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plan.py
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plan.py
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# Copyright (c) Microsoft. All rights reserved.
import logging
import re
import threading
from collections.abc import Callable
from copy import copy
from typing import Any, ClassVar, Optional
from pydantic import PrivateAttr
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai import PromptExecutionSettings
from semantic_kernel.exceptions import KernelInvokeException
from semantic_kernel.functions.function_result import FunctionResult
from semantic_kernel.functions.kernel_arguments import KernelArguments
from semantic_kernel.functions.kernel_function import KernelFunction
from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
from semantic_kernel.utils.naming import generate_random_ascii_name
logger: logging.Logger = logging.getLogger(__name__)
class Plan:
_state: KernelArguments = PrivateAttr()
_steps: list["Plan"] = PrivateAttr()
_function: KernelFunction = PrivateAttr()
_parameters: KernelArguments = PrivateAttr()
_outputs: list[str] = PrivateAttr()
_has_next_step: bool = PrivateAttr()
_next_step_index: int = PrivateAttr()
_name: str = PrivateAttr()
_plugin_name: str = PrivateAttr()
_description: str = PrivateAttr()
_is_prompt: bool = PrivateAttr()
_prompt_execution_settings: PromptExecutionSettings = PrivateAttr()
DEFAULT_RESULT_KEY: ClassVar[str] = "PLAN.RESULT"
@property
def name(self) -> str:
return self._name
@property
def state(self) -> KernelArguments:
return self._state
@property
def steps(self) -> list["Plan"]:
return self._steps
@property
def plugin_name(self) -> str:
return self._plugin_name
@property
def description(self) -> str:
return self._description
@property
def function(self) -> Callable[..., Any]:
return self._function
@property
def parameters(self) -> KernelArguments:
return self._parameters
@property
def is_prompt(self) -> bool:
return self._is_prompt
@property
def is_native(self) -> bool:
if self._is_prompt is None:
return None
else:
return not self._is_prompt
@property
def prompt_execution_settings(self) -> PromptExecutionSettings:
return self._prompt_execution_settings
@property
def has_next_step(self) -> bool:
return self._next_step_index < len(self._steps)
@property
def next_step_index(self) -> int:
return self._next_step_index
def __init__(
self,
name: str | None = None,
plugin_name: str | None = None,
description: str | None = None,
next_step_index: int | None = None,
state: KernelArguments | None = None,
parameters: KernelArguments | None = None,
outputs: list[str] | None = None,
steps: list["Plan"] | None = None,
function: KernelFunction | None = None,
) -> None:
self._name = f"plan_{generate_random_ascii_name()}" if name is None else name
self._plugin_name = f"p_{generate_random_ascii_name()}" if plugin_name is None else plugin_name
self._description = "" if description is None else description
self._next_step_index = 0 if next_step_index is None else next_step_index
self._state = KernelArguments() if state is None else state
self._parameters = KernelArguments() if parameters is None else parameters
self._outputs = [] if outputs is None else outputs
self._steps = [] if steps is None else steps
self._has_next_step = len(self._steps) > 0
self._is_prompt = None
self._function = function or None
self._prompt_execution_settings = None
if function is not None:
self.set_function(function)
@classmethod
def from_goal(cls, goal: str) -> "Plan":
return cls(description=goal, plugin_name=cls.__name__)
@classmethod
def from_function(cls, function: KernelFunction) -> "Plan":
plan = cls()
plan.set_function(function)
return plan
async def invoke(
self,
kernel: Kernel,
arguments: KernelArguments | None = None,
# TODO: cancellation_token: CancellationToken,
) -> FunctionResult:
"""
Invoke the plan asynchronously.
Args:
input (str, optional): The input to the plan. Defaults to None.
arguments (KernelArguments, optional): The context to use. Defaults to None.
settings (PromptExecutionSettings, optional): The AI request settings to use. Defaults to None.
memory (SemanticTextMemoryBase, optional): The memory to use. Defaults to None.
**kwargs: Additional keyword arguments.
Returns:
KernelContext: The updated context.
