mirror of
https://github.com/crewAIInc/crewAI.git
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Bugfix/kickoff for each usage metrics (#844)
* WIP. Figuring out disconnect issue. * Cleaned up logs now that I've isolated the issue to the LLM * more wip. * WIP. It looks like usage metrics has always been broken for async * Update parent crew who is managing for_each loop * Merge in main to bugfix/kickoff-for-each-usage-metrics * Clean up code for review * Add new tests * Final cleanup. Ready for review. * Moving copy functionality from Agent to BaseAgent * Fix renaming issue * Fix linting errors * use BaseAgent instead of Agent where applicable
This commit is contained in:
Brandon Hancock (bhancock_ai)
committed by
GitHub
GitHub
parent
706f4cd74a
commit
57fc079267
3
.gitignore
vendored
3
.gitignore
vendored
@@ -13,4 +13,5 @@ db/
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test.py
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rc-tests/*
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*.pkl
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temp/*
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temp/*
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.vscode/*
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@@ -7,16 +7,15 @@ from langchain.tools.render import render_text_description
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from langchain_core.agents import AgentAction
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_openai import ChatOpenAI
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from pydantic import Field, InstanceOf, model_validator
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from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.memory.contextual.contextual_memory import ContextualMemory
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from crewai.tools.agent_tools import AgentTools
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from crewai.utilities import Prompts, Converter
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from crewai.utilities import Converter, Prompts
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from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
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from crewai.utilities.token_counter_callback import TokenCalcHandler
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.utilities.training_handler import CrewTrainingHandler
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agentops = None
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@@ -91,7 +90,6 @@ class Agent(BaseAgent):
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response_template: Optional[str] = Field(
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default=None, description="Response format for the agent."
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)
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allow_code_execution: Optional[bool] = Field(
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default=False, description="Enable code execution for the agent."
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)
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@@ -103,7 +101,7 @@ class Agent(BaseAgent):
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@model_validator(mode="after")
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def set_agent_executor(self) -> "Agent":
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"""Ensure agent executor and token process is set."""
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"""Ensure agent executor and token process are set."""
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if hasattr(self.llm, "model_name"):
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token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
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@@ -1,23 +1,26 @@
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from copy import deepcopy
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import uuid
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from typing import Any, Dict, List, Optional
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from abc import ABC, abstractmethod
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from copy import copy as shallow_copy
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from typing import Any, Dict, List, Optional, TypeVar
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from pydantic import (
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UUID4,
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BaseModel,
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ConfigDict,
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Field,
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InstanceOf,
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PrivateAttr,
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field_validator,
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model_validator,
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ConfigDict,
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PrivateAttr,
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)
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from pydantic_core import PydanticCustomError
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from crewai.utilities import I18N, RPMController, Logger
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from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
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from crewai.agents.cache.cache_handler import CacheHandler
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.utilities.token_counter_callback import TokenProcess
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from crewai.utilities import I18N, Logger, RPMController
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T = TypeVar("T", bound="BaseAgent")
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class BaseAgent(ABC, BaseModel):
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@@ -188,6 +191,31 @@ class BaseAgent(ABC, BaseModel):
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"""Get the converter class for the agent to create json/pydantic outputs."""
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pass
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def copy(self: T) -> T:
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"""Create a deep copy of the Agent."""
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exclude = {
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"id",
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"_logger",
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"_rpm_controller",
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"_request_within_rpm_limit",
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"_token_process",
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"agent_executor",
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"tools",
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"tools_handler",
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"cache_handler",
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"llm",
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}
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# Copy llm and clear callbacks
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existing_llm = shallow_copy(self.llm)
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existing_llm.callbacks = []
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copied_data = self.model_dump(exclude=exclude)
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copied_data = {k: v for k, v in copied_data.items() if v is not None}
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copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
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return copied_agent
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def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
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"""Interpolate inputs into the agent description and backstory."""
