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# Source code for langchain_core.tools

    """**Tools** are classes that an Agent uses to interact with the world.

    Each tool has a **description**. Agent uses the description to choose the right
    tool for the job.

    **Class hierarchy:**

    .. code-block::

        RunnableSerializable --> BaseTool --> <name>Tool  # Examples: AIPluginTool, BaseGraphQLTool
                                              <name>      # Examples: BraveSearch, HumanInputRun

    **Main helpers:**

    .. code-block::

        CallbackManagerForToolRun, AsyncCallbackManagerForToolRun
    """  # noqa: E501

    from __future__ import annotations

    import asyncio
    import inspect
    import textwrap
    import uuid
    import warnings
    from abc import ABC, abstractmethod
    from contextvars import copy_context
    from functools import partial
    from inspect import signature
    from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union

    from langchain_core._api import deprecated
    from langchain_core.callbacks import (
        AsyncCallbackManager,
        AsyncCallbackManagerForToolRun,
        BaseCallbackManager,
        CallbackManager,
        CallbackManagerForToolRun,
    )
    from langchain_core.callbacks.manager import (
        Callbacks,
    )
    from langchain_core.load.serializable import Serializable
    from langchain_core.prompts import (
        BasePromptTemplate,
        PromptTemplate,
        aformat_document,
        format_document,
    )
    from langchain_core.pydantic_v1 import (
        BaseModel,
        Extra,
        Field,
        ValidationError,
        create_model,
        root_validator,
        validate_arguments,
    )
    from langchain_core.retrievers import BaseRetriever
    from langchain_core.runnables import (
        Runnable,
        RunnableConfig,
        RunnableSerializable,
        ensure_config,
    )
    from langchain_core.runnables.config import (
        patch_config,
        run_in_executor,
        var_child_runnable_config,
    )
    from langchain_core.runnables.utils import accepts_context

    class SchemaAnnotationError(TypeError):
        """Raised when 'args_schema' is missing or has an incorrect type annotation."""

    def _create_subset_model(
        name: str, model: Type[BaseModel], field_names: list
    ) -> Type[BaseModel]:
        """Create a pydantic model with only a subset of model's fields."""
        fields = {}
        for field_name in field_names:
            field = model.__fields__[field_name]
            t = (
                # this isn't perfect but should work for most functions
                field.outer_type_
                if field.required and not field.allow_none
                else Optional[field.outer_type_]
            )
            fields[field_name] = (t, field.field_info)
        rtn = create_model(name, **fields)  # type: ignore
        return rtn

    def _get_filtered_args(
        inferred_model: Type[BaseModel],
        func: Callable,
    ) -> dict:
        """Get the arguments from a function's signature."""
        schema = inferred_model.schema()["properties"]
        valid_keys = signature(func).parameters
        return {k: schema[k] for k in valid_keys if k not in ("run_manager", "callbacks")}

    class _SchemaConfig:
        """Configuration for the pydantic model."""

        extra: Any = Extra.forbid
        arbitrary_types_allowed: bool = True

    def create_schema_from_function(
        model_name: str,
        func: Callable,
    ) -> Type[BaseModel]:
        """Create a pydantic schema from a function's signature.
        Args:
            model_name: Name to assign to the generated pydandic schema
            func: Function to generate the schema from
        Returns:
            A pydantic model with the same arguments as the function
        """
        # https://docs.pydantic.dev/latest/usage/validation_decorator/
        validated = validate_arguments(func, config=_SchemaConfig)  # type: ignore
        inferred_model = validated.model  # type: ignore
        if "run_manager" in inferred_model.__fields__:
            del inferred_model.__fields__["run_manager"]
        if "callbacks" in inferred_model.__fields__:
            del inferred_model.__fields__["callbacks"]
        # Pydantic adds placeholder virtual fields we need to strip
        valid_properties = _get_filtered_args(inferred_model, func)
        return _create_subset_model(
            f"{model_name}Schema", inferred_model, list(valid_properties)
        )

    class ToolException(Exception):
        """Optional exception that tool throws when execution error occurs.

