_format = ProofreadingResponse

        response = await chat_service.get_chat_message_content(chat_history=chat_history, settings=settings)

        formatted_response: ProofreadingResponse = ProofreadingResponse.model_validate_json(response.content)

        suggestions_text = "\n\t\t".join(formatted_response.suggestions)
        print(
            f"\n\tGrade: {'Pass' if formatted_response.meets_expectations else 'Fail'}\n\t"
            f"Explanation: {formatted_response.explanation}\n\t"
            f"Suggestions: {suggestions_text}"
        )

        if formatted_response.meets_expectations:
            await context.emit_event(process_event="documentation_approved", data=docs)
        else:
            await context.emit_event(
                process_event="documentation_rejected",
                data={"explanation": formatted_response.explanation, "suggestions": formatted_response.suggestions},
            )

ProofreadStep という名前の新しいステップが作成されました。 この手順では、LLM を使用して、上で説明したように生成されたドキュメントを評価します。 この手順では、LLM からの応答に基づいて、documentation_approved イベントまたは documentation_rejected イベントが条件付きで出力されます。 documentation_approvedの場合、イベントにはペイロードとして承認済みのドキュメントが含まれます。documentation_rejected の場合は、校正者からの提案が含まれます。

ドキュメント生成手順を更新する

// Updated process step to generate and edit documentation for a product
public class GenerateDocumentationStep : KernelProcessStep<GeneratedDocumentationState>
{
    private GeneratedDocumentationState _state = new();

    private string systemPrompt =
            """
            Your job is to write high quality and engaging customer facing documentation for a new product from Contoso. You will be provide with information
            about the product in the form of internal documentation, specs, and troubleshooting guides and you must use this information and
            nothing else to generate the documentation. If suggestions are provided on the documentation you create, take the suggestions into account and
            rewrite the documentation. Make sure the product sounds amazing.
            """;

    override public ValueTask ActivateAsync(KernelProcessStepState<GeneratedDocumentationState> state)
    {
        this._state = state.State!;
        this._state.ChatHistory ??= new ChatHistory(systemPrompt);

        return base.ActivateAsync(state);
    }

    [KernelFunction]
    public async Task GenerateDocumentationAsync(Kernel kernel, KernelProcessStepContext context, string productInfo)
    {
        Console.WriteLine($"{nameof(GenerateDocumentationStep)}:\n\tGenerating documentation for provided productInfo...");

        // Add the new product info to the chat history
        this._state.ChatHistory!.AddUserMessage($"Product Info:\n\n{productInfo}");

        // Get a response from the LLM
        IChatCompletionService chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
        var generatedDocumentationResponse = await chatCompletionService.GetChatMessageContentAsync(this._state.ChatHistory!);

        await context.EmitEventAsync("DocumentationGenerated", generatedDocumentationResponse.Content!.ToString());
    }

    [KernelFunction]
    public async Task ApplySuggestionsAsync(Kernel kernel, KernelProcessStepContext context, string suggestions)
    {
        Console.WriteLine($"{nameof(GenerateDocumentationStep)}:\n\tRewriting documentation with provided suggestions...");

        // Add the new product info to the chat history
        this._state.ChatHistory!.AddUserMessage($"Rewrite the documentation with the following suggestions:\n\n{suggestions}");

        // Get a response from the LLM
        IChatCompletionService chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
        var generatedDocumentationResponse = await chatCompletionService.GetChatMessageContentAsync(this._state.ChatHistory!);

        await context.EmitEventAsync("DocumentationGenerated", generatedDocumentationResponse.Content!.ToString());
    }

    public class GeneratedDocumentationState
    {
        public ChatHistory? ChatHistory { get; set; }
    }
}

GenerateDocumentationStep が更新され、新しい KernelFunction が追加されました。 この新しい関数は、校正手順で必要な場合に、推奨される変更をドキュメントに適用するために使用されます。 ドキュメントを生成または書き換える両方の関数は、新しいドキュメントが使用可能であることを示す DocumentationGenerated という名前の同じイベントを生成することに注意してください。