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if self._original_role is None:
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@@ -217,35 +245,6 @@ class BaseAgent(ABC, BaseModel):
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def increment_formatting_errors(self) -> None:
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self.formatting_errors += 1
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def copy(self):
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exclude = {
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"id",
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"_logger",
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"_rpm_controller",
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"_request_within_rpm_limit",
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"token_process",
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"agent_executor",
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"tools",
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"tools_handler",
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"cache_handler",
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"crew",
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"llm",
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}
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copied_data = self.model_dump(exclude=exclude, exclude_unset=True)
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copied_agent = self.__class__(**copied_data)
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# Copy mutable attributes separately
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copied_agent.tools = deepcopy(self.tools)
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copied_agent.config = deepcopy(self.config)
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# Preserve original values for interpolation
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copied_agent._original_role = self._original_role
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copied_agent._original_goal = self._original_goal
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copied_agent._original_backstory = self._original_backstory
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return copied_agent
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def set_rpm_controller(self, rpm_controller: RPMController) -> None:
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"""Set the rpm controller for the agent.
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@@ -299,16 +299,13 @@ class Crew(BaseModel):
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# type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
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agent.crew = self # type: ignore[attr-defined]
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# TODO: Create an AgentFunctionCalling protocol for future refactoring
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if (
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hasattr(agent, "function_calling_llm")
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and not agent.function_calling_llm
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):
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if not agent.function_calling_llm:
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agent.function_calling_llm = self.function_calling_llm
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if hasattr(agent, "allow_code_execution") and agent.allow_code_execution:
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if agent.allow_code_execution:
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agent.tools += agent.get_code_execution_tools()
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if hasattr(agent, "step_callback") and not agent.step_callback:
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if not agent.step_callback:
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agent.step_callback = self.step_callback
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agent.create_agent_executor()
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@@ -326,9 +323,7 @@ class Crew(BaseModel):
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raise NotImplementedError(
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f"The process '{self.process}' is not implemented yet."
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)
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metrics = metrics + [
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agent._token_process.get_summary() for agent in self.agents
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]
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metrics += [agent._token_process.get_summary() for agent in self.agents]
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self.usage_metrics = {
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key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
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@@ -336,21 +331,32 @@ class Crew(BaseModel):
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return result
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def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List:
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def kickoff_for_each(
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self, inputs: List[Dict[str, Any]]
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) -> List[Union[str, Dict[str, Any]]]:
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"""Executes the Crew's workflow for each input in the list and aggregates results."""
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results = []
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# Initialize the parent crew's usage metrics
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total_usage_metrics = {
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"total_tokens": 0,
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"successful_requests": 0,
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}
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for input_data in inputs:
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crew = self.copy()
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for task in crew.tasks:
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task.interpolate_inputs(input_data)
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for agent in crew.agents:
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agent.interpolate_inputs(input_data)
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output = crew.kickoff(inputs=input_data)
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if crew.usage_metrics:
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for key in total_usage_metrics:
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total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
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output = crew.kickoff()
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results.append(output)
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self.usage_metrics = total_usage_metrics
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return results
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async def kickoff_async(
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@@ -360,26 +366,37 @@ class Crew(BaseModel):
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return await asyncio.to_thread(self.kickoff, inputs)
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async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[Any]:
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async def run_crew(input_data):
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crew = self.copy()
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crew_copies = [self.copy() for _ in inputs]
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for task in crew.tasks:
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task.interpolate_inputs(input_data)
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for agent in crew.agents:
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agent.interpolate_inputs(input_data)
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async def run_crew(crew, input_data):
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return await crew.kickoff_async(inputs=input_data)
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return await crew.kickoff_async()
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tasks = [asyncio.create_task(run_crew(input_data)) for input_data in inputs]
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tasks = [
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asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
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for i in range(len(inputs))
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]
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results = await asyncio.gather(*tasks)
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total_usage_metrics = {
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"total_tokens": 0,
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"successful_requests": 0,
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}
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for crew in crew_copies:
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if crew.usage_metrics:
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for key in total_usage_metrics:
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total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
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self.usage_metrics = total_usage_metrics
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return results
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def _run_sequential_process(self) -> str:
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"""Executes tasks sequentially and returns the final output."""