        When this exception is thrown, the agent will not stop working,
        but it will handle the exception according to the handle_tool_error
        variable of the tool, and the processing result will be returned
        to the agent as observation, and printed in red on the console.
        """

        pass

    class BaseTool(RunnableSerializable[Union[str, Dict], Any]):
        """Interface LangChain tools must implement."""

        def __init_subclass__(cls, **kwargs: Any) -> None:
            """Create the definition of the new tool class."""
            super().__init_subclass__(**kwargs)

            args_schema_type = cls.__annotations__.get("args_schema", None)

            if args_schema_type is not None and args_schema_type == BaseModel:
                # Throw errors for common mis-annotations.
                # TODO: Use get_args / get_origin and fully
                # specify valid annotations.
                typehint_mandate = """
    class ChildTool(BaseTool):
        ...
        args_schema: Type[BaseModel] = SchemaClass
        ..."""
                name = cls.__name__
                raise SchemaAnnotationError(
                    f"Tool definition for {name} must include valid type annotations"
                    f" for argument 'args_schema' to behave as expected.\n"
                    f"Expected annotation of 'Type[BaseModel]'"
                    f" but got '{args_schema_type}'.\n"
                    f"Expected class looks like:\n"
                    f"{typehint_mandate}"
                )

        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.

        You can provide few-shot examples as a part of the description.
        """
        args_schema: Optional[Type[BaseModel]] = None
        """Pydantic model class to validate and parse the tool's input arguments."""
        return_direct: bool = False
        """Whether to return the tool's output directly. Setting this to True means

        that after the tool is called, the AgentExecutor will stop looping.
        """
        verbose: bool = False
        """Whether to log the tool's progress."""

        callbacks: Callbacks = Field(default=None, exclude=True)
        """Callbacks to be called during tool execution."""
        callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
        """Deprecated. Please use callbacks instead."""
        tags: Optional[List[str]] = None
        """Optional list of tags associated with the tool. Defaults to None
        These tags will be associated with each call to this tool,
        and passed as arguments to the handlers defined in `callbacks`.
        You can use these to eg identify a specific instance of a tool with its use case.
        """
        metadata: Optional[Dict[str, Any]] = None
        """Optional metadata associated with the tool. Defaults to None
        This metadata will be associated with each call to this tool,
        and passed as arguments to the handlers defined in `callbacks`.
        You can use these to eg identify a specific instance of a tool with its use case.
        """

        handle_tool_error: Optional[
            Union[bool, str, Callable[[ToolException], str]]
        ] = False
        """Handle the content of the ToolException thrown."""

        handle_validation_error: Optional[
            Union[bool, str, Callable[[ValidationError], str]]
        ] = False
        """Handle the content of the ValidationError thrown."""

        class Config(Serializable.Config):
            """Configuration for this pydantic object."""

            arbitrary_types_allowed = True

        @property
        def is_single_input(self) -> bool:
            """Whether the tool only accepts a single input."""
            keys = {k for k in self.args if k != "kwargs"}
            return len(keys) == 1

        @property
        def args(self) -> dict:
            if self.args_schema is not None:
                return self.args_schema.schema()["properties"]
            else:
                schema = create_schema_from_function(self.name, self._run)
                return schema.schema()["properties"]

        # --- Runnable ---

        def get_input_schema(
            self, config: Optional[RunnableConfig] = None
        ) -> Type[BaseModel]:
            """The tool's input schema."""
            if self.args_schema is not None:
                return self.args_schema
            else:
                return create_schema_from_function(self.name, self._run)

        def invoke(
            self,
            input: Union[str, Dict],
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            config = ensure_config(config)
            return self.run(
                input,
                callbacks=config.get("callbacks"),
                tags=config.get("tags"),
                metadata=config.get("metadata"),
                run_name=config.get("run_name"),
                run_id=config.pop("run_id", None),
                config=config,
                **kwargs,
            )

        async def ainvoke(
            self,
            input: Union[str, Dict],
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            config = ensure_config(config)
            return await self.arun(
                input,
                callbacks=config.get("callbacks"),
                tags=config.get("tags"),
                metadata=config.get("metadata"),
                run_name=config.get("run_name"),
                run_id=config.pop("run_id", None),
                config=config,
                **kwargs,
            )

