mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-08 07:38:29 +00:00
407 lines
18 KiB
Python
407 lines
18 KiB
Python
import ast
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from difflib import SequenceMatcher
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from textwrap import dedent
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from typing import Any, List, Union
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from langchain_core.tools import BaseTool
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from langchain_openai import ChatOpenAI
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.telemetry import Telemetry
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from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
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from crewai.utilities import I18N, Converter, ConverterError, Printer
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try:
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import agentops
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except ImportError:
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agentops = None
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OPENAI_BIGGER_MODELS = ["gpt-4"]
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class ToolUsageErrorException(Exception):
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"""Exception raised for errors in the tool usage."""
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def __init__(self, message: str) -> None:
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self.message = message
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super().__init__(self.message)
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class ToolUsage:
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"""
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Class that represents the usage of a tool by an agent.
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Attributes:
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task: Task being executed.
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tools_handler: Tools handler that will manage the tool usage.
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tools: List of tools available for the agent.
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original_tools: Original tools available for the agent before being converted to BaseTool.
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tools_description: Description of the tools available for the agent.
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tools_names: Names of the tools available for the agent.
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function_calling_llm: Language model to be used for the tool usage.
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"""
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def __init__(
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self,
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tools_handler: ToolsHandler,
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tools: List[BaseTool],
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original_tools: List[Any],
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tools_description: str,
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tools_names: str,
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task: Any,
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function_calling_llm: Any,
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agent: Any,
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action: Any,
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) -> None:
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self._i18n: I18N = I18N()
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self._printer: Printer = Printer()
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self._telemetry: Telemetry = Telemetry()
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self._run_attempts: int = 1
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self._max_parsing_attempts: int = 3
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self._remember_format_after_usages: int = 3
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self.agent = agent
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self.tools_description = tools_description
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self.tools_names = tools_names
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self.tools_handler = tools_handler
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self.original_tools = original_tools
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self.tools = tools
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self.task = task
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self.action = action
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self.function_calling_llm = function_calling_llm
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# Set the maximum parsing attempts for bigger models
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if (isinstance(self.function_calling_llm, ChatOpenAI)) and (
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self.function_calling_llm.openai_api_base is None
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):
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if self.function_calling_llm.model_name in OPENAI_BIGGER_MODELS:
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self._max_parsing_attempts = 2
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self._remember_format_after_usages = 4
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def parse(self, tool_string: str):
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"""Parse the tool string and return the tool calling."""
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return self._tool_calling(tool_string)
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def use(
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self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
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) -> str:
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if isinstance(calling, ToolUsageErrorException):
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error = calling.message
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self._printer.print(content=f"\n\n{error}\n", color="red")
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self.task.increment_tools_errors()
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return error
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# BUG? The code below seems to be unreachable
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try:
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tool = self._select_tool(calling.tool_name)
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except Exception as e:
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error = getattr(e, "message", str(e))
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self.task.increment_tools_errors()
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self._printer.print(content=f"\n\n{error}\n", color="red")
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return error
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return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
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def _use(
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self,
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tool_string: str,
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tool: BaseTool,
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calling: Union[ToolCalling, InstructorToolCalling],
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) -> str: # TODO: Fix this return type
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tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
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if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
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try:
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result = self._i18n.errors("task_repeated_usage").format(
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tool_names=self.tools_names
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)
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self._printer.print(content=f"\n\n{result}\n", color="purple")
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self._telemetry.tool_repeated_usage(
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llm=self.function_calling_llm,
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tool_name=tool.name,
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attempts=self._run_attempts,
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)
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result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
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return result # type: ignore # Fix the reutrn type of this function
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except Exception:
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self.task.increment_tools_errors()
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result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
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if self.tools_handler.cache:
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result = self.tools_handler.cache.read( # type: ignore # Incompatible types in assignment (expression has type "str | None", variable has type "str")
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tool=calling.tool_name, input=calling.arguments
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)
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original_tool = next(
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(ot for ot in self.original_tools if ot.name == tool.name), None
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)
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if result is None: #! finecwg: if not result --> if result is None
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try:
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if calling.tool_name in [
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"Delegate work to coworker",
