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feat: Add Train feature for Crews (#686)

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* feat: add training logic to agent and crew

* feat: add training logic to agent executor

* feat: add input parameter  to cli command

* feat: add utilities for the training logic

* feat: polish code, logic and add private variables

* feat: add docstring and type hinting to executor

* feat: add constant file, add constant to code

* feat: fix name of training handler function

* feat: remove unused var

* feat: change file handler file name

* feat: Add training handler file, class and change on the code

* feat: fix name error from file

* fix: change import to adapt to logic

* feat: add training handler test

* feat: add tests for file and training_handler

* feat: add test for task evaluator function

* feat: change text to fit in-screen

* feat: add test for train function

* feat: add test for agent training_handler function

* feat: add test for agent._use_trained_data
This commit is contained in:
Eduardo Chiarotti
2024-06-27 02:22:34 -03:00
committed by GitHub
parent 9e61b8325b
commit 175d5b3dd6
15 changed files with 564 additions and 45 deletions
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39
src/crewai/agents/executor.py
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@@ -18,8 +18,10 @@ from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities.training_handler import CrewTrainingHandler
class CrewAgentExecutor(AgentExecutor):
@@ -246,12 +248,17 @@ class CrewAgentExecutor(AgentExecutor):
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
if self.should_ask_for_human_input:
human_feedback = self._ask_human_input(output.return_values["output"])
if self.crew._train:
self._handle_crew_training_output(output, human_feedback)
# Making sure we only ask for it once, so disabling for the next thought loop
self.should_ask_for_human_input = False
human_feedback = self._ask_human_input(output.return_values["output"])
action = AgentAction(
tool="Human Input", tool_input=human_feedback, log=output.log
)
yield AgentStep(
action=action,
observation=self._i18n.slice("human_feedback").format(
@@ -261,6 +268,9 @@ class CrewAgentExecutor(AgentExecutor):
return
else:
if self.crew._train:
self._handle_crew_training_output(output)
yield output
return
@@ -305,3 +315,30 @@ class CrewAgentExecutor(AgentExecutor):
return input(
self._i18n.slice("getting_input").format(final_answer=final_answer)
)
def _handle_crew_training_output(
self, output: AgentFinish, human_feedback: str | None = None
) -> None:
"""Function to handle the process of the training data."""
agent_id = str(self.crew_agent.id)
if (
training_data := CrewTrainingHandler(TRAINING_DATA_FILE).load()
and not self.should_ask_for_human_input
):
if training_data.get(agent_id):
training_data[agent_id][self.crew._train_iteration][
"improved_output"
] = output.return_values["output"]
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
if self.should_ask_for_human_input and human_feedback is not None:
training_data = {
"initial_output": output.return_values["output"],
"human_feedback": human_feedback,
"agent": agent_id,
"agent_role": self.crew_agent.role,
}
CrewTrainingHandler(TRAINING_DATA_FILE).append(
self.crew._train_iteration, agent_id, training_data
)