        # --- Tool ---

        def _parse_input(
            self,
            tool_input: Union[str, Dict],
        ) -> Union[str, Dict[str, Any]]:
            """Convert tool input to pydantic model."""
            input_args = self.args_schema
            if isinstance(tool_input, str):
                if input_args is not None:
                    key_ = next(iter(input_args.__fields__.keys()))
                    input_args.validate({key_: tool_input})
                return tool_input
            else:
                if input_args is not None:
                    result = input_args.parse_obj(tool_input)
                    return {
                        k: getattr(result, k)
                        for k, v in result.dict().items()
                        if k in tool_input
                    }
            return tool_input

        @root_validator()
        def raise_deprecation(cls, values: Dict) -> Dict:
            """Raise deprecation warning if callback_manager is used."""
            if values.get("callback_manager") is not None:
                warnings.warn(
                    "callback_manager is deprecated. Please use callbacks instead.",
                    DeprecationWarning,
                )
                values["callbacks"] = values.pop("callback_manager", None)
            return values

        @abstractmethod
        def _run(
            self,
            *args: Any,
            **kwargs: Any,
        ) -> Any:
            """Use the tool.

            Add run_manager: Optional[CallbackManagerForToolRun] = None
            to child implementations to enable tracing,
            """

        async def _arun(
            self,
            *args: Any,
            **kwargs: Any,
        ) -> Any:
            """Use the tool asynchronously.

            Add run_manager: Optional[AsyncCallbackManagerForToolRun] = None
            to child implementations to enable tracing,
            """
            return await run_in_executor(None, self._run, *args, **kwargs)

        def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
            # For backwards compatibility, if run_input is a string,
            # pass as a positional argument.
            if isinstance(tool_input, str):
                return (tool_input,), {}
            else:
                return (), tool_input

        def run(
            self,
            tool_input: Union[str, Dict[str, Any]],
            verbose: Optional[bool] = None,
            start_color: Optional[str] = "green",
            color: Optional[str] = "green",
            callbacks: Callbacks = None,
            *,
            tags: Optional[List[str]] = None,
            metadata: Optional[Dict[str, Any]] = None,
            run_name: Optional[str] = None,
            run_id: Optional[uuid.UUID] = None,
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            """Run the tool."""
            if not self.verbose and verbose is not None:
                verbose_ = verbose
            else:
                verbose_ = self.verbose
            callback_manager = CallbackManager.configure(
                callbacks,
                self.callbacks,
                verbose_,
                tags,
                self.tags,
                metadata,
                self.metadata,
            )
            # TODO: maybe also pass through run_manager is _run supports kwargs
            new_arg_supported = signature(self._run).parameters.get("run_manager")
            run_manager = callback_manager.on_tool_start(
                {"name": self.name, "description": self.description},
                tool_input if isinstance(tool_input, str) else str(tool_input),
                color=start_color,
                name=run_name,
                run_id=run_id,
                # Inputs by definition should always be dicts.
                # For now, it's unclear whether this assumption is ever violated,
                # but if it is we will send a `None` value to the callback instead
                # And will need to address issue via a patch.
                inputs=None if isinstance(tool_input, str) else tool_input,
                **kwargs,
            )
            try:
                child_config = patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                )
                context = copy_context()
                context.run(var_child_runnable_config.set, child_config)
                parsed_input = self._parse_input(tool_input)
                tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
                observation = (
                    context.run(
                        self._run, *tool_args, run_manager=run_manager, **tool_kwargs
                    )
                    if new_arg_supported
                    else context.run(self._run, *tool_args, **tool_kwargs)
                )
            except ValidationError as e:
                if not self.handle_validation_error:
                    raise e
                elif isinstance(self.handle_validation_error, bool):
                    observation = "Tool input validation error"
                elif isinstance(self.handle_validation_error, str):
                    observation = self.handle_validation_error
                elif callable(self.handle_validation_error):
                    observation = self.handle_validation_error(e)
                else:
                    raise ValueError(
                        f"Got unexpected type of `handle_validation_error`. Expected bool, "
                        f"str or callable. Received: {self.handle_validation_error}"
                    )
                return observation
            except ToolException as e:
                if not self.handle_tool_error:
                    run_manager.on_tool_error(e)
                    raise e
                elif isinstance(self.handle_tool_error, bool):
                    if e.args:
                        observation = e.args[0]
                    else:
                        observation = "Tool execution error"
                elif isinstance(self.handle_tool_error, str):
                    observation = self.handle_tool_error
                elif callable(self.handle_tool_error):
                    observation = self.handle_tool_error(e)
                else:
                    raise ValueError(
                        f"Got unexpected type of `handle_tool_error`. Expected bool, str "
                        f"or callable. Received: {self.handle_tool_error}"
                    )
                run_manager.on_tool_end(observation, color="red", name=self.name, **kwargs)
                return observation
            except (Exception, KeyboardInterrupt) as e:
                run_manager.on_tool_error(e)
                raise e
            else:
                run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)
                return observation

