Files
crewAI/src/crewai/flow/flow.py
Marco Vinciguerra a548463fae feat: add docstring (#1819)
Co-authored-by: João Moura <joaomdmoura@gmail.com>
2024-12-31 01:51:43 -03:00

683 lines
23 KiB
Python

import asyncio
import inspect
from typing import (
Any,
Callable,
Dict,
Generic,
List,
Optional,
Set,
Type,
TypeVar,
Union,
cast,
)
from blinker import Signal
from pydantic import BaseModel, ValidationError
from crewai.flow.flow_events import (
FlowFinishedEvent,
FlowStartedEvent,
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
from crewai.flow.flow_visualizer import plot_flow
from crewai.flow.utils import get_possible_return_constants
from crewai.telemetry import Telemetry
T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
"""
Marks a method as a flow's starting point.
This decorator designates a method as an entry point for the flow execution.
It can optionally specify conditions that trigger the start based on other
method executions.
Parameters
----------
condition : Optional[Union[str, dict, Callable]], optional
Defines when the start method should execute. Can be:
- str: Name of a method that triggers this start
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this start
Default is None, meaning unconditional start.
Returns
-------
Callable
A decorator function that marks the method as a flow start point.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @start() # Unconditional start
>>> def begin_flow(self):
... pass
>>> @start("method_name") # Start after specific method
>>> def conditional_start(self):
... pass
>>> @start(and_("method1", "method2")) # Start after multiple methods
>>> def complex_start(self):
... pass
"""
def decorator(func):
func.__is_start_method__ = True
if condition is not None:
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
elif (
isinstance(condition, dict)
and "type" in condition
and "methods" in condition
):
func.__trigger_methods__ = condition["methods"]
func.__condition_type__ = condition["type"]
elif callable(condition) and hasattr(condition, "__name__"):
func.__trigger_methods__ = [condition.__name__]
func.__condition_type__ = "OR"
else:
raise ValueError(
"Condition must be a method, string, or a result of or_() or and_()"
)
return func
return decorator
def listen(condition: Union[str, dict, Callable]) -> Callable:
"""
Creates a listener that executes when specified conditions are met.
This decorator sets up a method to execute in response to other method
executions in the flow. It supports both simple and complex triggering
conditions.
Parameters
----------
condition : Union[str, dict, Callable]
Specifies when the listener should execute. Can be:
- str: Name of a method that triggers this listener
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this listener
Returns
-------
Callable
A decorator function that sets up the method as a listener.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @listen("process_data") # Listen to single method
>>> def handle_processed_data(self):
... pass
>>> @listen(or_("success", "failure")) # Listen to multiple methods
>>> def handle_completion(self):
... pass
"""
def decorator(func):
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
elif (
isinstance(condition, dict)
and "type" in condition
and "methods" in condition
):
func.__trigger_methods__ = condition["methods"]
func.__condition_type__ = condition["type"]
elif callable(condition) and hasattr(condition, "__name__"):
func.__trigger_methods__ = [condition.__name__]
func.__condition_type__ = "OR"
else:
raise ValueError(
"Condition must be a method, string, or a result of or_() or and_()"
)
return func
return decorator
def router(condition: Union[str, dict, Callable]) -> Callable:
"""
Creates a routing method that directs flow execution based on conditions.
This decorator marks a method as a router, which can dynamically determine
the next steps in the flow based on its return value. Routers are triggered
by specified conditions and can return constants that determine which path
the flow should take.
Parameters
----------
condition : Union[str, dict, Callable]
Specifies when the router should execute. Can be:
- str: Name of a method that triggers this router
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this router
Returns
-------
Callable
A decorator function that sets up the method as a router.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @router("check_status")
>>> def route_based_on_status(self):
... if self.state.status == "success":
... return SUCCESS
... return FAILURE
>>> @router(and_("validate", "process"))
>>> def complex_routing(self):
... if all([self.state.valid, self.state.processed]):
... return CONTINUE
... return STOP
"""
def decorator(func):
func.__is_router__ = True
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
elif (
isinstance(condition, dict)
and "type" in condition
and "methods" in condition
):
func.__trigger_methods__ = condition["methods"]
func.__condition_type__ = condition["type"]
elif callable(condition) and hasattr(condition, "__name__"):
func.__trigger_methods__ = [condition.__name__]
func.__condition_type__ = "OR"
else:
raise ValueError(
"Condition must be a method, string, or a result of or_() or and_()"
)
return func
return decorator
def or_(*conditions: Union[str, dict, Callable]) -> dict:
"""
Combines multiple conditions with OR logic for flow control.