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task_output = ""
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token_usage = []
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for task in self.tasks:
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if task.agent.allow_delegation: # type: ignore # Item "None" of "Agent | None" has no attribute "allow_delegation"
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agents_for_delegation = [
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@@ -405,18 +422,16 @@ class Crew(BaseModel):
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role = task.agent.role if task.agent is not None else "None"
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self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
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token_summ = task.agent._token_process.get_summary()
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token_usage.append(token_summ)
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if self.output_log_file:
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self._file_handler.log(agent=role, task=task_output, status="completed")
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token_usage_formatted = self.aggregate_token_usage(token_usage)
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self._finish_execution(task_output)
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token_usage = self.calculate_usage_metrics()
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# type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
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return self._format_output(task_output, token_usage_formatted)
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return self._format_output(task_output, token_usage)
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def _run_hierarchical_process(self) -> Union[str, Dict[str, Any]]:
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"""Creates and assigns a manager agent to make sure the crew completes the tasks."""
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@@ -437,9 +452,10 @@ class Crew(BaseModel):
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llm=self.manager_llm,
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verbose=self.verbose,
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)
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self.manager_agent = manager
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task_output = ""
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token_usage = []
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for task in self.tasks:
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self._logger.log("debug", f"Working Agent: {manager.role}")
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self._logger.log("info", f"Starting Task: {task.description}")
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@@ -454,9 +470,6 @@ class Crew(BaseModel):
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)
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self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
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if hasattr(task, "agent._token_process"):
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token_summ = task.agent._token_process.get_summary()
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token_usage.append(token_summ)
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if self.output_log_file:
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self._file_handler.log(
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agent=manager.role, task=task_output, status="completed"
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@@ -465,13 +478,9 @@ class Crew(BaseModel):
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self._finish_execution(task_output)
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# type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
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manager_token_usage = manager._token_process.get_summary()
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token_usage.append(manager_token_usage)
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token_usage_formatted = self.aggregate_token_usage(token_usage)
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token_usage = self.calculate_usage_metrics()
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return self._format_output(
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task_output, token_usage_formatted
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), manager_token_usage
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return self._format_output(task_output, token_usage), token_usage
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def copy(self):
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"""Create a deep copy of the Crew."""
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@@ -486,12 +495,13 @@ class Crew(BaseModel):
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"_short_term_memory",
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"_long_term_memory",
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"_entity_memory",
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"_telemetry",
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"agents",
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"tasks",
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}
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cloned_agents = [agent.copy() for agent in self.agents]
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cloned_tasks = [task.copy() for task in self.tasks]
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cloned_tasks = [task.copy(cloned_agents) for task in self.tasks]
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copied_data = self.model_dump(exclude=exclude)
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copied_data = {k: v for k, v in copied_data.items() if v is not None}
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@@ -529,6 +539,7 @@ class Crew(BaseModel):
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Formats the output of the crew execution.
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If full_output is True, then returned data type will be a dictionary else returned outputs are string
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"""
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if self.full_output:
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return { # type: ignore # Incompatible return value type (got "dict[str, Sequence[str | TaskOutput | None]]", expected "str")
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"final_output": output,
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@@ -547,11 +558,27 @@ class Crew(BaseModel):
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)
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self._telemetry.end_crew(self, output)
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def calculate_usage_metrics(self) -> Dict[str, int]:
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"""Calculates and returns the usage metrics."""