        async def arun(
            self,
            tool_input: Union[str, Dict],
            verbose: Optional[bool] = None,
            start_color: Optional[str] = "green",
            color: Optional[str] = "green",
            callbacks: Callbacks = None,
            *,
            tags: Optional[List[str]] = None,
            metadata: Optional[Dict[str, Any]] = None,
            run_name: Optional[str] = None,
            run_id: Optional[uuid.UUID] = None,
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            """Run the tool asynchronously."""
            if not self.verbose and verbose is not None:
                verbose_ = verbose
            else:
                verbose_ = self.verbose
            callback_manager = AsyncCallbackManager.configure(
                callbacks,
                self.callbacks,
                verbose_,
                tags,
                self.tags,
                metadata,
                self.metadata,
            )
            new_arg_supported = signature(self._arun).parameters.get("run_manager")
            run_manager = await callback_manager.on_tool_start(
                {"name": self.name, "description": self.description},
                tool_input if isinstance(tool_input, str) else str(tool_input),
                color=start_color,
                name=run_name,
                inputs=tool_input,
                run_id=run_id,
                **kwargs,
            )
            try:
                parsed_input = self._parse_input(tool_input)
                # We then call the tool on the tool input to get an observation
                tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
                child_config = patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                )
                context = copy_context()
                context.run(var_child_runnable_config.set, child_config)
                coro = (
                    context.run(
                        self._arun, *tool_args, run_manager=run_manager, **tool_kwargs
                    )
                    if new_arg_supported
                    else context.run(self._arun, *tool_args, **tool_kwargs)
                )
                if accepts_context(asyncio.create_task):
                    observation = await asyncio.create_task(coro, context=context)  # type: ignore
                else:
                    observation = await coro