Creates a condition that is satisfied when any of the specified conditions
are met. This is used with @start, @listen, or @router decorators to create
complex triggering conditions.
Parameters
----------
*conditions : Union[str, dict, Callable]
Variable number of conditions that can be:
- str: Method names
- dict: Existing condition dictionaries
- Callable: Method references
Returns
-------
dict
A condition dictionary with format:
{"type": "OR", "methods": list_of_method_names}
Raises
------
ValueError
If any condition is invalid.
Examples
--------
>>> @listen(or_("success", "timeout"))
>>> def handle_completion(self):
... pass
"""
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
methods.extend(condition["methods"])
elif isinstance(condition, str):
methods.append(condition)
elif callable(condition):
methods.append(getattr(condition, "__name__", repr(condition)))
else:
raise ValueError("Invalid condition in or_()")
return {"type": "OR", "methods": methods}
def and_(*conditions: Union[str, dict, Callable]) -> dict:
"""
Combines multiple conditions with AND logic for flow control.
Creates a condition that is satisfied only when all specified conditions
are met. This is used with @start, @listen, or @router decorators to create
complex triggering conditions.
Parameters
----------
*conditions : Union[str, dict, Callable]
Variable number of conditions that can be:
- str: Method names
- dict: Existing condition dictionaries
- Callable: Method references
Returns
-------
dict
A condition dictionary with format:
{"type": "AND", "methods": list_of_method_names}
Raises
------
ValueError
If any condition is invalid.
Examples
--------
>>> @listen(and_("validated", "processed"))
>>> def handle_complete_data(self):
... pass
"""
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
methods.extend(condition["methods"])
elif isinstance(condition, str):
methods.append(condition)
elif callable(condition):
methods.append(getattr(condition, "__name__", repr(condition)))
else:
raise ValueError("Invalid condition in and_()")
return {"type": "AND", "methods": methods}
class FlowMeta(type):
def __new__(mcs, name, bases, dct):
cls = super().__new__(mcs, name, bases, dct)
start_methods = []
listeners = {}
router_paths = {}
routers = set()
for attr_name, attr_value in dct.items():
if hasattr(attr_value, "__is_start_method__"):
start_methods.append(attr_name)
if hasattr(attr_value, "__trigger_methods__"):
methods = attr_value.__trigger_methods__
condition_type = getattr(attr_value, "__condition_type__", "OR")
listeners[attr_name] = (condition_type, methods)
elif hasattr(attr_value, "__trigger_methods__"):
methods = attr_value.__trigger_methods__
condition_type = getattr(attr_value, "__condition_type__", "OR")
listeners[attr_name] = (condition_type, methods)
if hasattr(attr_value, "__is_router__") and attr_value.__is_router__:
routers.add(attr_name)
possible_returns = get_possible_return_constants(attr_value)
if possible_returns:
router_paths[attr_name] = possible_returns
setattr(cls, "_start_methods", start_methods)
setattr(cls, "_listeners", listeners)
setattr(cls, "_routers", routers)
setattr(cls, "_router_paths", router_paths)
return cls
class Flow(Generic[T], metaclass=FlowMeta):
_telemetry = Telemetry()
_start_methods: List[str] = []
_listeners: Dict[str, tuple[str, List[str]]] = {}
_routers: Set[str] = set()
_router_paths: Dict[str, List[str]] = {}
initial_state: Union[Type[T], T, None] = None
event_emitter = Signal("event_emitter")
def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
class _FlowGeneric(cls): # type: ignore
_initial_state_T = item # type: ignore
_FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
return _FlowGeneric
def __init__(self) -> None:
self._methods: Dict[str, Callable] = {}
self._state: T = self._create_initial_state()
self._method_execution_counts: Dict[str, int] = {}
self._pending_and_listeners: Dict[str, Set[str]] = {}
self._method_outputs: List[Any] = [] # List to store all method outputs
self._telemetry.flow_creation_span(self.__class__.__name__)
for method_name in dir(self):
if callable(getattr(self, method_name)) and not method_name.startswith(
"__"
):
self._methods[method_name] = getattr(self, method_name)
def _create_initial_state(self) -> T:
if self.initial_state is None and hasattr(self, "_initial_state_T"):
return self._initial_state_T() # type: ignore
if self.initial_state is None:
return {} # type: ignore
elif isinstance(self.initial_state, type):
return self.initial_state()
else:
return self.initial_state
@property
def state(self) -> T:
return self._state
@property
def method_outputs(self) -> List[Any]:
"""Returns the list of all outputs from executed methods."""