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total_usage_metrics = {
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"total_tokens": 0,
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"successful_requests": 0,
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}
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for agent in self.agents:
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if hasattr(agent, "_token_process"):
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token_sum = agent._token_process.get_summary()
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for key in total_usage_metrics:
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total_usage_metrics[key] += token_sum.get(key, 0)
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if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
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token_sum = self.manager_agent._token_process.get_summary()
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for key in total_usage_metrics:
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total_usage_metrics[key] += token_sum.get(key, 0)
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return total_usage_metrics
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def __repr__(self):
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return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
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def aggregate_token_usage(self, token_usage_list: List[Dict[str, Any]]):
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return {
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key: sum([m[key] for m in token_usage_list if m is not None])
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for key in token_usage_list[0]
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}
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@@ -2,8 +2,8 @@ import os
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import re
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import threading
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import uuid
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from copy import deepcopy
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from typing import Any, Dict, List, Optional, Type
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from copy import copy
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from typing import Any, Dict, List, Optional, Type, Union
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from langchain_openai import ChatOpenAI
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from opentelemetry.trace import Span
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@@ -13,7 +13,9 @@ from pydantic_core import PydanticCustomError
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.tasks.task_output import TaskOutput
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from crewai.telemetry.telemetry import Telemetry
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from crewai.utilities import I18N, ConverterError, Printer
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from crewai.utilities.converter import ConverterError
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from crewai.utilities.i18n import I18N
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from crewai.utilities.printer import Printer
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from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
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|
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@@ -216,7 +218,7 @@ class Task(BaseModel):
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)
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return result
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def _execute(self, agent, task, context, tools):
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def _execute(self, agent: "BaseAgent", task, context, tools):
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result = agent.execute_task(
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task=task,
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context=context,
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@@ -274,7 +276,7 @@ class Task(BaseModel):
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"""Increment the delegations counter."""
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self.delegations += 1
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def copy(self):
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def copy(self, agents: Optional[List["BaseAgent"]] = None) -> "Task":
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"""Create a deep copy of the Task."""
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exclude = {
|
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"id",
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@@ -289,8 +291,12 @@ class Task(BaseModel):
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cloned_context = (
|
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[task.copy() for task in self.context] if self.context else None
|
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)
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cloned_agent = self.agent.copy() if self.agent else None
|
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cloned_tools = deepcopy(self.tools) if self.tools else []
|
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def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
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return next((agent for agent in agents if agent.role == role), None)
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|
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cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
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cloned_tools = copy(self.tools) if self.tools else []
|
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|
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copied_task = Task(
|
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**copied_data,
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@@ -298,6 +304,7 @@ class Task(BaseModel):
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agent=cloned_agent,
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tools=cloned_tools,
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)
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|
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return copied_task
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|
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def _export_output(self, result: str) -> Any:
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@@ -329,7 +336,7 @@ class Task(BaseModel):
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except Exception:
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pass
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|
||||
# type: ignore # Item "None" of "Agent | None" has no attribute "function_calling_llm"
|
||||
# type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
|
||||
llm = getattr(self.agent, "function_calling_llm", None) or self.agent.llm
|
||||
if not self._is_gpt(llm):
|
||||
# type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
|
||||
@@ -355,7 +362,9 @@ class Task(BaseModel):
|
||||
if self.output_file:
|
||||
content = (
|
||||
# type: ignore # "str" has no attribute "json"
|
||||
exported_result if not self.output_pydantic else exported_result.json()
|
||||
exported_result
|
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if not self.output_pydantic
|
||||
else exported_result.model_dump_json()
|
||||
)
|
||||
self._save_file(content)
|
||||
|
||||
|
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@@ -8,18 +8,18 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
|
||||
|
||||
|
||||
class TokenCalcHandler(BaseCallbackHandler):
|
||||
model: str = ""
|
||||
model_name: str = ""
|
||||
token_cost_process: TokenProcess
|
||||
|
||||
def __init__(self, model, token_cost_process):
|
||||
self.model = model
|
||||
def __init__(self, model_name, token_cost_process):
|
||||
self.model_name = model_name
|
||||
self.token_cost_process = token_cost_process
|
||||
|
||||
def on_llm_start(
|
||||
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
|
||||
) -> None:
|
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try:
|
||||
encoding = tiktoken.encoding_for_model(self.model)
|
||||
encoding = tiktoken.encoding_for_model(self.model_name)
|
||||
except KeyError:
|
||||
encoding = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
|
||||
@@ -12,7 +12,6 @@ from crewai import Agent, Crew, Task
|
||||
from crewai.agents.cache import CacheHandler
|
||||
from crewai.agents.executor import CrewAgentExecutor
|
||||
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|
||||
|
||||
from crewai.tools.tool_calling import InstructorToolCalling
|
||||
from crewai.tools.tool_usage import ToolUsage
|
||||
from crewai.utilities import RPMController
|
||||
@@ -734,7 +733,7 @@ def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
|
||||
|
||||
assert len(agent1.llm.callbacks) == 1
|
||||
assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
|
||||
assert agent1.llm.callbacks[0].model == "gpt-4o"
|
||||
assert agent1.llm.callbacks[0].model_name == "gpt-4o"
|
||||
assert (
|
||||
agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
|
||||
)
|
||||
|
||||
585
tests/cassettes/test_crew_async_kickoff_for_each_full_ouput.yaml
Normal file
585
tests/cassettes/test_crew_async_kickoff_for_each_full_ouput.yaml
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give"},"logprobs":null,"finish_reason":null}]}
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a"},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
|
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great"},"logprobs":null,"finish_reason":null}]}
|
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|
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|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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answer"},"logprobs":null,"finish_reason":null}]}
|
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|
||||
|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":".\n"},"logprobs":null,"finish_reason":null}]}
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|
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Answer"},"logprobs":null,"finish_reason":null}]}
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|
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Apple"},"logprobs":null,"finish_reason":null}]}
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|
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|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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technology"},"logprobs":null,"finish_reason":null}]}
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with"},"logprobs":null,"finish_reason":null}]}
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the"},"logprobs":null,"finish_reason":null}]}
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|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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i"},"logprobs":null,"finish_reason":null}]}
|
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|
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|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"Phone"},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":","},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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communication"},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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and"},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"
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consumption"},"logprobs":null,"finish_reason":null}]}
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|
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{"content":"."},"logprobs":null,"finish_reason":null}]}
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data: {"id":"chatcmpl-9dIQ14W5gaHA4p3dYi6n7FrRBnzhN","object":"chat.completion.chunk","created":1719152621,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_5e6c71d4a8","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
|
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|
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|
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data: [DONE]
|
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|
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|
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'
|
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headers:
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CF-Cache-Status:
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- DYNAMIC
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CF-RAY:
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- 8985232a4e71453b-ATL
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Connection:
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- keep-alive
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Content-Type:
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- text/event-stream; charset=utf-8
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Date:
|
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- Sun, 23 Jun 2024 14:23:41 GMT
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Server:
|
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- cloudflare
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Transfer-Encoding:
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- chunked
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alt-svc:
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openai-organization:
|
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- crewai-iuxna1
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openai-processing-ms:
|
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- '108'
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openai-version:
|
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- '2020-10-01'
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strict-transport-security:
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x-ratelimit-limit-requests:
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x-ratelimit-remaining-requests:
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x-ratelimit-remaining-tokens:
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x-ratelimit-reset-requests:
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x-ratelimit-reset-tokens:
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status:
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code: 200
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message: OK
|
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version: 1
|
||||
@@ -360,7 +360,7 @@ def test_api_calls_throttling(capsys):
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_crew_full_ouput():
|
||||
def test_crew_full_output():
|
||||
agent = Agent(
|
||||
role="test role",
|
||||
goal="test goal",
|
||||
@@ -388,14 +388,94 @@ def test_crew_full_ouput():
|
||||
"final_output": "Hello!",
|
||||
"tasks_outputs": [task1.output, task2.output],
|
||||
"usage_metrics": {
|
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"total_tokens": 517,
|
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"prompt_tokens": 466,
|
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"completion_tokens": 51,
|
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"successful_requests": 3,
|
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"total_tokens": 348,
|
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"prompt_tokens": 314,
|
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"completion_tokens": 34,
|
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"successful_requests": 2,
|
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},
|
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}
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_crew_kickoff_for_each_full_ouput():
|
||||
inputs = [
|
||||
{"topic": "dog"},
|
||||
{"topic": "cat"},
|
||||
{"topic": "apple"},
|
||||
]
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task], full_output=True)
|
||||
results = crew.kickoff_for_each(inputs=inputs)
|
||||
|
||||
assert len(results) == len(inputs)
|
||||
for result in results:
|
||||
assert "usage_metrics" in result
|
||||
assert isinstance(result["usage_metrics"], dict)
|
||||
|
||||
# Assert that all required keys are in usage_metrics and their values are not None
|
||||
for key in [
|
||||
"total_tokens",
|
||||
"prompt_tokens",
|
||||
"completion_tokens",
|
||||
"successful_requests",
|
||||
]:
|
||||
assert key in result["usage_metrics"]
|
||||
assert result["usage_metrics"][key] > 0
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.mark.asyncio
|
||||
async def test_crew_async_kickoff_for_each_full_ouput():
|
||||
inputs = [
|
||||
{"topic": "dog"},
|
||||
{"topic": "cat"},
|
||||
{"topic": "apple"},
|
||||
]
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task], full_output=True)
|
||||
results = await crew.kickoff_for_each_async(inputs=inputs)
|
||||
|
||||
assert len(results) == len(inputs)
|
||||
for result in results:
|
||||
assert "usage_metrics" in result
|
||||
assert isinstance(result["usage_metrics"], dict)
|
||||
|
||||
# Assert that all required keys are in usage_metrics and their values are not None
|
||||
for key in [
|
||||
"total_tokens",
|
||||
"prompt_tokens",
|
||||
"completion_tokens",
|
||||
"successful_requests",
|
||||
]:
|
||||
assert key in result["usage_metrics"]
|
||||
# TODO: FIX THIS WHEN USAGE METRICS ARE RE-DONE
|
||||
# assert result["usage_metrics"][key] > 0
|
||||
|
||||
|
||||
def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
|
||||
agent = Agent(
|
||||
role="test role",
|
||||
@@ -468,6 +548,262 @@ def test_async_task_execution():
|
||||
join.assert_called()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_kickoff_for_each_single_input():
|
||||
"""Tests if kickoff_for_each works with a single input."""
|
||||
from unittest.mock import patch
|
||||
|
||||
inputs = [{"topic": "dog"}]
|
||||
expected_outputs = ["Dogs are loyal companions and popular pets."]
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
with patch.object(Agent, "execute_task") as mock_execute_task:
|
||||
mock_execute_task.side_effect = expected_outputs
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
results = crew.kickoff_for_each(inputs=inputs)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results == expected_outputs
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_kickoff_for_each_multiple_inputs():
|
||||
"""Tests if kickoff_for_each works with multiple inputs."""
|
||||
from unittest.mock import patch
|
||||
|
||||
inputs = [
|
||||
{"topic": "dog"},
|
||||
{"topic": "cat"},
|
||||
{"topic": "apple"},
|
||||
]
|
||||
expected_outputs = [
|
||||
"Dogs are loyal companions and popular pets.",
|
||||
"Cats are independent and low-maintenance pets.",
|
||||
"Apples are a rich source of dietary fiber and vitamin C.",
|
||||
]
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
with patch.object(Agent, "execute_task") as mock_execute_task:
|
||||
mock_execute_task.side_effect = expected_outputs
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
results = crew.kickoff_for_each(inputs=inputs)
|
||||
|
||||
assert len(results) == len(inputs)
|
||||
for i, res in enumerate(results):
|
||||
assert res == expected_outputs[i]
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_kickoff_for_each_empty_input():
|
||||
"""Tests if kickoff_for_each handles an empty input list."""
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
results = crew.kickoff_for_each(inputs=[])
|
||||
assert results == []
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_kickoff_for_each_invalid_input():
|
||||
"""Tests if kickoff_for_each raises TypeError for invalid input types."""