            except ValidationError as e:
                if not self.handle_validation_error:
                    raise e
                elif isinstance(self.handle_validation_error, bool):
                    observation = "Tool input validation error"
                elif isinstance(self.handle_validation_error, str):
                    observation = self.handle_validation_error
                elif callable(self.handle_validation_error):
                    observation = self.handle_validation_error(e)
                else:
                    raise ValueError(
                        f"Got unexpected type of `handle_validation_error`. Expected bool, "
                        f"str or callable. Received: {self.handle_validation_error}"
                    )
                return observation
            except ToolException as e:
                if not self.handle_tool_error:
                    await run_manager.on_tool_error(e)
                    raise e
                elif isinstance(self.handle_tool_error, bool):
                    if e.args:
                        observation = e.args[0]
                    else:
                        observation = "Tool execution error"
                elif isinstance(self.handle_tool_error, str):
                    observation = self.handle_tool_error
                elif callable(self.handle_tool_error):
                    observation = self.handle_tool_error(e)
                else:
                    raise ValueError(
                        f"Got unexpected type of `handle_tool_error`. Expected bool, str "
                        f"or callable. Received: {self.handle_tool_error}"
                    )
                await run_manager.on_tool_end(
                    observation, color="red", name=self.name, **kwargs
                )
                return observation
            except (Exception, KeyboardInterrupt) as e:
                await run_manager.on_tool_error(e)
                raise e
            else:
                await run_manager.on_tool_end(
                    observation, color=color, name=self.name, **kwargs
                )
                return observation

        @deprecated("0.1.47", alternative="invoke", removal="0.3.0")
        def __call__(self, tool_input: str, callbacks: Callbacks = None) -> str:
            """Make tool callable."""
            return self.run(tool_input, callbacks=callbacks)

    class Tool(BaseTool):
        """Tool that takes in function or coroutine directly."""

        description: str = ""
        func: Optional[Callable[..., str]]
        """The function to run when the tool is called."""
        coroutine: Optional[Callable[..., Awaitable[str]]] = None
        """The asynchronous version of the function."""

        # --- Runnable ---

        async def ainvoke(
            self,
            input: Union[str, Dict],
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            if not self.coroutine:
                # If the tool does not implement async, fall back to default implementation
                return await run_in_executor(config, self.invoke, input, config, **kwargs)

            return await super().ainvoke(input, config, **kwargs)

        # --- Tool ---

        @property
        def args(self) -> dict:
            """The tool's input arguments."""
            if self.args_schema is not None:
                return self.args_schema.schema()["properties"]
            # For backwards compatibility, if the function signature is ambiguous,
            # assume it takes a single string input.
            return {"tool_input": {"type": "string"}}

        def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
            """Convert tool input to pydantic model."""
            args, kwargs = super()._to_args_and_kwargs(tool_input)
            # For backwards compatibility. The tool must be run with a single input
            all_args = list(args) + list(kwargs.values())
            if len(all_args) != 1:
                raise ToolException(
                    f"""Too many arguments to single-input tool {self.name}.
                    Consider using StructuredTool instead."""
                    f" Args: {all_args}"
                )
            return tuple(all_args), {}

        def _run(
            self,
            *args: Any,
            run_manager: Optional[CallbackManagerForToolRun] = None,
            **kwargs: Any,
        ) -> Any:
            """Use the tool."""
            if self.func:
                new_argument_supported = signature(self.func).parameters.get("callbacks")
                return (
                    self.func(
                        *args,
                        callbacks=run_manager.get_child() if run_manager else None,
                        **kwargs,
                    )
                    if new_argument_supported
                    else self.func(*args, **kwargs)
                )
            raise NotImplementedError("Tool does not support sync")

        async def _arun(
            self,
            *args: Any,
            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
            **kwargs: Any,
        ) -> Any:
            """Use the tool asynchronously."""
            if self.coroutine:
                new_argument_supported = signature(self.coroutine).parameters.get(
                    "callbacks"
                )
                return (
                    await self.coroutine(
                        *args,
                        callbacks=run_manager.get_child() if run_manager else None,
                        **kwargs,
                    )
                    if new_argument_supported
                    else await self.coroutine(*args, **kwargs)
                )
            else:
                return await run_in_executor(
                    None,
                    self._run,
                    run_manager=run_manager.get_sync() if run_manager else None,
                    *args,
                    **kwargs,
                )

