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refactor and more clear messages

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This commit is contained in:
Brandon Hancock
2024-12-04 14:17:44 -05:00
parent 78d49657ff
commit aa8610dd79
2 changed files with 100 additions and 92 deletions
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12
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -3,16 +3,15 @@ from typing import TYPE_CHECKING, Optional
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.utilities import I18N
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
from crewai.utilities.printer import Printer
if TYPE_CHECKING:
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.crew import Crew
from crewai.task import Task
from crewai.agents.agent_builder.base_agent import BaseAgent
class CrewAgentExecutorMixin:
@@ -107,7 +106,12 @@ class CrewAgentExecutorMixin:
)
self._printer.print(
content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
content=(
"\n\n=====\n"
"## Please provide feedback on the Final Result and the Agent's actions. "
"Respond with 'looks good' or a similar phrase when you're satisfied.\n"
"=====\n"
),
color="bold_yellow",
)
return input()

180
src/crewai/agents/crew_agent_executor.py
View File

@@ -90,7 +90,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if "system" in self.prompt:
system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
self.messages.append(self._format_msg(system_prompt, role="system"))
self.messages.append(self._format_msg(user_prompt))
else:
@@ -102,75 +101,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
formatted_answer = self._invoke_loop()
while self.ask_for_human_input:
human_feedback = self._ask_human_input(formatted_answer.output)
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer, human_feedback)
# Make an LLM call to verify if additional changes are requested based on human feedback
additional_changes_prompt = self._i18n.slice(
"human_feedback_classification"
).format(feedback=human_feedback)
retry_count = 0
llm_call_successful = False
additional_changes_response = None
while retry_count < MAX_LLM_RETRY and not llm_call_successful:
try:
additional_changes_response = (
self.llm.call(
[
self._format_msg(
additional_changes_prompt, role="system"
)
],
callbacks=self.callbacks,
)
.strip()
.lower()
)
llm_call_successful = True
except Exception as e:
retry_count += 1
# Log the error and retry
self._logger.log(
"error",
f"Error during LLM call: {e}. Retrying... ({retry_count}/{max_retries})",
color="red",
)
if not llm_call_successful:
# After max retries, log error and move on
self._logger.log(
"error",
"Error processing feedback after multiple attempts.",
color="red",
)
self.ask_for_human_input = False
break # Exit the feedback loop
# Handle unexpected responses
if additional_changes_response == "false":
self.ask_for_human_input = False
elif additional_changes_response == "true":
self.ask_for_human_input = True
# Add human feedback to messages
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
# Invoke the loop again with updated messages
formatted_answer = self._invoke_loop()
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer)
else:
# Unexpected response
self._logger.log(
"error",
f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
color="red",
)
self.ask_for_human_input = False
if self.ask_for_human_input:
formatted_answer = self._handle_human_feedback(formatted_answer)
self._create_short_term_memory(formatted_answer)
self._create_long_term_memory(formatted_answer)
@@ -384,16 +316,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _handle_context_length(self) -> None:
if self.respect_context_window:
self._logger.log(
"debug",
"Context length exceeded. Summarizing content to fit the model context window.",
self._printer.print(
content="Context length exceeded. Summarizing content to fit the model context window.",
color="yellow",
)
self._summarize_messages()
else:
self._logger.log(
"debug",
"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
self._printer.print(
content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
color="red",
)
raise SystemExit(
@@ -420,15 +350,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
] = result.output
training_handler.save(training_data)
else:
self._logger.log(
"error",
"Invalid train iteration type or agent_id not in training data.",
self._printer.print(
content="Invalid train iteration type or agent_id not in training data.",
color="red",
)
else:
self._logger.log(
"error",
"Crew is None or does not have _train_iteration attribute.",
self._printer.print(
content="Crew is None or does not have _train_iteration attribute.",
color="red",
)
@@ -446,15 +374,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
train_iteration, agent_id, training_data
)
else:
self._logger.log(
"error",
"Invalid train iteration type. Expected int.",
self._printer.print(
content="Invalid train iteration type. Expected int.",
color="red",
)
else:
self._logger.log(
"error",
"Crew is None or does not have _train_iteration attribute.",
self._printer.print(
content="Crew is None or does not have _train_iteration attribute.",
color="red",
)
@@ -470,3 +396,81 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
prompt = prompt.rstrip()
return {"role": role, "content": prompt}
def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
"""
Handles the human feedback loop, allowing the user to provide feedback
on the agent's output and determining if additional iterations are needed.
Parameters:
formatted_answer (AgentFinish): The initial output from the agent.
Returns:
AgentFinish: The final output after incorporating human feedback.
"""
while self.ask_for_human_input:
human_feedback = self._ask_human_input(formatted_answer.output)
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer, human_feedback)
# Make an LLM call to verify if additional changes are requested based on human feedback
additional_changes_prompt = self._i18n.slice(
"human_feedback_classification"
).format(feedback=human_feedback)
retry_count = 0
llm_call_successful = False
additional_changes_response = None
while retry_count < MAX_LLM_RETRY and not llm_call_successful:
try:
additional_changes_response = (
self.llm.call(
[
self._format_msg(
additional_changes_prompt, role="system"
)
],
callbacks=self.callbacks,
)
.strip()
.lower()
)
llm_call_successful = True
except Exception as e:
retry_count += 1
self._printer.print(
content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
color="red",
)
if not llm_call_successful:
self._printer.print(
content="Error processing feedback after multiple attempts.",
color="red",
)
self.ask_for_human_input = False
break
if additional_changes_response == "false":
self.ask_for_human_input = False
elif additional_changes_response == "true":
self.ask_for_human_input = True
# Add human feedback to messages
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
# Invoke the loop again with updated messages
formatted_answer = self._invoke_loop()
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer)
else:
# Unexpected response
self._printer.print(
content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
color="red",
)
self.ask_for_human_input = False
return formatted_answer