mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-05-03 08:12:39 +00:00
Merge branch 'main' into revert-90f1bee
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@@ -709,16 +709,35 @@ class Flow(Generic[T], metaclass=FlowMeta):
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raise TypeError(f"State must be dict or BaseModel, got {type(self._state)}")
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def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
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"""Start the flow execution.
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"""
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Start the flow execution in a synchronous context.
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This method wraps kickoff_async so that all state initialization and event
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emission is handled in the asynchronous method.
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"""
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async def run_flow():
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return await self.kickoff_async(inputs)
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return asyncio.run(run_flow())
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@init_flow_main_trace
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async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
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"""
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Start the flow execution asynchronously.
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This method performs state restoration (if an 'id' is provided and persistence is available)
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and updates the flow state with any additional inputs. It then emits the FlowStartedEvent,
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logs the flow startup, and executes all start methods. Once completed, it emits the
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FlowFinishedEvent and returns the final output.
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Args:
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inputs: Optional dictionary containing input values and potentially a state ID to restore
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"""
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# Handle state restoration if ID is provided in inputs
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if inputs and "id" in inputs and self._persistence is not None:
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restore_uuid = inputs["id"]
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stored_state = self._persistence.load_state(restore_uuid)
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inputs: Optional dictionary containing input values and/or a state ID for restoration.
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Returns:
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The final output from the flow, which is the result of the last executed method.
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"""
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if inputs:
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# Override the id in the state if it exists in inputs
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if "id" in inputs:
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if isinstance(self._state, dict):
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@@ -726,24 +745,27 @@ class Flow(Generic[T], metaclass=FlowMeta):
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elif isinstance(self._state, BaseModel):
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setattr(self._state, "id", inputs["id"])
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if stored_state:
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self._log_flow_event(
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f"Loading flow state from memory for UUID: {restore_uuid}",
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color="yellow",
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)
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# Restore the state
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self._restore_state(stored_state)
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else:
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self._log_flow_event(
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f"No flow state found for UUID: {restore_uuid}", color="red"
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)
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# If persistence is enabled, attempt to restore the stored state using the provided id.
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if "id" in inputs and self._persistence is not None:
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restore_uuid = inputs["id"]
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stored_state = self._persistence.load_state(restore_uuid)
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if stored_state:
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self._log_flow_event(
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f"Loading flow state from memory for UUID: {restore_uuid}",
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color="yellow",
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)
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self._restore_state(stored_state)
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else:
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self._log_flow_event(
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f"No flow state found for UUID: {restore_uuid}", color="red"
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)
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# Apply any additional inputs after restoration
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# Update state with any additional inputs (ignoring the 'id' key)
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filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
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if filtered_inputs:
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self._initialize_state(filtered_inputs)
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# Start flow execution
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# Emit FlowStartedEvent and log the start of the flow.
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crewai_event_bus.emit(
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self,
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FlowStartedEvent(
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@@ -765,14 +787,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
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if not self._start_methods:
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raise ValueError("No start method defined")
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# Execute all start methods concurrently.
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tasks = [
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self._execute_start_method(start_method)
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for start_method in self._start_methods
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]
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await asyncio.gather(*tasks)
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final_output = self._method_outputs[-1] if self._method_outputs else None
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# Emit FlowFinishedEvent after all processing is complete.
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crewai_event_bus.emit(
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self,
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FlowFinishedEvent(
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@@ -216,7 +216,6 @@ def convert_with_instructions(
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def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
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instructions = "Please convert the following text into valid JSON."
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print("Using function calling: ", llm.supports_function_calling())
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if llm.supports_function_calling():
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model_schema = PydanticSchemaParser(model=model).get_schema()
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instructions += (
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@@ -225,7 +224,6 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
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)
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else:
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model_description = generate_model_description(model)
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print("Model description: ", model_description)
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instructions += (
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f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
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f"The JSON must follow this format exactly:\n{model_description}"
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