# Updated process step to generate and edit documentation for a product
class GenerateDocumentationStep(KernelProcessStep[GeneratedDocumentationState]):
    state: GeneratedDocumentationState = Field(default_factory=GeneratedDocumentationState)

    system_prompt: ClassVar[str] = """
Your job is to write high quality and engaging customer facing documentation for a new product from Contoso. You will 
be provided with information about the product in the form of internal documentation, specs, and troubleshooting guides 
and you must use this information and nothing else to generate the documentation. If suggestions are provided on the 
documentation you create, take the suggestions into account and rewrite the documentation. Make sure the product 
sounds amazing.
"""

    async def activate(self, state: KernelProcessStepState[GeneratedDocumentationState]):
        self.state = state.state
        if self.state.chat_history is None:
            self.state.chat_history = ChatHistory(system_message=self.system_prompt)
        self.state.chat_history

    @kernel_function
    async def generate_documentation(
        self, context: KernelProcessStepContext, product_info: str, kernel: Kernel
    ) -> None:
        print(f"{GenerateDocumentationStep.__name__}\n\t Generating documentation for provided product_info...")

        self.state.chat_history.add_user_message(f"Product Information:\n{product_info}")

        chat_service, settings = kernel.select_ai_service(type=ChatCompletionClientBase)
        assert isinstance(chat_service, ChatCompletionClientBase)  # nosec

        response = await chat_service.get_chat_message_content(chat_history=self.state.chat_history, settings=settings)

        await context.emit_event(process_event="documentation_generated", data=str(response))

    @kernel_function
    async def apply_suggestions(self, suggestions: str, context: KernelProcessStepContext, kernel: Kernel) -> None:
        print(f"{GenerateDocumentationStep.__name__}\n\t Rewriting documentation with provided suggestions...")

        self.state.chat_history.add_user_message(
            f"Rewrite the documentation with the following suggestions:\n\n{suggestions}"
        )

        chat_service, settings = kernel.select_ai_service(type=ChatCompletionClientBase)
        assert isinstance(chat_service, ChatCompletionClientBase)  # nosec

        generated_documentation_response = await chat_service.get_chat_message_content(
            chat_history=self.state.chat_history, settings=settings
        )

        await context.emit_event(process_event="documentation_generated", data=str(generated_documentation_response))

GenerateDocumentationStep が更新され、新しい KernelFunction が追加されました。 この新しい関数は、校正手順で必要な場合に、推奨される変更をドキュメントに適用するために使用されます。 ドキュメントを生成または書き換える両方の関数は、新しいドキュメントが使用可能であることを示す documentation_generated という名前の同じイベントを生成することに注意してください。

フローの更新

// Create the process builder
ProcessBuilder processBuilder = new("DocumentationGeneration");

// Add the steps
var infoGatheringStep = processBuilder.AddStepFromType<GatherProductInfoStep>();
var docsGenerationStep = processBuilder.AddStepFromType<GenerateDocumentationStepV2>();
var docsProofreadStep = processBuilder.AddStepFromType<ProofreadStep>(); // Add new step here
var docsPublishStep = processBuilder.AddStepFromType<PublishDocumentationStep>();

// Orchestrate the events
processBuilder
    .OnInputEvent("Start")
    .SendEventTo(new(infoGatheringStep));

infoGatheringStep
    .OnFunctionResult()
    .SendEventTo(new(docsGenerationStep, functionName: "GenerateDocumentation"));

docsGenerationStep
    .OnEvent("DocumentationGenerated")
    .SendEventTo(new(docsProofreadStep));

docsProofreadStep
    .OnEvent("DocumentationRejected")
    .SendEventTo(new(docsGenerationStep, functionName: "ApplySuggestions"));

docsProofreadStep
    .OnEvent("DocumentationApproved")
    .SendEventTo(new(docsPublishStep));

var process = processBuilder.Build();
return process;

更新されたプロセス ルーティングでは、次の処理が行われます。

  • id = Startを含む外部イベントがプロセスに送信されると、このイベントとそれに関連付けられているデータがinfoGatheringStepに送信されます。
  • infoGatheringStepの実行が完了したら、返されたオブジェクトをdocsGenerationStepに送信します。
  • docsGenerationStepの実行が完了したら、生成されたドキュメントをdocsProofreadStepに送信します。
  • docsProofreadStep がドキュメントを拒否し、提案を提供したら、提案を docsGenerationStepに送り返します。
  • 最後に、 docsProofreadStep がドキュメントを承認したら、返されたオブジェクトを docsPublishStepに送信します。
# Create the process builder
process_builder = ProcessBuilder(name="DocumentationGeneration")