        # TODO: this is for backwards compatibility, remove in future
        def __init__(
            self, name: str, func: Optional[Callable], description: str, **kwargs: Any
        ) -> None:
            """Initialize tool."""
            super(Tool, self).__init__(  # type: ignore[call-arg]
                name=name, func=func, description=description, **kwargs
            )

        @classmethod
        def from_function(
            cls,
            func: Optional[Callable],
            name: str,  # We keep these required to support backwards compatibility
            description: str,
            return_direct: bool = False,
            args_schema: Optional[Type[BaseModel]] = None,
            coroutine: Optional[
                Callable[..., Awaitable[Any]]
            ] = None,  # This is last for compatibility, but should be after func
            **kwargs: Any,
        ) -> Tool:
            """Initialize tool from a function."""
            if func is None and coroutine is None:
                raise ValueError("Function and/or coroutine must be provided")
            return cls(
                name=name,
                func=func,
                coroutine=coroutine,
                description=description,
                return_direct=return_direct,
                args_schema=args_schema,
                **kwargs,
            )

    class StructuredTool(BaseTool):
        """Tool that can operate on any number of inputs."""

        description: str = ""
        args_schema: Type[BaseModel] = Field(..., description="The tool schema.")
        """The input arguments' schema."""
        func: Optional[Callable[..., Any]]
        """The function to run when the tool is called."""
        coroutine: Optional[Callable[..., Awaitable[Any]]] = None
        """The asynchronous version of the function."""

        # --- Runnable ---

        async def ainvoke(
            self,
            input: Union[str, Dict],
            config: Optional[RunnableConfig] = None,
            **kwargs: Any,
        ) -> Any:
            if not self.coroutine:
                # If the tool does not implement async, fall back to default implementation
                return await run_in_executor(config, self.invoke, input, config, **kwargs)

            return await super().ainvoke(input, config, **kwargs)

        # --- Tool ---

        @property
        def args(self) -> dict:
            """The tool's input arguments."""
            return self.args_schema.schema()["properties"]

        def _run(
            self,
            *args: Any,
            run_manager: Optional[CallbackManagerForToolRun] = None,
            **kwargs: Any,
        ) -> Any:
            """Use the tool."""
            if self.func:
                new_argument_supported = signature(self.func).parameters.get("callbacks")
                return (
                    self.func(
                        *args,
                        callbacks=run_manager.get_child() if run_manager else None,
                        **kwargs,
                    )
                    if new_argument_supported
                    else self.func(*args, **kwargs)
                )
            raise NotImplementedError("Tool does not support sync")

        async def _arun(
            self,
            *args: Any,
            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
            **kwargs: Any,
        ) -> str:
            """Use the tool asynchronously."""
            if self.coroutine:
                new_argument_supported = signature(self.coroutine).parameters.get(
                    "callbacks"
                )
                return (
                    await self.coroutine(
                        *args,
                        callbacks=run_manager.get_child() if run_manager else None,
                        **kwargs,
                    )
                    if new_argument_supported
                    else await self.coroutine(*args, **kwargs)
                )
            return await run_in_executor(
                None,
                self._run,
                run_manager=run_manager.get_sync() if run_manager else None,
                *args,
                **kwargs,
            )

        @classmethod
        def from_function(
            cls,
            func: Optional[Callable] = None,
            coroutine: Optional[Callable[..., Awaitable[Any]]] = None,
            name: Optional[str] = None,
            description: Optional[str] = None,
            return_direct: bool = False,
            args_schema: Optional[Type[BaseModel]] = None,
            infer_schema: bool = True,
            **kwargs: Any,
        ) -> StructuredTool:
            """Create tool from a given function.

            A classmethod that helps to create a tool from a function.

