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4 Commits

Author SHA1 Message Date
Brandon Hancock
5cfafa7cf1 fix test 2024-10-22 21:28:08 -04:00
Brandon Hancock
468ebae5e6 fix last test 2024-10-22 21:13:36 -04:00
Brandon Hancock
5164e43317 make tests handle new copy functionality on train and test 2024-10-22 21:10:33 -04:00
Brandon Hancock
89258c2c72 use copy to split testing and training on crews 2024-10-22 18:14:39 -04:00
2 changed files with 45 additions and 68 deletions

View File

@@ -435,15 +435,16 @@ class Crew(BaseModel):
self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
) -> None:
"""Trains the crew for a given number of iterations."""
self._setup_for_training(filename)
train_crew = self.copy()
train_crew._setup_for_training(filename)
for n_iteration in range(n_iterations):
self._train_iteration = n_iteration
self.kickoff(inputs=inputs)
train_crew._train_iteration = n_iteration
train_crew.kickoff(inputs=inputs)
training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
for agent in self.agents:
for agent in train_crew.agents:
result = TaskEvaluator(agent).evaluate_training_data(
training_data=training_data, agent_id=str(agent.id)
)
@@ -987,17 +988,19 @@ class Crew(BaseModel):
inputs: Optional[Dict[str, Any]] = None,
) -> None:
"""Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
self._test_execution_span = self._telemetry.test_execution_span(
self,
test_crew = self.copy()
self._test_execution_span = test_crew._telemetry.test_execution_span(
test_crew,
n_iterations,
inputs,
openai_model_name, # type: ignore[arg-type]
) # type: ignore[arg-type]
evaluator = CrewEvaluator(self, openai_model_name) # type: ignore[arg-type]
evaluator = CrewEvaluator(test_crew, openai_model_name) # type: ignore[arg-type]
for i in range(1, n_iterations + 1):
evaluator.set_iteration(i)
self.kickoff(inputs=inputs)
test_crew.kickoff(inputs=inputs)
evaluator.print_crew_evaluation_result()

View File

@@ -9,6 +9,7 @@ from unittest.mock import MagicMock, patch
import instructor
import pydantic_core
import pytest
from crewai.agent import Agent
from crewai.agents.cache import CacheHandler
from crewai.crew import Crew
@@ -497,6 +498,7 @@ def test_cache_hitting_between_agents():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_api_calls_throttling(capsys):
from unittest.mock import patch
from crewai_tools import tool
@tool
@@ -779,11 +781,14 @@ def test_async_task_execution_call_count():
list_important_history.output = mock_task_output
write_article.output = mock_task_output
with patch.object(
Task, "execute_sync", return_value=mock_task_output
) as mock_execute_sync, patch.object(
Task, "execute_async", return_value=mock_future
) as mock_execute_async:
with (
patch.object(
Task, "execute_sync", return_value=mock_task_output
) as mock_execute_sync,
patch.object(
Task, "execute_async", return_value=mock_future
) as mock_execute_async,
):
crew.kickoff()
assert mock_execute_async.call_count == 2
@@ -1105,6 +1110,7 @@ def test_dont_set_agents_step_callback_if_already_set():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_crew_function_calling_llm():
from unittest.mock import patch
from crewai_tools import tool
llm = "gpt-4o"
@@ -1448,52 +1454,6 @@ def test_crew_does_not_interpolate_without_inputs():
interpolate_task_inputs.assert_not_called()
# def test_crew_partial_inputs():
# 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="{points} bullet points about {topic}.",
# )
# crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI"})
# inputs = {"topic": "AI"}
# crew._interpolate_inputs(inputs=inputs) # Manual call for now
# assert crew.tasks[0].description == "Give me an analysis around AI."
# assert crew.tasks[0].expected_output == "{points} bullet points about AI."
# assert crew.agents[0].role == "AI Researcher"
# assert crew.agents[0].goal == "Express hot takes on AI."
# assert crew.agents[0].backstory == "You have a lot of experience with AI."
# def test_crew_invalid_inputs():
# 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="{points} bullet points about {topic}.",
# )
# crew = Crew(agents=[agent], tasks=[task], inputs={"subject": "AI"})
# inputs = {"subject": "AI"}
# crew._interpolate_inputs(inputs=inputs) # Manual call for now
# assert crew.tasks[0].description == "Give me an analysis around {topic}."
# assert crew.tasks[0].expected_output == "{points} bullet points about {topic}."
# assert crew.agents[0].role == "{topic} Researcher"
# assert crew.agents[0].goal == "Express hot takes on {topic}."
# assert crew.agents[0].backstory == "You have a lot of experience with {topic}."
def test_task_callback_on_crew():
from unittest.mock import MagicMock, patch
@@ -1770,7 +1730,10 @@ def test_manager_agent_with_tools_raises_exception():
@patch("crewai.crew.Crew.kickoff")
@patch("crewai.crew.CrewTrainingHandler")
@patch("crewai.crew.TaskEvaluator")
def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
@patch("crewai.crew.Crew.copy")
def test_crew_train_success(
copy_mock, task_evaluator, crew_training_handler, kickoff_mock
):
task = Task(
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
expected_output="5 bullet points with a paragraph for each idea.",
@@ -1781,9 +1744,19 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
agents=[researcher, writer],
tasks=[task],
)
# Create a mock for the copied crew
copy_mock.return_value = crew
crew.train(
n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl"
)
# Ensure kickoff is called on the copied crew
kickoff_mock.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)
task_evaluator.assert_has_calls(
[
mock.call(researcher),
@@ -1822,10 +1795,6 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
]
)
kickoff.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)
def test_crew_train_error():
task = Task(
@@ -1840,7 +1809,7 @@ def test_crew_train_error():
)
with pytest.raises(TypeError) as e:
crew.train()
crew.train() # type: ignore purposefully throwing err
assert "train() missing 1 required positional argument: 'n_iterations'" in str(
e
)
@@ -2536,8 +2505,9 @@ def test_conditional_should_execute():
@mock.patch("crewai.crew.CrewEvaluator")
@mock.patch("crewai.crew.Crew.copy")
@mock.patch("crewai.crew.Crew.kickoff")
def test_crew_testing_function(mock_kickoff, crew_evaluator):
def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
task = Task(
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
expected_output="5 bullet points with a paragraph for each idea.",
@@ -2548,11 +2518,15 @@ def test_crew_testing_function(mock_kickoff, crew_evaluator):
agents=[researcher],
tasks=[task],
)
# Create a mock for the copied crew
copy_mock.return_value = crew
n_iterations = 2
crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
assert len(mock_kickoff.mock_calls) == n_iterations
mock_kickoff.assert_has_calls(
# Ensure kickoff is called on the copied crew
kickoff_mock.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)