return self._method_outputs
def _initialize_state(self, inputs: Dict[str, Any]) -> None:
if isinstance(self._state, BaseModel):
# Structured state
try:
def create_model_with_extra_forbid(
base_model: Type[BaseModel],
) -> Type[BaseModel]:
class ModelWithExtraForbid(base_model): # type: ignore
model_config = base_model.model_config.copy()
model_config["extra"] = "forbid"
return ModelWithExtraForbid
ModelWithExtraForbid = create_model_with_extra_forbid(
self._state.__class__
)
self._state = cast(
T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
)
except ValidationError as e:
raise ValueError(f"Invalid inputs for structured state: {e}") from e
elif isinstance(self._state, dict):
self._state.update(inputs)
else:
raise TypeError("State must be a BaseModel instance or a dictionary.")
def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
self.event_emitter.send(
self,
event=FlowStartedEvent(
type="flow_started",
flow_name=self.__class__.__name__,
),
)
if inputs is not None:
self._initialize_state(inputs)
return asyncio.run(self.kickoff_async())
async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
if not self._start_methods:
raise ValueError("No start method defined")
self._telemetry.flow_execution_span(
self.__class__.__name__, list(self._methods.keys())
)
tasks = [
self._execute_start_method(start_method)
for start_method in self._start_methods
]
await asyncio.gather(*tasks)
final_output = self._method_outputs[-1] if self._method_outputs else None
self.event_emitter.send(
self,
event=FlowFinishedEvent(
type="flow_finished",
flow_name=self.__class__.__name__,
result=final_output,
),
)
return final_output
async def _execute_start_method(self, start_method_name: str) -> None:
"""
Executes a flow's start method and its triggered listeners.
This internal method handles the execution of methods marked with @start
decorator and manages the subsequent chain of listener executions.
Parameters
----------
start_method_name : str
The name of the start method to execute.
Notes
-----
- Executes the start method and captures its result
- Triggers execution of any listeners waiting on this start method
- Part of the flow's initialization sequence
"""
result = await self._execute_method(
start_method_name, self._methods[start_method_name]
)
await self._execute_listeners(start_method_name, result)
async def _execute_method(
self, method_name: str, method: Callable, *args: Any, **kwargs: Any
) -> Any:
result = (
await method(*args, **kwargs)
if asyncio.iscoroutinefunction(method)
else method(*args, **kwargs)
)
self._method_outputs.append(result)
self._method_execution_counts[method_name] = (
self._method_execution_counts.get(method_name, 0) + 1
)
return result
async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
"""
Executes all listeners and routers triggered by a method completion.
This internal method manages the execution flow by:
1. First executing all triggered routers sequentially
2. Then executing all triggered listeners in parallel
Parameters
----------
trigger_method : str
The name of the method that triggered these listeners.
result : Any
The result from the triggering method, passed to listeners
that accept parameters.