            Args:
                func: The function from which to create a tool
                coroutine: The async function from which to create a tool
                name: The name of the tool. Defaults to the function name
                description: The description of the tool. Defaults to the function docstring
                return_direct: Whether to return the result directly or as a callback
                args_schema: The schema of the tool's input arguments
                infer_schema: Whether to infer the schema from the function's signature
                **kwargs: Additional arguments to pass to the tool

            Returns:
                The tool

            Examples:

                .. code-block:: python

                    def add(a: int, b: int) -> int:
                        \"\"\"Add two numbers\"\"\"
                        return a + b
                    tool = StructuredTool.from_function(add)
                    tool.run(1, 2) # 3
            """

            if func is not None:
                source_function = func
            elif coroutine is not None:
                source_function = coroutine
            else:
                raise ValueError("Function and/or coroutine must be provided")
            name = name or source_function.__name__
            description_ = description or source_function.__doc__
            if description_ is None:
                raise ValueError(
                    "Function must have a docstring if description not provided."
                )
            if description is None:
                # Only apply if using the function's docstring
                description_ = textwrap.dedent(description_).strip()

            # Description example:
            # search_api(query: str) - Searches the API for the query.
            sig = signature(source_function)
            description_ = f"{name}{sig} - {description_.strip()}"
            _args_schema = args_schema
            if _args_schema is None and infer_schema:
                # schema name is appended within function
                _args_schema = create_schema_from_function(name, source_function)
            return cls(
                name=name,
                func=func,
                coroutine=coroutine,
                args_schema=_args_schema,  # type: ignore[arg-type]
                description=description_,
                return_direct=return_direct,
                **kwargs,
            )

    def tool(
        *args: Union[str, Callable, Runnable],
        return_direct: bool = False,
        args_schema: Optional[Type[BaseModel]] = None,
        infer_schema: bool = True,
    ) -> Callable:
        """Make tools out of functions, can be used with or without arguments.

        Args:
            *args: The arguments to the tool.
            return_direct: Whether to return directly from the tool rather
                than continuing the agent loop.
            args_schema: optional argument schema for user to specify
            infer_schema: Whether to infer the schema of the arguments from
                the function's signature. This also makes the resultant tool
                accept a dictionary input to its `run()` function.

        Requires:
            - Function must be of type (str) -> str
            - Function must have a docstring

        Examples:
            .. code-block:: python

                @tool
                def search_api(query: str) -> str:
                    # Searches the API for the query.
                    return

                @tool("search", return_direct=True)
                def search_api(query: str) -> str:
                    # Searches the API for the query.
                    return
        """

        def _make_with_name(tool_name: str) -> Callable:
            def _make_tool(dec_func: Union[Callable, Runnable]) -> BaseTool:
                if isinstance(dec_func, Runnable):
                    runnable = dec_func

                    if runnable.input_schema.schema().get("type") != "object":
                        raise ValueError("Runnable must have an object schema.")

                    async def ainvoke_wrapper(
                        callbacks: Optional[Callbacks] = None, **kwargs: Any
                    ) -> Any:
                        return await runnable.ainvoke(kwargs, {"callbacks": callbacks})

                    def invoke_wrapper(
                        callbacks: Optional[Callbacks] = None, **kwargs: Any
                    ) -> Any:
                        return runnable.invoke(kwargs, {"callbacks": callbacks})

                    coroutine = ainvoke_wrapper
                    func = invoke_wrapper
                    schema: Optional[Type[BaseModel]] = runnable.input_schema
                    description = repr(runnable)
                elif inspect.iscoroutinefunction(dec_func):
                    coroutine = dec_func
                    func = None
                    schema = args_schema
                    description = None
                else:
                    coroutine = None
                    func = dec_func
                    schema = args_schema
                    description = None

                if infer_schema or args_schema is not None:
                    return StructuredTool.from_function(
                        func,
                        coroutine,
                        name=tool_name,
                        description=description,
                        return_direct=return_direct,
                        args_schema=schema,
                        infer_schema=infer_schema,
                    )
                # If someone doesn't want a schema applied, we must treat it as
                # a simple string->string function
                if func.__doc__ is None:
                    raise ValueError(
                        "Function must have a docstring if "
                        "description not provided and infer_schema is False."
                    )
                return Tool(
                    name=tool_name,
                    func=func,
                    description=f"{tool_name} tool",
                    return_direct=return_direct,
                    coroutine=coroutine,
                )

