Fixes issues with result as answer not properly exiting LLM loop (#1689)

* v1 of fix implemented. Need to confirm with tokens.

* remove print statements
This commit is contained in:
Brandon Hancock (bhancock_ai)
2024-12-02 13:38:17 -05:00
committed by GitHub
parent aa532acdd2
commit 4d06708938
4 changed files with 44 additions and 15 deletions

View File

@@ -1,5 +1,6 @@
import json
import re
from dataclasses import dataclass
from typing import Any, Dict, List, Union
from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -12,6 +13,7 @@ from crewai.agents.parser import (
OutputParserException,
)
from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.base_tool import BaseTool
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N, Printer
from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -22,6 +24,12 @@ from crewai.utilities.logger import Logger
from crewai.utilities.training_handler import CrewTrainingHandler
@dataclass
class ToolResult:
result: Any
result_as_answer: bool
class CrewAgentExecutor(CrewAgentExecutorMixin):
_logger: Logger = Logger()
@@ -33,7 +41,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
agent: BaseAgent,
prompt: dict[str, str],
max_iter: int,
tools: List[Any],
tools: List[BaseTool],
tools_names: str,
stop_words: List[str],
tools_description: str,
@@ -70,7 +78,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.iterations = 0
self.log_error_after = 3
self.have_forced_answer = False
self.name_to_tool_map = {tool.name: tool for tool in self.tools}
self.tool_name_to_tool_map: Dict[str, BaseTool] = {
tool.name: tool for tool in self.tools
}
if self.llm.stop:
self.llm.stop = list(set(self.llm.stop + self.stop))
else:
@@ -140,9 +150,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
formatted_answer = self._format_answer(answer)
if isinstance(formatted_answer, AgentAction):
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
formatted_answer.result = action_result
tool_result = self._execute_tool_and_check_finality(
formatted_answer
)
formatted_answer.text += f"\nObservation: {tool_result.result}"
formatted_answer.result = tool_result.result
if tool_result.result_as_answer:
return AgentFinish(
thought="",
output=tool_result.result,
text=formatted_answer.text,
)
self._show_logs(formatted_answer)
if self.step_callback:
@@ -239,7 +257,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
)
def _use_tool(self, agent_action: AgentAction) -> Any:
def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
tool_usage = ToolUsage(
tools_handler=self.tools_handler,
tools=self.tools,
@@ -255,19 +273,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if isinstance(tool_calling, ToolUsageErrorException):
tool_result = tool_calling.message
return ToolResult(result=tool_result, result_as_answer=False)
else:
if tool_calling.tool_name.casefold().strip() in [
name.casefold().strip() for name in self.name_to_tool_map
name.casefold().strip() for name in self.tool_name_to_tool_map
] or tool_calling.tool_name.casefold().replace("_", " ") in [
name.casefold().strip() for name in self.name_to_tool_map
name.casefold().strip() for name in self.tool_name_to_tool_map
]:
tool_result = tool_usage.use(tool_calling, agent_action.text)
tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
if tool:
return ToolResult(
result=tool_result, result_as_answer=tool.result_as_answer
)
else:
tool_result = self._i18n.errors("wrong_tool_name").format(
tool=tool_calling.tool_name,
tools=", ".join([tool.name.casefold() for tool in self.tools]),
)
return tool_result
return ToolResult(result=tool_result, result_as_answer=False)
def _summarize_messages(self) -> None:
messages_groups = []
@@ -333,9 +357,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if self.crew is not None and hasattr(self.crew, "_train_iteration"):
train_iteration = self.crew._train_iteration
if agent_id in training_data and isinstance(train_iteration, int):
training_data[agent_id][train_iteration]["improved_output"] = (
result.output
)
training_data[agent_id][train_iteration][
"improved_output"
] = result.output
training_handler.save(training_data)
else:
self._logger.log(