Merge pull request #121 from piotrm0/piotrm/base_tool

remove independent BaseTool

src/crewai_tools/tools/base_tool.py

@@ -5,100 +5,7 @@ from langchain_core.tools import StructuredTool
 from pydantic import BaseModel, ConfigDict, Field, validator
 from pydantic import BaseModel as PydanticBaseModel
 
-
+from crewai.tools.base_tool import BaseTool
-class BaseTool(BaseModel, ABC):
-    class _ArgsSchemaPlaceholder(PydanticBaseModel):
-        pass
-
-    model_config = ConfigDict()
-
-    name: str
-    """The unique name of the tool that clearly communicates its purpose."""
-    description: str
-    """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
-    """The schema for the arguments that the tool accepts."""
-    description_updated: bool = False
-    """Flag to check if the description has been updated."""
-    cache_function: Optional[Callable] = lambda _args, _result: True
-    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
-    result_as_answer: bool = False
-    """Flag to check if the tool should be the final agent answer."""
-
-    @validator("args_schema", always=True, pre=True)
-    def _default_args_schema(
-        cls, v: Type[PydanticBaseModel]
-    ) -> Type[PydanticBaseModel]:
-        if not isinstance(v, cls._ArgsSchemaPlaceholder):
-            return v
-
-        return type(
-            f"{cls.__name__}Schema",
-            (PydanticBaseModel,),
-            {
-                "__annotations__": {
-                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
-                },
-            },
-        )
-
-    def model_post_init(self, __context: Any) -> None:
-        self._generate_description()
-
-        super().model_post_init(__context)
-
-    def run(
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        print(f"Using Tool: {self.name}")
-        return self._run(*args, **kwargs)
-
-    @abstractmethod
-    def _run(
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        """Here goes the actual implementation of the tool."""
-
-    def to_langchain(self) -> StructuredTool:
-        self._set_args_schema()
-        return StructuredTool(
-            name=self.name,
-            description=self.description,
-            args_schema=self.args_schema,
-            func=self._run,
-        )
-
-    def _set_args_schema(self):
-        if self.args_schema is None:
-            class_name = f"{self.__class__.__name__}Schema"
-            self.args_schema = type(
-                class_name,
-                (PydanticBaseModel,),
-                {
-                    "__annotations__": {
-                        k: v
-                        for k, v in self._run.__annotations__.items()
-                        if k != "return"
-                    },
-                },
-            )
-
-    def _generate_description(self):
-        args = []
-        args_description = []
-        for arg, attribute in self.args_schema.schema()["properties"].items():
-            if "type" in attribute:
-                args.append(f"{arg}: '{attribute['type']}'")
-            if "description" in attribute:
-                args_description.append(f"{arg}: '{attribute['description']}'")
-
-        description = self.description.replace("\n", " ")
-        self.description = f"{self.name}({', '.join(args)}) - {description} {', '.join(args_description)}"
-
 
 class Tool(BaseTool):
     func: Callable