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| import time | |
| from typing import Any, Dict, Iterator, List, Optional, Tuple, Union | |
| from langchain.agents import AgentExecutor | |
| from langchain.agents.agent import ExceptionTool | |
| from langchain.agents.tools import InvalidTool | |
| from langchain.callbacks.manager import CallbackManagerForChainRun | |
| from langchain_core.agents import AgentAction, AgentFinish, AgentStep | |
| from langchain_core.exceptions import OutputParserException | |
| from langchain_core.pydantic_v1 import root_validator | |
| from langchain_core.tools import BaseTool | |
| from langchain_core.utils.input import get_color_mapping | |
| from crewai.agents.cache.cache_hit import CacheHit | |
| from crewai.tools.cache_tools import CacheTools | |
| from crewai.utilities import I18N | |
| class CrewAgentExecutor(AgentExecutor): | |
| i18n: I18N = I18N() | |
| iterations: int = 0 | |
| request_within_rpm_limit: Any = None | |
| max_iterations: Optional[int] = 15 | |
| force_answer_max_iterations: Optional[int] = None | |
| def set_force_answer_max_iterations(cls, values: Dict) -> Dict: | |
| values["force_answer_max_iterations"] = values["max_iterations"] - 2 | |
| return values | |
| def _should_force_answer(self) -> bool: | |
| return True if self.iterations == self.force_answer_max_iterations else False | |
| def _force_answer(self, output: AgentAction): | |
| return AgentStep( | |
| action=output, observation=self.i18n.errors("force_final_answer") | |
| ) | |
| def _call( | |
| self, | |
| inputs: Dict[str, str], | |
| run_manager: Optional[CallbackManagerForChainRun] = None, | |
| ) -> Dict[str, Any]: | |
| """Run text through and get agent response.""" | |
| # Construct a mapping of tool name to tool for easy lookup | |
| name_to_tool_map = {tool.name: tool for tool in self.tools} | |
| # We construct a mapping from each tool to a color, used for logging. | |
| color_mapping = get_color_mapping( | |
| [tool.name for tool in self.tools], excluded_colors=["green", "red"] | |
| ) | |
| intermediate_steps: List[Tuple[AgentAction, str]] = [] | |
| # Let's start tracking the number of iterations and time elapsed | |
| self.iterations = 0 | |
| time_elapsed = 0.0 | |
| start_time = time.time() | |
| # We now enter the agent loop (until it returns something). | |
| while self._should_continue(self.iterations, time_elapsed): | |
| if not self.request_within_rpm_limit or self.request_within_rpm_limit(): | |
| next_step_output = self._take_next_step( | |
| name_to_tool_map, | |
| color_mapping, | |
| inputs, | |
| intermediate_steps, | |
| run_manager=run_manager, | |
| ) | |
| if isinstance(next_step_output, AgentFinish): | |
| return self._return( | |
| next_step_output, intermediate_steps, run_manager=run_manager | |
| ) | |
| intermediate_steps.extend(next_step_output) | |
| if len(next_step_output) == 1: | |
| next_step_action = next_step_output[0] | |
| # See if tool should return directly | |
| tool_return = self._get_tool_return(next_step_action) | |
| if tool_return is not None: | |
| return self._return( | |
| tool_return, intermediate_steps, run_manager=run_manager | |
| ) | |
| self.iterations += 1 | |
| time_elapsed = time.time() - start_time | |
| output = self.agent.return_stopped_response( | |
| self.early_stopping_method, intermediate_steps, **inputs | |
| ) | |
| return self._return(output, intermediate_steps, run_manager=run_manager) | |
| def _iter_next_step( | |
| self, | |
| name_to_tool_map: Dict[str, BaseTool], | |
| color_mapping: Dict[str, str], | |
| inputs: Dict[str, str], | |
| intermediate_steps: List[Tuple[AgentAction, str]], | |
| run_manager: Optional[CallbackManagerForChainRun] = None, | |
| ) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]: | |
| """Take a single step in the thought-action-observation loop. | |
| Override this to take control of how the agent makes and acts on choices. | |
| """ | |
| try: | |
| intermediate_steps = self._prepare_intermediate_steps(intermediate_steps) | |