"""
if not arguments:
arguments = copy(self._state)
if self._function is not None:
try:
result = await self._function.invoke(kernel=kernel, arguments=arguments)
except Exception as exc:
logger.error(f"Something went wrong in plan step {self._plugin_name}.{self._name}:'{exc}'")
raise KernelInvokeException(
"Error occurred while running plan step: " + str(exc),
exc,
) from exc
return result
else:
# loop through steps until completion
partial_results = []
while self.has_next_step:
function_arguments = copy(arguments)
self.add_variables_to_state(self._state, function_arguments)
logger.info(
"Invoking next step: "
+ str(self._steps[self._next_step_index].name)
+ " with arguments: "
+ str(function_arguments)
)
result = await self.invoke_next_step(kernel, function_arguments)
if result:
partial_results.append(result)
self._state[Plan.DEFAULT_RESULT_KEY] = str(result)
arguments = self.update_arguments_with_outputs(arguments)
logger.info(f"updated arguments: {arguments}")
result_string = str(partial_results[-1]) if len(partial_results) > 0 else ""
return FunctionResult(function=self.metadata, value=result_string, metadata={"results": partial_results})
def set_ai_configuration(
self,
settings: PromptExecutionSettings,
) -> None:
self._prompt_execution_settings = settings
@property
def metadata(self) -> KernelFunctionMetadata:
if self._function is not None:
return self._function.metadata
return KernelFunctionMetadata(
name=self._name or "Plan",
plugin_name=self._plugin_name,
parameters=[],
description=self._description,
is_prompt=self._is_prompt or False,
)
def set_available_functions(self, plan: "Plan", kernel: "Kernel", arguments: "KernelArguments") -> "Plan":
if len(plan.steps) == 0:
try:
pluginFunction = kernel.plugins[plan.plugin_name][plan.name]
plan.set_function(pluginFunction)
except Exception:
pass
else:
for step in plan.steps:
step = self.set_available_functions(step, kernel, arguments)
return plan
def add_steps(self, steps: list["Plan"] | list[KernelFunction]) -> None:
for step in steps:
if type(step) is Plan:
self._steps.append(step)
else:
new_step = Plan(
name=step.name,
plugin_name=step.plugin_name,
description=step.description,
next_step_index=0,
state=KernelArguments(),
parameters=KernelArguments(),
outputs=[],
steps=[],
)
new_step.set_function(step)
self._steps.append(new_step)
def set_function(self, function: KernelFunction) -> None:
self._function = function
self._name = function.name
self._plugin_name = function.plugin_name
self._description = function.description
self._is_prompt = function.is_prompt
if hasattr(function, "prompt_execution_settings"):
self._prompt_execution_settings = function.prompt_execution_settings
async def run_next_step(
self,
kernel: Kernel,
arguments: KernelArguments,
) -> Optional["FunctionResult"]:
return await self.invoke_next_step(kernel, arguments)
async def invoke_next_step(self, kernel: Kernel, arguments: KernelArguments) -> Optional["FunctionResult"]:
if not self.has_next_step:
return None
step = self._steps[self._next_step_index]
# merge the state with the current context variables for step execution
arguments = self.get_next_step_arguments(arguments, step)
try:
result = await step.invoke(kernel, arguments)
except Exception as exc:
raise KernelInvokeException(
"Error occurred while running plan step: " + str(exc),
exc,
) from exc
# Update state with result
self._state["input"] = str(result)
# Update plan result in state with matching outputs (if any)
if set(self._outputs).intersection(set(step._outputs)):
current_plan_result = ""
if Plan.DEFAULT_RESULT_KEY in self._state:
current_plan_result = self._state[Plan.DEFAULT_RESULT_KEY]
self._state[Plan.DEFAULT_RESULT_KEY] = current_plan_result.strip() + str(result)
# Increment the step
self._next_step_index += 1
return result
def add_variables_to_state(self, state: KernelArguments, variables: KernelArguments) -> None:
for key in variables.keys():
if key not in state.keys():
state[key] = variables[key]
def update_arguments_with_outputs(self, arguments: KernelArguments) -> KernelArguments:
if Plan.DEFAULT_RESULT_KEY in self._state:
result_string = self._state[Plan.DEFAULT_RESULT_KEY]
else:
result_string = str(self._state)
arguments["input"] = result_string
for item in self._steps[self._next_step_index - 1]._outputs:
if item in self._state:
arguments[item] = self._state[item]
else:
arguments[item] = result_string
return arguments
def get_next_step_arguments(self, arguments: KernelArguments, step: "Plan") -> KernelArguments:
# Priority for Input
# - Parameters (expand from variables if needed)
# - KernelArguments
# - Plan.State
# - Empty if sending to another plan
# - Plan.Description
input_ = None
step_input_value = step._parameters.get("input")
variables_input_value = arguments.get("input")
state_input_value = self._state.get("input")
if step_input_value and step_input_value != "":
input_ = step_input_value
elif variables_input_value and variables_input_value != "":
input_ = variables_input_value
elif state_input_value and state_input_value != "":
input_ = state_input_value
elif len(step._steps) > 0:
input_ = ""
elif self._description is not None and self._description != "":
input_ = self._description
step_arguments = KernelArguments(input=input_)
logger.debug(f"Step input: {step_arguments}")
# Priority for remaining stepVariables is:
# - Function Parameters (pull from variables or state by a key value)
# - Step Parameters (pull from variables or state by a key value)
# - All other variables. These are carried over in case the function wants access to the ambient content.
function_params = step.metadata
if function_params:
logger.debug(f"Function parameters: {function_params.parameters}")
for param in function_params.parameters:
if param.name in arguments:
step_arguments[param.name] = arguments[param.name]
elif param.name in self._state and (
self._state[param.name] is not None and self._state[param.name] != ""
):
step_arguments[param.name] = self._state[param.name]
logger.debug(f"Added other parameters: {step_arguments}")
for param_name, param_val in step.parameters.items():
if param_name in step_arguments:
continue
if param_name in arguments:
step_arguments[param_name] = param_val
elif param_name in self._state:
step_arguments[param_name] = self._state[param_name]
else:
expanded_value = self.expand_from_arguments(arguments, param_val)
step_arguments[param_name] = expanded_value
for item in arguments:
if item not in step_arguments:
step_arguments[item] = arguments[item]
logger.debug(f"Final step arguments: {step_arguments}")
return step_arguments
def expand_from_arguments(self, arguments: KernelArguments, input_from_step: Any) -> str:
result = input_from_step
variables_regex = r"\$(?P<var>\w+)"
matches = [m for m in re.finditer(variables_regex, str(input_from_step))]
ordered_matches = sorted(matches, key=lambda m: len(m.group("var")), reverse=True)
for match in ordered_matches:
var_name = match.group("var")
if var_name in arguments:
result = result.replace(f"${var_name}", arguments[var_name])
return result
def _runThread(self, code: Callable):
result = []
thread = threading.Thread(target=self._runCode, args=(code, result))
thread.start()
thread.join()
return result[0]