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0f715d17fb..8b3db9d85f 100644 --- a/src/crewai/agents/crew_agent_executor.py +++ b/src/crewai/agents/crew_agent_executor.py @@ -70,7 +70,10 @@ def __init__( self.log_error_after = 3 self.have_forced_answer = False self.name_to_tool_map = {tool.name: tool for tool in self.tools} - self.llm.stop = self.stop + if self.llm.stop: + self.llm.stop = list(set(self.llm.stop + self.stop)) + else: + self.llm.stop = self.stop def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]: if "system" in self.prompt: diff --git a/src/crewai/project/annotations.py b/src/crewai/project/annotations.py index d315edace1..e95909827f 100644 --- a/src/crewai/project/annotations.py +++ b/src/crewai/project/annotations.py @@ -1,6 +1,7 @@ from functools import wraps from crewai.project.utils import memoize +from crewai import Crew def task(func): @@ -72,7 +73,7 @@ def pipeline(func): return memoize(func) -def crew(func): +def crew(func) -> "Crew": def wrapper(self, *args, **kwargs): instantiated_tasks = [] instantiated_agents = [] diff --git a/src/crewai/project/crew_base.py b/src/crewai/project/crew_base.py index 1e807a2ab2..2ecf50e13d 100644 --- a/src/crewai/project/crew_base.py +++ b/src/crewai/project/crew_base.py @@ -5,11 +5,8 @@ import yaml from dotenv import load_dotenv -from crewai.crew import Crew - load_dotenv() - T = TypeVar("T", bound=Type[Any]) @@ -37,19 +34,6 @@ def __init__(self, *args, **kwargs): self.map_all_agent_variables() self.map_all_task_variables() - def crew(self) -> "Crew": - agents = [ - getattr(self, name)() - for name, func in self._get_all_functions().items() - if hasattr(func, "is_agent") - ] - tasks = [ - getattr(self, name)() - for name, func in reversed(self._get_all_functions().items()) - if hasattr(func, "is_task") - ] - return Crew(agents=agents, tasks=tasks) - @staticmethod def load_yaml(config_path: Path): try: diff --git a/tests/agent_test.py b/tests/agent_test.py index 4a5a58ce5f..907a6e30c7 100644 --- a/tests/agent_test.py +++ b/tests/agent_test.py @@ -1093,7 +1093,7 @@ def test_agent_training_handler(crew_training_handler): result = agent._training_handler(task_prompt=task_prompt) - assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good" + assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good" crew_training_handler.assert_has_calls( [mock.call(), mock.call("training_data.pkl"), mock.call().load()] @@ -1121,8 +1121,8 @@ def test_agent_use_trained_data(crew_training_handler): result = agent._use_trained_data(task_prompt=task_prompt) assert ( - result == "What is 1 + 1?You MUST follow these feedbacks: \n " - "The result of the math operation must be right.\n - Result must be better than 1." + result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n" + " - The result of the math operation must be right.\n - Result must be better than 1." ) crew_training_handler.assert_has_calls( [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()] @@ -1205,7 +1205,7 @@ def test_agent_with_custom_stop_words(): ) assert isinstance(agent.llm, LLM) - assert agent.llm.stop == stop_words + assert agent.llm.stop == stop_words + ["\nObservation:"] def test_agent_with_callbacks(): @@ -1368,7 +1368,7 @@ def test_agent_with_all_llm_attributes(): assert agent.llm.temperature == 0.7 assert agent.llm.top_p == 0.9 assert agent.llm.n == 1 - assert agent.llm.stop == ["STOP", "END"] + assert agent.llm.stop == ["STOP", "END", "\nObservation:"] assert agent.llm.max_tokens == 100 assert agent.llm.presence_penalty == 0.1 assert agent.llm.frequency_penalty == 0.1 diff --git a/tests/utilities/evaluators/test_task_evaluator.py b/tests/utilities/evaluators/test_task_evaluator.py index f76bca73bd..8a0be027a3 100644 --- a/tests/utilities/evaluators/test_task_evaluator.py +++ b/tests/utilities/evaluators/test_task_evaluator.py @@ -50,11 +50,12 @@ def test_evaluate_training_data(converter_mock): text="Assess the quality of the training data based on the llm output, human feedback , and llm " "output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback " "1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman " - "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- " - "Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs " - "provide action items based on human_feedback for future tasks\n- A score from 0 to 10 evaluating " - "on completion, quality, and overall performance from the improved output to the initial output " - "based on the human feedback\n", + "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- Provide " + "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's " + "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific " + "action items for future tasks. Ensure all key and specificpoints from the human feedback are " + "incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and " + "overall performance from the improved output to the initial output based on the human feedback\n", model=TrainingTaskEvaluation, instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the " "following structure, with the following keys:\n{\n suggestions: List[str],\n quality: float,\n final_summary: str\n}",