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semantic-kernel/python/samples/getting_started_with_processes/step01/step01_processes.py
T
Evan Mattson 29e3e83c2d Python: Add the Dapr Runtime for Processes (#9642)
### 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.

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### 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 😄
2024-11-15 14:43:15 +00:00

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())