from textwrap import dedent from typing import Any, List, Union from langchain_core.tools import BaseTool from langchain_openai import ChatOpenAI from crewai.agents.tools_handler import ToolsHandler from crewai.telemtry import Telemetry from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling from crewai.utilities import I18N, Converter, ConverterError, Printer OPENAI_BIGGER_MODELS = ["gpt-4"] class ToolUsageErrorException(Exception): """Exception raised for errors in the tool usage.""" def __init__(self, message: str) -> None: self.message = message super().__init__(self.message) class ToolUsage: """ Class that represents the usage of a tool by an agent. Attributes: task: Task being executed. tools_handler: Tools handler that will manage the tool usage. tools: List of tools available for the agent. tools_description: Description of the tools available for the agent. tools_names: Names of the tools available for the agent. llm: Language model to be used for the tool usage. function_calling_llm: Language model to be used for the tool usage. """ def __init__( self, tools_handler: ToolsHandler, tools: List[BaseTool], tools_description: str, tools_names: str, task: Any, llm: Any, function_calling_llm: Any, ) -> None: self._i18n: I18N = I18N() self._printer: Printer = Printer() self._telemetry: Telemetry = Telemetry() self._run_attempts: int = 1 self._max_parsing_attempts: int = 3 self._remeber_format_after_usages: int = 3 self.tools_description = tools_description self.tools_names = tools_names self.tools_handler = tools_handler self.tools = tools self.task = task self.llm = function_calling_llm or llm # Set the maximum parsing attempts for bigger models if (isinstance(self.llm, ChatOpenAI)) and (self.llm.openai_api_base == None): if self.llm.model_name in OPENAI_BIGGER_MODELS: self._max_parsing_attempts = 2 self._remeber_format_after_usages = 4 def parse(self, tool_string: str): """Parse the tool string and return the tool calling.""" return self._tool_calling(tool_string) def use( self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str ) -> str: if isinstance(calling, ToolUsageErrorException): error = calling.message self._printer.print(content=f"\n\n{error}\n", color="red") self.task.increment_tools_errors() return error try: tool = self._select_tool(calling.tool_name) except Exception as e: error = getattr(e, "message", str(e)) self.task.increment_tools_errors() self._printer.print(content=f"\n\n{error}\n", color="red") return error return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}\n\n{self._i18n.slice('final_answer_format')}" def _use( self, tool_string: str, tool: BaseTool, calling: Union[ToolCalling, InstructorToolCalling], ) -> None: if self._check_tool_repeated_usage(calling=calling): try: result = self._i18n.errors("task_repeated_usage").format( tool=calling.tool_name, tool_input=", ".join( [str(arg) for arg in calling.arguments.values()] ), ) self._printer.print(content=f"\n\n{result}\n", color="yellow") self._telemetry.tool_repeated_usage( llm=self.llm, tool_name=tool.name, attempts=self._run_attempts ) result = self._format_result(result=result) return result except Exception: self.task.increment_tools_errors() result = self.tools_handler.cache.read( tool=calling.tool_name, input=calling.arguments ) if not result: try: print(f"Calling tool: {calling.tool_name}") if calling.tool_name in [ "Delegate work to co-worker", "Ask question to co-worker", ]: self.task.increment_delegations() if calling.arguments: print(f"Calling tool NOW: {calling.tool_name}") result = tool._run(**calling.arguments) print("Got result back from tool") else: result = tool._run() except Exception as e: self._run_attempts += 1 if self._run_attempts > self._max_parsing_attempts: self._telemetry.tool_usage_error(llm=self.llm) error_message = self._i18n.errors("tool_usage_exception").format( error=e ) error = ToolUsageErrorException( f'\n{error_message}.\nMoving one then. {self._i18n.slice("format").format(tool_names=self.tools_names)}' ).message self.task.increment_tools_errors() self._printer.print(content=f"\n\n{error_message}\n", color="red") return error self.task.increment_tools_errors() return self.use(calling=calling, tool_string=tool_string) self.tools_handler.on_tool_use(calling=calling, output=result) self._printer.print(content=f"\n\n{result}\n", color="yellow") self._telemetry.tool_usage( llm=self.llm, tool_name=tool.name, attempts=self._run_attempts ) result = self._format_result(result=result) return result def _format_result(self, result: Any) -> None: self.task.used_tools += 1 if self._should_remember_format(): result = self._remember_format(result=result) return result def _should_remember_format(self) -> None: return self.task.used_tools % self._remeber_format_after_usages == 0 def _remember_format(self, result: str) -> None: result = str(result) result += "\n\n" + self._i18n.slice("tools").format( tools=self.tools_description, tool_names=self.tools_names ) return result def _check_tool_repeated_usage( self, calling: Union[ToolCalling, InstructorToolCalling] ) -> None: if last_tool_usage := self.tools_handler.last_used_tool: return (calling.tool_name == last_tool_usage.tool_name) and ( calling.arguments == last_tool_usage.arguments ) def _select_tool(self, tool_name: str) -> BaseTool: for tool in self.tools: if tool.name.lower().strip() == tool_name.lower().strip(): return tool self.task.increment_tools_errors() raise Exception(f"Tool '{tool_name}' not found.") def _render(self) -> str: """Render the tool name and description in plain text.""" descriptions = [] for tool in self.tools: args = { k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]} for k, v in tool.args.items() } descriptions.append( "\n".join( [ f"Tool Name: {tool.name.lower()}", f"Tool Description: {tool.description}", f"Tool Arguments: {args}", ] ) ) return "\n--\n".join(descriptions) def _is_gpt(self, llm) -> bool: return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None def _tool_calling( self, tool_string: str ) -> Union[ToolCalling, InstructorToolCalling]: try: model = InstructorToolCalling if self._is_gpt(self.llm) else ToolCalling converter = Converter( text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid ouput schema:\n\n{tool_string}```", llm=self.llm, model=model, instructions=dedent( """\ The schema should have the following structure, only two keys: - tool_name: str - arguments: dict (with all arguments being passed) Example: {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""", ), max_attemps=1, ) print(f"Converter: {converter}") calling = converter.to_pydantic() print(f"Calling: {calling}") if isinstance(calling, ConverterError): raise calling except Exception as e: self._run_attempts += 1 if self._run_attempts > self._max_parsing_attempts: self._telemetry.tool_usage_error(llm=self.llm) self.task.increment_tools_errors() self._printer.print(content=f"\n\n{e}\n", color="red") return ToolUsageErrorException( f'{self._i18n.errors("tool_usage_error")}\n{self._i18n.slice("format").format(tool_names=self.tools_names)}' ) return self._tool_calling(tool_string) return calling