警告
语义内核进程框架 是实验性的，仍在开发中，并且可能会更改。
概述
在上一部分中，我们构建了一个简单的流程，帮助我们自动创建新产品的文档。 在本部分中，我们将通过添加校对步骤来改进该过程。 此步骤将使用 LLM 将生成的文档评分为 Pass/Fail，并根据需要提供建议的更改。 通过利用进程框架对周期的支持，我们可以更进一步，自动应用建议的更改（如果有），然后重新开始循环，重复此过程，直到内容达到我们的质量标准。 更新后的过程如下所示：
对进程的更新
我们需要创建新的校对程序步骤，并对文档生成步骤进行一些更改，以便在需要时应用建议。
添加校对步骤
// 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();
}
}
已创建名为 ProofreadStep 的新步骤。 此步骤使用 LLM 对生成的文档进行评分，如上所述。 请注意，此步骤根据 LLM 的响应有条件地发出 DocumentationApproved 事件或 DocumentationRejected 事件。 对于 DocumentationApproved，事件将包含已批准的文档作为内容，而对于 DocumentationRejected，事件将包含校对员的建议。
# 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},
)
已创建名为 ProofreadStep 的新步骤。 此步骤使用 LLM 对生成的文档进行评分，如上所述。 请注意，此步骤根据 LLM 的响应有条件地发出 documentation_approved 事件或 documentation_rejected 事件。 对于 documentation_approved，事件将包含已批准的文档作为内容，而对于 documentation_rejected，事件将包含校对员的建议。
更新文档生成步骤
// 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，新增一个 KernelFunction。 校对步骤需要更改时，新功能将用于在文档中应用建议的修改。 请注意，这两个用于生成或重写文档的函数都会发出与 DocumentationGenerated 相同的事件，指示新文档可用。
# 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，新增一个 KernelFunction。 校对步骤需要更改时，新功能将用于在文档中应用建议的修改。 请注意，这两个用于生成或重写文档的函数都会发出与 documentation_generated 相同的事件，指示新文档可用。
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;
更新的流程路由现在执行以下操作：
- 向进程发送外部事件
id = Start时，此事件及其关联的数据将被发送到infoGatheringStep。 -
infoGatheringStep完成运行后，将返回的对象发送到docsGenerationStep该对象。 -
docsGenerationStep运行完成后，将生成的文档发送到 .docsProofreadStep - 当
docsProofreadStep拒绝我们的文档并提供建议时，请将建议发送回docsGenerationStep。 - 最后，
docsProofreadStep批准我们的文档后，将返回的对象发送到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)
更新的流程路由现在执行以下操作：
- 向进程发送外部事件
id = Start时，此事件及其关联的数据将被发送到info_gathering_step。 -
info_gathering_step完成运行后，将返回的对象发送到docs_generation_step该对象。 -
docs_generation_step运行完成后，将生成的文档发送到 .docs_proofread_step - 当
docs_proofread_step拒绝我们的文档并提供建议时，请将建议发送回docs_generation_step。 - 最后，当
docs_proofread_step批准我们的文档后，将返回的对象发送到docs_publish_step。
生成并运行进程
运行更新的进程会显示控制台中的以下输出：
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.
下一步是什么？
我们的流程现在可靠地生成符合我们定义的标准的文档。 这是伟大的，但在我们公开发布我们的文档之前，我们真的应该要求人工审查和批准。 接下来我们来做一下吧。