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"Ask question to coworker",
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]:
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self.task.increment_delegations()
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if calling.arguments:
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try:
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acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
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arguments = {
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k: v
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for k, v in calling.arguments.items()
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if k in acceptable_args
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}
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result = tool.invoke(input=arguments)
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except Exception:
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arguments = calling.arguments
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result = tool.invoke(input=arguments)
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else:
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result = tool.invoke(input={})
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except Exception as e:
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self._run_attempts += 1
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if self._run_attempts > self._max_parsing_attempts:
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self._telemetry.tool_usage_error(llm=self.function_calling_llm)
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error_message = self._i18n.errors("tool_usage_exception").format(
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error=e, tool=tool.name, tool_inputs=tool.description
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)
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error = ToolUsageErrorException(
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f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
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).message
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self.task.increment_tools_errors()
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self._printer.print(content=f"\n\n{error_message}\n", color="red")
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return error # type: ignore # No return value expected
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self.task.increment_tools_errors()
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if agentops:
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agentops.record(
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agentops.ErrorEvent(exception=e, trigger_event=tool_event)
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)
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return self.use(calling=calling, tool_string=tool_string) # type: ignore # No return value expected
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if self.tools_handler:
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should_cache = True
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if (
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hasattr(original_tool, "cache_function")
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and original_tool.cache_function # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
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):
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should_cache = original_tool.cache_function( # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
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calling.arguments, result
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)
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self.tools_handler.on_tool_use(
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calling=calling, output=result, should_cache=should_cache
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)
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self._printer.print(content=f"\n\n{result}\n", color="purple")
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if agentops:
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agentops.record(tool_event)
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self._telemetry.tool_usage(
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llm=self.function_calling_llm,
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tool_name=tool.name,
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attempts=self._run_attempts,
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)
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result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
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data = {
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"result": result,
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"tool_name": tool.name,
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"tool_args": calling.arguments,
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}
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if (
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hasattr(original_tool, "result_as_answer")
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and original_tool.result_as_answer # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
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):
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result_as_answer = original_tool.result_as_answer # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
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data["result_as_answer"] = result_as_answer
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self.agent.tools_results.append(data)
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return result # type: ignore # No return value expected
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def _format_result(self, result: Any) -> None:
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self.task.used_tools += 1
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if self._should_remember_format(): # type: ignore # "_should_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
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result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
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return result
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def _should_remember_format(self) -> None:
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return self.task.used_tools % self._remember_format_after_usages == 0
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def _remember_format(self, result: str) -> None:
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result = str(result)
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result += "\n\n" + self._i18n.slice("tools").format(
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tools=self.tools_description, tool_names=self.tools_names
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)
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return result # type: ignore # No return value expected
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def _check_tool_repeated_usage(
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self, calling: Union[ToolCalling, InstructorToolCalling]
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) -> None:
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if not self.tools_handler:
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return False # type: ignore # No return value expected
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if last_tool_usage := self.tools_handler.last_used_tool:
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return (calling.tool_name == last_tool_usage.tool_name) and ( # type: ignore # No return value expected
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calling.arguments == last_tool_usage.arguments
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)
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def _select_tool(self, tool_name: str) -> BaseTool:
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order_tools = sorted(
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self.tools,
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key=lambda tool: SequenceMatcher(
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None, tool.name.lower().strip(), tool_name.lower().strip()
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).ratio(),
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reverse=True,
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)
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for tool in order_tools:
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if (
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tool.name.lower().strip() == tool_name.lower().strip()
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or SequenceMatcher(
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None, tool.name.lower().strip(), tool_name.lower().strip()
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).ratio()
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> 0.85
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):
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return tool
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self.task.increment_tools_errors()
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if tool_name and tool_name != "":
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raise Exception(
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f"Action '{tool_name}' don't exist, these are the only available Actions:\n {self.tools_description}"
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)
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else:
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raise Exception(
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f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
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)
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def _render(self) -> str:
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"""Render the tool name and description in plain text."""