# Add the steps
info_gathering_step = process_builder.add_step(GatherProductInfoStep)
docs_generation_step = process_builder.add_step(GenerateDocumentationStep)
docs_proofread_step = process_builder.add_step(ProofreadStep)  # Add new step here
docs_publish_step = process_builder.add_step(PublishDocumentationStep)

# Orchestrate the events
process_builder.on_input_event("Start").send_event_to(target=info_gathering_step)

info_gathering_step.on_function_result().send_event_to(
    target=docs_generation_step, function_name="generate_documentation", parameter_name="product_info"
)

docs_generation_step.on_event("documentation_generated").send_event_to(
    target=docs_proofread_step, parameter_name="docs"
)

docs_proofread_step.on_event("documentation_rejected").send_event_to(
    target=docs_generation_step,
    function_name="apply_suggestions",
    parameter_name="suggestions",
)

docs_proofread_step.on_event("documentation_approved").send_event_to(target=docs_publish_step)

更新されたプロセス ルーティングでは、次の処理が行われます。

  • id = Startを含む外部イベントがプロセスに送信されると、このイベントとそれに関連付けられているデータがinfo_gathering_stepに送信されます。
  • info_gathering_stepの実行が完了したら、返されたオブジェクトをdocs_generation_stepに送信します。
  • docs_generation_stepの実行が完了したら、生成されたドキュメントをdocs_proofread_stepに送信します。
  • docs_proofread_step がドキュメントを拒否し、提案を提供したら、提案を docs_generation_stepに送り返します。
  • 最後に、 docs_proofread_step がドキュメントを承認したら、返されたオブジェクトを docs_publish_stepに送信します。

プロセスをビルドして実行する

更新されたプロセスを実行すると、コンソールに次の出力が表示されます。

GatherProductInfoStep:
        Gathering product information for product named Contoso GlowBrew
GenerateDocumentationStep:
        Generating documentation for provided productInfo...
ProofreadDocumentationAsync:
        Proofreading documentation...

        Grade: Fail
        Explanation: The proposed documentation has an overly casual tone and uses informal expressions that might not suit all customers. Additionally, some phrases may detract from the professionalism expected in customer-facing documentation. There are minor areas that could benefit from clarity and conciseness.
        Suggestions: Adjust the tone to be more professional and less casual; phrases like 'dazzling light show' and 'coffee performing' could be simplified.
                Remove informal phrases such as 'who knew coffee could be so... illuminating?'
                Consider editing out overly whimsical phrases like 'it's like a warm hug for your nose!' for a more straightforward description.
                Clarify the troubleshooting section for better customer understanding; avoid metaphorical language like 'secure that coffee cup when you realize Monday is still a thing.'
GenerateDocumentationStep:
        Rewriting documentation with provided suggestions...
ProofreadDocumentationAsync:
        Proofreading documentation...

        Grade: Fail
        Explanation: The documentation generally maintains a professional tone but contains minor phrasing issues that could be improved. There are no spelling or grammar mistakes noted, and it excludes any offensive language. However, the content could be more concise, and some phrases can be streamlined for clarity. Additionally, technical accuracy regarding troubleshooting solutions may require more details for the user's understanding. For example, clarifying how to 'reset the lighting settings through the designated app' would enhance user experience.
        Suggestions: Rephrase 'Join us as we elevate your coffee experience to new heights!' to make it more straightforward, such as 'Experience an elevated coffee journey with us.'
                In the 'Solution' section for the LED lights malfunction, add specific instructions on how to find and use the 'designated app' for resetting the lighting settings.
                Consider simplifying sentences such as 'Meet your new personal barista!' to be more straightforward, for example, 'Introducing your personal barista.'
                Ensure clarity in troubleshooting steps by elaborating on what a 'factory reset' entails.
GenerateDocumentationStep:
        Rewriting documentation with provided suggestions...
ProofreadDocumentationAsync:
        Proofreading documentation...

        Grade: Pass
        Explanation: The documentation presents a professional tone, contains no spelling or grammar mistakes, is free of offensive language, and is technically accurate regarding the product's features and troubleshooting guidance.
        Suggestions:
PublishDocumentationStep:
        Publishing product documentation:

# GlowBrew User Documentation

## Product Overview
Introducing GlowBrew-your new partner in coffee brewing that brings together advanced technology and aesthetic appeal. This innovative AI-driven coffee machine not only brews your favorite coffee but also features the industry's leading number of customizable LEDs and programmable light shows.

