mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
### Motivation and Context In addition to the Python local process runtime, we're adding the ability to congfigure the Dapr runtime for Python SK processes. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description The PR introduces: - The ability to configure the Dapr Runtime for Python processes - Adds a sample FastAPI app showing how to configure the Dapr runtime with a sample SK Python process (see the README for details on setup) - Adds some unit test coverage for the Dapr runtime - Closes #9355 <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄
190 lines
6.9 KiB
Python
190 lines
6.9 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import asyncio
|
|
from enum import Enum
|
|
from typing import ClassVar
|
|
|
|
from pydantic import Field
|
|
|
|
from semantic_kernel import Kernel
|
|
from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
|
|
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
|
|
from semantic_kernel.contents import ChatHistory
|
|
from semantic_kernel.functions import kernel_function
|
|
from semantic_kernel.kernel_pydantic import KernelBaseModel
|
|
from semantic_kernel.processes.kernel_process.kernel_process_step import KernelProcessStep
|
|
from semantic_kernel.processes.kernel_process.kernel_process_step_context import KernelProcessStepContext
|
|
from semantic_kernel.processes.kernel_process.kernel_process_step_state import KernelProcessStepState
|
|
from semantic_kernel.processes.local_runtime.local_event import KernelProcessEvent
|
|
from semantic_kernel.processes.local_runtime.local_kernel_process import start
|
|
from semantic_kernel.processes.process_builder import ProcessBuilder
|
|
|
|
|
|
class CommonEvents(Enum):
|
|
UserInputReceived = "UserInputReceived"
|
|
AssistantResponseGenerated = "AssistantResponseGenerated"
|
|
|
|
|
|
class ChatBotEvents(Enum):
|
|
StartProcess = "startProcess"
|
|
IntroComplete = "introComplete"
|
|
AssistantResponseGenerated = "assistantResponseGenerated"
|
|
Exit = "exit"
|
|
|
|
|
|
class UserInputState(KernelBaseModel):
|
|
user_inputs: list[str] = []
|
|
current_input_index: int = 0
|
|
|
|
|
|
class ScriptedUserInputStep(KernelProcessStep[UserInputState]):
|
|
GET_USER_INPUT: ClassVar[str] = "get_user_input"
|
|
|
|
def create_default_state(self) -> "UserInputState":
|
|
"""Creates the default UserInputState."""
|
|
return UserInputState()
|
|
|
|
def populate_user_inputs(self):
|
|
"""Method to be overridden by the user to populate with custom user messages."""
|
|
pass
|
|
|
|
async def on_activate(self):
|
|
"""This is called during the activation of the process step."""
|
|
self.populate_user_inputs()
|
|
|
|
async def activate(self, state: KernelProcessStepState[UserInputState]):
|
|
"""Activates the step and sets the state."""
|
|
state.state = state.state or self.create_default_state()
|
|
self.state = state.state
|
|
self.populate_user_inputs()
|
|
pass
|
|
|
|
@kernel_function(name=GET_USER_INPUT)
|
|
async def get_user_input(self, context: KernelProcessStepContext):
|
|
"""Gets the user input."""
|
|
if not self.state:
|
|
raise ValueError("State has not been initialized")
|
|
|
|
user_message = self.state.user_inputs[self.state.current_input_index]
|
|
|
|
print(f"USER: {user_message}")
|
|
|
|
if "exit" in user_message:
|
|
await context.emit_event(process_event=ChatBotEvents.Exit, data=None)
|
|
return
|
|
|
|
self.state.current_input_index += 1
|
|
|
|
# Emit the user input event
|
|
await context.emit_event(process_event=CommonEvents.UserInputReceived, data=user_message)
|
|
|
|
|
|
class ChatUserInputStep(ScriptedUserInputStep):
|
|
def populate_user_inputs(self):
|
|
"""Override the method to populate user inputs for the chat step."""
|
|
if self.state is not None:
|
|
self.state.user_inputs.append("Hello")
|
|
self.state.user_inputs.append("How tall is the tallest mountain?")
|
|
self.state.user_inputs.append("How low is the lowest valley?")
|
|
self.state.user_inputs.append("How wide is the widest river?")