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descriptions = []
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for tool in self.tools:
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args = {
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k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]}
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for k, v in tool.args.items()
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}
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descriptions.append(
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"\n".join(
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[
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f"Tool Name: {tool.name.lower()}",
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f"Tool Description: {tool.description}",
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f"Tool Arguments: {args}",
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]
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)
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)
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return "\n--\n".join(descriptions)
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def _is_gpt(self, llm) -> bool:
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return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
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def _tool_calling(
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self, tool_string: str
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) -> Union[ToolCalling, InstructorToolCalling]:
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try:
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if self.function_calling_llm:
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model = (
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InstructorToolCalling
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if self._is_gpt(self.function_calling_llm)
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else ToolCalling
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)
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converter = Converter(
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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 output schema:\n\n{tool_string}```",
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llm=self.function_calling_llm,
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model=model,
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instructions=dedent(
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"""\
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The schema should have the following structure, only two keys:
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- tool_name: str
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- arguments: dict (with all arguments being passed)
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Example:
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{"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
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),
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max_attempts=1,
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)
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calling = converter.to_pydantic()
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if isinstance(calling, ConverterError):
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raise calling
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else:
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tool_name = self.action.tool
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tool = self._select_tool(tool_name)
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try:
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tool_input = self._validate_tool_input(self.action.tool_input)
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arguments = ast.literal_eval(tool_input)
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except Exception:
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return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
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f'{self._i18n.errors("tool_arguments_error")}'
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)
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if not isinstance(arguments, dict):
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return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
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f'{self._i18n.errors("tool_arguments_error")}'
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)
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calling = ToolCalling( # type: ignore # Unexpected keyword argument "log" for "ToolCalling"
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tool_name=tool.name,
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arguments=arguments,
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log=tool_string,
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)
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except Exception as e:
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self._run_attempts += 1
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if self._run_attempts > self._max_parsing_attempts:
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self._telemetry.tool_usage_error(llm=self.function_calling_llm)
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self.task.increment_tools_errors()
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self._printer.print(content=f"\n\n{e}\n", color="red")
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return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
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f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
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)
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return self._tool_calling(tool_string)
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return calling
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def _validate_tool_input(self, tool_input: str) -> str:
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try:
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ast.literal_eval(tool_input)
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return tool_input
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except Exception:
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# Clean and ensure the string is properly enclosed in braces
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tool_input = tool_input.strip()
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if not tool_input.startswith("{"):
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tool_input = "{" + tool_input
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if not tool_input.endswith("}"):
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tool_input += "}"
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# Manually split the input into key-value pairs
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entries = tool_input.strip("{} ").split(",")
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formatted_entries = []
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for entry in entries:
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if ":" not in entry:
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continue # Skip malformed entries
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key, value = entry.split(":", 1)
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# Remove extraneous white spaces and quotes, replace single quotes
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key = key.strip().strip('"').replace("'", '"')
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value = value.strip()
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# Handle replacement of single quotes at the start and end of the value string
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if value.startswith("'") and value.endswith("'"):
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value = value[1:-1] # Remove single quotes
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value = (
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'"' + value.replace('"', '\\"') + '"'
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) # Re-encapsulate with double quotes
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elif value.isdigit(): # Check if value is a digit, hence integer
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formatted_value = value
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elif value.lower() in [
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"true",
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"false",
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"null",
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]: # Check for boolean and null values
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formatted_value = value.lower()
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else:
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# Assume the value is a string and needs quotes
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formatted_value = '"' + value.replace('"', '\\"') + '"'
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# Rebuild the entry with proper quoting
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formatted_entry = f'"{key}": {formatted_value}'
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formatted_entries.append(formatted_entry)
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# Reconstruct the JSON string
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new_json_string = "{" + ", ".join(formatted_entries) + "}"
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return new_json_string
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