            return _make_tool

        if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable):
            return _make_with_name(args[0])(args[1])
        elif len(args) == 1 and isinstance(args[0], str):
            # if the argument is a string, then we use the string as the tool name
            # Example usage: @tool("search", return_direct=True)
            return _make_with_name(args[0])
        elif len(args) == 1 and callable(args[0]):
            # if the argument is a function, then we use the function name as the tool name
            # Example usage: @tool
            return _make_with_name(args[0].__name__)(args[0])
        elif len(args) == 0:
            # if there are no arguments, then we use the function name as the tool name
            # Example usage: @tool(return_direct=True)
            def _partial(func: Callable[[str], str]) -> BaseTool:
                return _make_with_name(func.__name__)(func)

            return _partial
        else:
            raise ValueError("Too many arguments for tool decorator")

    class RetrieverInput(BaseModel):
        """Input to the retriever."""

        query: str = Field(description="query to look up in retriever")

    def _get_relevant_documents(
        query: str,
        retriever: BaseRetriever,
        document_prompt: BasePromptTemplate,
        document_separator: str,
        callbacks: Callbacks = None,
    ) -> str:
        docs = retriever.invoke(query, config={"callbacks": callbacks})
        return document_separator.join(
            format_document(doc, document_prompt) for doc in docs
        )

    async def _aget_relevant_documents(
        query: str,
        retriever: BaseRetriever,
        document_prompt: BasePromptTemplate,
        document_separator: str,
        callbacks: Callbacks = None,
    ) -> str:
        docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
        return document_separator.join(
            [await aformat_document(doc, document_prompt) for doc in docs]
        )

    def create_retriever_tool(
        retriever: BaseRetriever,
        name: str,
        description: str,
        *,
        document_prompt: Optional[BasePromptTemplate] = None,
        document_separator: str = "\n\n",
    ) -> Tool:
        """Create a tool to do retrieval of documents.

        Args:
            retriever: The retriever to use for the retrieval
            name: The name for the tool. This will be passed to the language model,
                so should be unique and somewhat descriptive.
            description: The description for the tool. This will be passed to the language
                model, so should be descriptive.

        Returns:
            Tool class to pass to an agent
        """
        document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
        func = partial(
            _get_relevant_documents,
            retriever=retriever,
            document_prompt=document_prompt,
            document_separator=document_separator,
        )
        afunc = partial(
            _aget_relevant_documents,
            retriever=retriever,
            document_prompt=document_prompt,
            document_separator=document_separator,
        )
        return Tool(
            name=name,
            description=description,
            func=func,
            coroutine=afunc,
            args_schema=RetrieverInput,
        )

    ToolsRenderer = Callable[[List[BaseTool]], str]

    def render_text_description(tools: List[BaseTool]) -> str:
        """Render the tool name and description in plain text.

        Output will be in the format of:

        .. code-block:: markdown

            search: This tool is used for search
            calculator: This tool is used for math
        """
        return "\n".join([f"{tool.name}: {tool.description}" for tool in tools])

    def render_text_description_and_args(tools: List[BaseTool]) -> str:
        """Render the tool name, description, and args in plain text.

        Output will be in the format of:

        .. code-block:: markdown

            search: This tool is used for search, args: {"query": {"type": "string"}}
            calculator: This tool is used for math, \
    args: {"expression": {"type": "string"}}
        """
        tool_strings = []
        for tool in tools:
            args_schema = str(tool.args)
            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
        return "\n".join(tool_strings)

    class BaseToolkit(BaseModel, ABC):
        """Base Toolkit representing a collection of related tools."""

        @abstractmethod
        def get_tools(self) -> List[BaseTool]:
            """Get the tools in the toolkit."""