Notes
-----
- Routers are executed sequentially to maintain flow control
- Each router's result becomes the new trigger_method
- Normal listeners are executed in parallel for efficiency
- Listeners can receive the trigger method's result as a parameter
"""
# First, handle routers repeatedly until no router triggers anymore
while True:
routers_triggered = self._find_triggered_methods(
trigger_method, router_only=True
)
if not routers_triggered:
break
for router_name in routers_triggered:
await self._execute_single_listener(router_name, result)
# After executing router, the router's result is the path
# The last router executed sets the trigger_method
# The router result is the last element in self._method_outputs
trigger_method = self._method_outputs[-1]
# Now that no more routers are triggered by current trigger_method,
# execute normal listeners
listeners_triggered = self._find_triggered_methods(
trigger_method, router_only=False
)
if listeners_triggered:
tasks = [
self._execute_single_listener(listener_name, result)
for listener_name in listeners_triggered
]
await asyncio.gather(*tasks)
def _find_triggered_methods(
self, trigger_method: str, router_only: bool
) -> List[str]:
"""
Finds all methods that should be triggered based on conditions.
This internal method evaluates both OR and AND conditions to determine
which methods should be executed next in the flow.
Parameters
----------
trigger_method : str
The name of the method that just completed execution.
router_only : bool
If True, only consider router methods.
If False, only consider non-router methods.
Returns
-------
List[str]
Names of methods that should be triggered.
Notes
-----
- Handles both OR and AND conditions:
* OR: Triggers if any condition is met
* AND: Triggers only when all conditions are met
- Maintains state for AND conditions using _pending_and_listeners
- Separates router and normal listener evaluation
"""
triggered = []
for listener_name, (condition_type, methods) in self._listeners.items():
is_router = listener_name in self._routers
if router_only != is_router:
continue
if condition_type == "OR":
# If the trigger_method matches any in methods, run this
if trigger_method in methods:
triggered.append(listener_name)
elif condition_type == "AND":
# Initialize pending methods for this listener if not already done
if listener_name not in self._pending_and_listeners:
self._pending_and_listeners[listener_name] = set(methods)
# Remove the trigger method from pending methods
if trigger_method in self._pending_and_listeners[listener_name]:
self._pending_and_listeners[listener_name].discard(trigger_method)
if not self._pending_and_listeners[listener_name]:
# All required methods have been executed
triggered.append(listener_name)
# Reset pending methods for this listener
self._pending_and_listeners.pop(listener_name, None)
return triggered
async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
"""
Executes a single listener method with proper event handling.
This internal method manages the execution of an individual listener,
including parameter inspection, event emission, and error handling.
Parameters
----------
listener_name : str
The name of the listener method to execute.
result : Any
The result from the triggering method, which may be passed
to the listener if it accepts parameters.
Notes
-----
- Inspects method signature to determine if it accepts the trigger result
- Emits events for method execution start and finish
- Handles errors gracefully with detailed logging
- Recursively triggers listeners of this listener
- Supports both parameterized and parameter-less listeners
Error Handling
-------------
Catches and logs any exceptions during execution, preventing
individual listener failures from breaking the entire flow.
"""
try:
method = self._methods[listener_name]
self.event_emitter.send(
self,
event=MethodExecutionStartedEvent(
type="method_execution_started",
method_name=listener_name,
flow_name=self.__class__.__name__,
),
)
sig = inspect.signature(method)
params = list(sig.parameters.values())
method_params = [p for p in params if p.name != "self"]
if method_params:
listener_result = await self._execute_method(
listener_name, method, result
)
else:
listener_result = await self._execute_method(listener_name, method)
self.event_emitter.send(
self,
event=MethodExecutionFinishedEvent(
type="method_execution_finished",
method_name=listener_name,
flow_name=self.__class__.__name__,
),
)
# Execute listeners (and possibly routers) of this listener
await self._execute_listeners(listener_name, listener_result)
except Exception as e:
print(
f"[Flow._execute_single_listener] Error in method {listener_name}: {e}"
)
import traceback
traceback.print_exc()
def plot(self, filename: str = "crewai_flow") -> None:
self._telemetry.flow_plotting_span(
self.__class__.__name__, list(self._methods.keys())
)
plot_flow(self, filename)