|
|
self.state.user_inputs.append("exit")
|
|
|
|
|
|
class IntroStep(KernelProcessStep):
|
|
@kernel_function
|
|
async def print_intro_message(self):
|
|
print("Welcome to Processes in Semantic Kernel.\n")
|
|
|
|
|
|
class ChatBotState(KernelBaseModel):
|
|
"""The state object for ChatBotResponseStep."""
|
|
|
|
chat_messages: list = []
|
|
|
|
|
|
SERVICE_ID = "default"
|
|
|
|
|
|
class ChatBotResponseStep(KernelProcessStep[ChatBotState]):
|
|
GET_CHAT_RESPONSE: ClassVar[str] = "get_chat_response"
|
|
|
|
state: ChatBotState = Field(default_factory=ChatBotState)
|
|
|
|
async def activate(self, state: "KernelProcessStepState[ChatBotState]"):
|
|
"""Activates the step and initializes the state object."""
|
|
self.state = state.state or ChatBotState()
|
|
self.state.chat_messages = self.state.chat_messages or []
|
|
|
|
@kernel_function(name=GET_CHAT_RESPONSE)
|
|
async def get_chat_response(self, context: "KernelProcessStepContext", user_message: str, kernel: "Kernel"):
|
|
"""Generates a response from the chat completion service."""
|
|
# Add user message to the state
|
|
self.state.chat_messages.append({"role": "user", "message": user_message})
|
|
|
|
# Get chat completion service and generate a response
|
|
chat_service: ChatCompletionClientBase = kernel.get_service(service_id=SERVICE_ID)
|
|
settings = chat_service.instantiate_prompt_execution_settings(service_id=SERVICE_ID)
|
|
|
|
chat_history = ChatHistory()
|
|
chat_history.add_user_message(user_message)
|
|
response = await chat_service.get_chat_message_contents(chat_history=chat_history, settings=settings)
|
|
|
|
if response is None:
|
|
raise ValueError("Failed to get a response from the chat completion service.")
|
|
|
|
answer = response[0].content
|
|
|
|
print(f"ASSISTANT: {answer}")
|
|
|
|
# Update state with the response
|
|
self.state.chat_messages.append(answer)
|
|
|
|
# Emit an event: assistantResponse
|
|
await context.emit_event(process_event=ChatBotEvents.AssistantResponseGenerated, data=answer)
|
|
|
|
|
|
kernel = Kernel()
|
|
|
|
|
|
async def step01_processes():
|
|
kernel.add_service(OpenAIChatCompletion(service_id="default"))
|
|
|
|
process = ProcessBuilder(name="ChatBot")
|
|
|
|
# Define the steps on the process builder based on their types, not concrete objects
|
|
intro_step = process.add_step(IntroStep)
|
|
user_input_step = process.add_step(ChatUserInputStep)
|
|
response_step = process.add_step(ChatBotResponseStep)
|
|
|
|
# Define the input event that starts the process and where to send it
|
|
process.on_input_event(event_id=ChatBotEvents.StartProcess).send_event_to(target=intro_step)
|
|
|
|
# Define the event that triggers the next step in the process
|
|
intro_step.on_function_result(function_name=IntroStep.print_intro_message.__name__).send_event_to(
|
|
target=user_input_step
|
|
)
|
|
|
|
# Define the event that triggers the process to stop
|
|
user_input_step.on_event(event_id=ChatBotEvents.Exit).stop_process()
|
|
# For the user step, send the user input to the response step
|
|
user_input_step.on_event(event_id=CommonEvents.UserInputReceived).send_event_to(
|
|
target=response_step, parameter_name="user_message"
|
|
)
|
|
|
|
# For the response step, send the response back to the user input step
|
|
response_step.on_event(event_id=ChatBotEvents.AssistantResponseGenerated).send_event_to(target=user_input_step)
|
|
|
|
# Build the kernel process
|
|
kernel_process = process.build()
|
|
|
|
# Start the process
|
|
await start(
|
|
process=kernel_process,
|
|
kernel=kernel,
|
|
initial_event=KernelProcessEvent(id=ChatBotEvents.StartProcess, data=None),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(step01_processes())
|