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
# Pass a string instead of a list
|
||||
crew.kickoff_for_each("invalid input")
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_kickoff_for_each_error_handling():
|
||||
"""Tests error handling in kickoff_for_each when kickoff raises an error."""
|
||||
from unittest.mock import patch
|
||||
|
||||
inputs = [
|
||||
{"topic": "dog"},
|
||||
{"topic": "cat"},
|
||||
{"topic": "apple"},
|
||||
]
|
||||
expected_outputs = [
|
||||
"Dogs are loyal companions and popular pets.",
|
||||
"Cats are independent and low-maintenance pets.",
|
||||
"Apples are a rich source of dietary fiber and vitamin C.",
|
||||
]
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
|
||||
with patch.object(Crew, "kickoff") as mock_kickoff:
|
||||
mock_kickoff.side_effect = expected_outputs[:2] + [
|
||||
Exception("Simulated kickoff error")
|
||||
]
|
||||
with pytest.raises(Exception, match="Simulated kickoff error"):
|
||||
crew.kickoff_for_each(inputs=inputs)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.mark.asyncio
|
||||
async def test_kickoff_async_basic_functionality_and_output():
|
||||
"""Tests the basic functionality and output of kickoff_async."""
|
||||
from unittest.mock import patch
|
||||
|
||||
inputs = {"topic": "dog"}
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
# Create the crew
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
|
||||
expected_output = "This is a sample output from kickoff."
|
||||
with patch.object(Crew, "kickoff", return_value=expected_output) as mock_kickoff:
|
||||
result = await crew.kickoff_async(inputs)
|
||||
|
||||
assert isinstance(result, str), "Result should be a string"
|
||||
assert result == expected_output, "Result should match expected output"
|
||||
mock_kickoff.assert_called_once_with(inputs)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_kickoff_for_each_async_basic_functionality_and_output():
|
||||
"""Tests the basic functionality and output of akickoff_for_each_async."""
|
||||
from unittest.mock import patch
|
||||
|
||||
inputs = [
|
||||
{"topic": "dog"},
|
||||
{"topic": "cat"},
|
||||
{"topic": "apple"},
|
||||
]
|
||||
|
||||
# Define expected outputs for each input
|
||||
expected_outputs = [
|
||||
"Dogs are loyal companions and popular pets.",
|
||||
"Cats are independent and low-maintenance pets.",
|
||||
"Apples are a rich source of dietary fiber and vitamin C.",
|
||||
]
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
with patch.object(
|
||||
Crew, "kickoff_async", side_effect=expected_outputs
|
||||
) as mock_kickoff_async:
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
|
||||
results = await crew.kickoff_for_each_async(inputs)
|
||||
|
||||
assert len(results) == len(inputs)
|
||||
assert results == expected_outputs
|
||||
for input_data in inputs:
|
||||
mock_kickoff_async.assert_any_call(inputs=input_data)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_kickoff_for_each_async_empty_input():
|
||||
"""Tests if akickoff_for_each_async handles an empty input list."""
|
||||
|
||||
agent = Agent(
|
||||
role="{topic} Researcher",
|
||||
goal="Express hot takes on {topic}.",
|
||||
backstory="You have a lot of experience with {topic}.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me an analysis around {topic}.",
|
||||
expected_output="1 bullet point about {topic} that's under 15 words.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
# Create the crew
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
|
||||
# Call the function we are testing
|
||||
results = await crew.kickoff_for_each_async([])
|
||||
|
||||
# Assertion
|
||||
assert results == [], "Result should be an empty list when input is empty"
|
||||
|
||||
|
||||
def test_set_agents_step_callback():
|
||||
from unittest.mock import patch
|
||||
|
||||
@@ -715,10 +1051,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
|
||||
assert result == '"Howdy!"'
|
||||
|
||||
assert crew.usage_metrics == {
|
||||
"total_tokens": 1616,
|
||||
"prompt_tokens": 1333,
|
||||
"completion_tokens": 283,
|
||||
"successful_requests": 3,
|
||||
"total_tokens": 1927,
|
||||
"prompt_tokens": 1557,
|
||||
"completion_tokens": 370,
|
||||
"successful_requests": 4,
|
||||
}
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user