## Key Features

1. **Luminous Brew Technology**: Transform your morning routine with our customizable LED lights that synchronize with your brewing process, creating the perfect ambiance to start your day.

2. **AI Taste Assistant**: Our intelligent system learns your preferences over time, recommending exciting new brew combinations tailored to your unique taste.

3. **Gourmet Aroma Diffusion**: Experience an enhanced aroma with built-in aroma diffusers that elevate your coffee's scent profile, invigorating your senses before that all-important first sip.

## Troubleshooting

### Issue: LED Lights Malfunctioning

**Solution**:
- Begin by resetting the lighting settings via the designated app. Open the app, navigate to the settings menu, and select "Reset LED Lights."
- Ensure that all LED connections inside the GlowBrew are secure and properly connected.
- If issues persist, you may consider performing a factory reset. To do this, hold down the reset button located on the machine's back panel for 10 seconds while the device is powered on.

We hope you enjoy your GlowBrew experience and that it brings a delightful blend of flavor and brightness to your coffee moments!

GatherProductInfoStep
         Gathering product information for Product Name: Contoso GlowBrew
GenerateDocumentationStep
         Generating documentation for provided product_info...
ProofreadStep
         Proofreading product documentation...

        Grade: Pass
        Explanation: The GlowBrew AI Coffee Machine User Guide meets all the required criteria for publishing. The document maintains a professional tone throughout, is free from spelling and grammatical errors, contains no offensive or inappropriate content, and appears to be technically accurate in its description of the product features and troubleshooting advice.
        Suggestions: 
PublishDocumentationStep
         Publishing product documentation:

# GlowBrew AI Coffee Machine User Guide

Welcome to the future of coffee making with the GlowBrew AI Coffee Machine! Step into a world where cutting-edge technology meets exquisite taste, creating a coffee experience like no other. Designed for coffee aficionados and tech enthusiasts alike, the GlowBrew promises not just a cup of coffee, but an adventure for your senses.

## Key Features

### Luminous Brew Technology
Illuminate your mornings with the GlowBrew's mesmerizing programmable LED light shows. With an unmatched number of LEDs, the GlowBrew can transform your kitchen ambiance to sync perfectly with each stage of the brewing process. Choose from a spectrum of colors and patterns to set the perfect mood, whether you're winding down with a rich decaf or kick-starting your day with a bold espresso.

### AI Taste Assistant
Expand your coffee horizons with the AI Taste Assistant, your personal barista that learns and evolves with your palate. Over time, GlowBrew adapts to your preferences, suggesting new and exciting brew combinations. Experience a variety of flavors, from single-origin specialties to intricate blend recipes, tailored to your unique taste.

### Gourmet Aroma Diffusion
Enhance your coffee experience with unrivaled aromatic pleasure. The GlowBrew's built-in aroma diffusers release a carefully calibrated scent profile that awakens your senses, heightening anticipation for your first sip. It's not just a coffee machine, it's an indulgent sensory journey.

## Troubleshooting

### LED Lights Malfunctioning
If you experience issues with your LED lights:

1. **Reset the LED Settings**: Use the GlowBrew app to navigate to the lighting settings and perform a reset.
2. **Check LED Connections**: Open the GlowBrew machine and ensure all LED wiring connections are secure.
3. **Perform a Factory Reset**: As a last resort, a full factory reset can resolve persistent issues. Follow the instructions in the user manual to perform this reset safely.

## Experience the Glow

With GlowBrew, every cup of coffee is an art form that combines luminous aesthetics, an intuitive learning AI, and the intoxicating allure of rich aromas. Make each morning magical and every break a celebration with the GlowBrew AI Coffee Machine. Brew brilliantly, taste innovatively, and glow endlessly.

For more support, explore our comprehensive FAQ section or contact our dedicated customer service team.

次はどうする？

私たちのプロセスは、定義された標準を満たすドキュメントを確実に生成しています。 これは素晴らしいことですが、ドキュメントを公開する前に、人間にレビューと承認を求める必要があります。 次はそうしましょう。

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