警告
語意核心進程架構 是實驗性的，仍在開發中，而且可能會變更。
概述
在上一節中，我們建置了一個簡單的流程，來協助我們自動化地建立新產品的說明文件。 在本節中，我們將藉由新增校訂步驟來改善該程式。 此步驟將使用 和 LLM 將產生的文件評分為 Pass/Fail，並視需要提供建議的變更。 藉由利用 Process Framework 對週期的支持，我們可以更進一步地自動套用建議的變更（如果有的話），然後開始循環，重複此動作，直到內容符合我們的品質列為止。 更新的程式看起來會像這樣：
對流程的更新
我們需要建立新的校訂程式步驟，並且對文件產生步驟進行一些變更，以允許我們視需要套用建議。
新增校訂程式步驟
// 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();
}
}
已建立名為 ProofreadStep 的新步驟。 此步驟會使用 LLM 來評分產生的檔，如上述所述。 請注意，此步驟會根據 LLM 的回應，有條件地發出 DocumentationApproved 事件或 DocumentationRejected 事件。 在 DocumentationApproved的情況下，事件會包含核准的文件作為負載內容，而在 DocumentationRejected 的情況下則會包含校對者的建議。
# 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},
)
已建立名為 ProofreadStep 的新步驟。 此步驟會使用 LLM 來評分產生的檔，如上述所述。 請注意，此步驟會根據 LLM 的回應，有條件地發出 documentation_approved 事件或 documentation_rejected 事件。 在 documentation_approved的情況下，事件會包含核准的文件作為負載內容，而在 documentation_rejected 的情況下則會包含校對者的建議。
更新檔產生步驟
// 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 已更新，現在包含新的 Kernel函數。 如果我們的校訂步驟需要這些變更，則新函式將用來將建議的變更套用至檔。 請注意，這兩個用於產生或重寫檔的函式都會發出名為 DocumentationGenerated 的相同事件，指出有新的檔可供使用。
# 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 已更新，現在包含新的 Kernel函數。 如果我們的校訂步驟需要這些變更，則新函式將用來將建議的變更套用至檔。 請注意，這兩個用於產生或重寫檔的函式都會發出名為 documentation_generated 的相同事件，指出有新的檔可供使用。
流程更新
// 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;
我們的更新程式路由現在會執行下列動作：
- 將具有
id = Start的外部事件傳送至行程時，此事件及其相關聯的數據將會傳送至infoGatheringStep。 - 當
infoGatheringStep完成執行時，將傳回的物件傳送至docsGenerationStep。 - 當
docsGenerationStep完成執行時，將產生的檔案傳送至docsProofreadStep。 - 當
docsProofreadStep拒絕我們的檔並提供建議時，請將建議傳送回docsGenerationStep。 - 最後，當
docsProofreadStep核准我們的檔案後，將返回的物件傳送至docsPublishStep。
# 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)
我們的更新程式路由現在會執行下列動作：
- 將具有
id = Start的外部事件傳送至行程時，此事件及其相關聯的數據將會傳送至info_gathering_step。 - 當
info_gathering_step完成執行時，將傳回的物件傳送至docs_generation_step。 - 當
docs_generation_step完成執行時，將產生的檔案傳送至docs_proofread_step。 - 當
docs_proofread_step拒絕我們的檔並提供建議時，請將建議傳送回docs_generation_step。 - 最後，當
docs_proofread_step核准我們的文件後，將返回的物件傳送至docs_publish_step。
建置並執行程式
執行更新的程式會顯示控制台中的下列輸出：
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.
接下來是什麼？
我們的程式現在正可靠地產生符合我們定義標準的檔。 這很好，但在公開發佈我們的檔之前，我們真的應該需要人類來檢閱和核准。 接下來讓我們這麼做。