| # Call the LLM to see what to do. | |
| output = self.agent.plan( | |
| intermediate_steps, | |
| callbacks=run_manager.get_child() if run_manager else None, | |
| **inputs, | |
| ) | |
| if self._should_force_answer(): | |
| if isinstance(output, AgentAction) or isinstance(output, AgentFinish): | |
| output = output | |
| elif isinstance(output, CacheHit): | |
| output = output.action | |
| else: | |
| raise ValueError( | |
| f"Unexpected output type from agent: {type(output)}" | |
| ) | |
| yield self._force_answer(output) | |
| return | |
| except OutputParserException as e: | |
| if isinstance(self.handle_parsing_errors, bool): | |
| raise_error = not self.handle_parsing_errors | |
| else: | |
| raise_error = False | |
| if raise_error: | |
| raise ValueError( | |
| "An output parsing error occurred. " | |
| "In order to pass this error back to the agent and have it try " | |
| "again, pass `handle_parsing_errors=True` to the AgentExecutor. " | |
| f"This is the error: {str(e)}" | |
| ) | |
| text = str(e) | |
| if isinstance(self.handle_parsing_errors, bool): | |
| if e.send_to_llm: | |
| observation = str(e.observation) | |
| text = str(e.llm_output) | |
| else: | |
| observation = "Invalid or incomplete response" | |
| elif isinstance(self.handle_parsing_errors, str): | |
| observation = self.handle_parsing_errors | |
| elif callable(self.handle_parsing_errors): | |
| observation = self.handle_parsing_errors(e) | |
| else: | |
| raise ValueError("Got unexpected type of `handle_parsing_errors`") | |
| output = AgentAction("_Exception", observation, text) | |
| if run_manager: | |
| run_manager.on_agent_action(output, color="green") | |
| tool_run_kwargs = self.agent.tool_run_logging_kwargs() | |
| observation = ExceptionTool().run( | |
| output.tool_input, | |
| verbose=self.verbose, | |
| color=None, | |
| callbacks=run_manager.get_child() if run_manager else None, | |
| **tool_run_kwargs, | |
| ) | |
| if self._should_force_answer(): | |
| yield self._force_answer(output) | |
| return | |
| yield AgentStep(action=output, observation=observation) | |
| return | |
| # If the tool chosen is the finishing tool, then we end and return. | |
| if isinstance(output, AgentFinish): | |
| yield output | |
| return | |
| # Override tool usage to use CacheTools | |
| if isinstance(output, CacheHit): | |
| cache = output.cache | |
| action = output.action | |
| tool = CacheTools(cache_handler=cache).tool() | |
| output = action.copy() | |
| output.tool_input = f"tool:{action.tool}|input:{action.tool_input}" | |
| output.tool = tool.name | |
| name_to_tool_map[tool.name] = tool | |
| color_mapping[tool.name] = color_mapping[action.tool] | |
| actions: List[AgentAction] | |
| actions = [output] if isinstance(output, AgentAction) else output | |
| yield from actions | |
| for agent_action in actions: | |
| if run_manager: | |
| run_manager.on_agent_action(agent_action, color="green") | |
| # Otherwise we lookup the tool | |
| if agent_action.tool in name_to_tool_map: | |
| tool = name_to_tool_map[agent_action.tool] | |
| return_direct = tool.return_direct | |
| color = color_mapping[agent_action.tool] | |
| tool_run_kwargs = self.agent.tool_run_logging_kwargs() | |
| if return_direct: | |
| tool_run_kwargs["llm_prefix"] = "" | |
| # We then call the tool on the tool input to get an observation | |
| observation = tool.run( | |
| agent_action.tool_input, | |
| verbose=self.verbose, | |
| color=color, | |
| callbacks=run_manager.get_child() if run_manager else None, | |
| **tool_run_kwargs, | |
| ) | |
| else: | |
| tool_run_kwargs = self.agent.tool_run_logging_kwargs() | |
| observation = InvalidTool().run( | |
| { | |
| "requested_tool_name": agent_action.tool, | |
| "available_tool_names": list(name_to_tool_map.keys()), | |
| }, | |
| verbose=self.verbose, | |
| color=None, | |
| callbacks=run_manager.get_child() if run_manager else None, | |
| **tool_run_kwargs, | |
| ) | |
| yield AgentStep(action=agent_action, observation=observation) | |