Source code for langchain_core.tools.structured

from __future__ import annotations

import textwrap
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Type, Union

from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain_core.messages import ToolCall
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import RunnableConfig, run_in_executor
from langchain_core.tools.base import (
    FILTERED_ARGS,
    BaseTool,
    _get_runnable_config_param,
    create_schema_from_function,
)
from langchain_core.utils.pydantic import TypeBaseModel


[docs]class StructuredTool(BaseTool): """Tool that can operate on any number of inputs.""" description: str = "" args_schema: TypeBaseModel = 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 --- # TODO: Is this needed?
[docs] async def ainvoke( self, input: Union[str, Dict, ToolCall], 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, config: RunnableConfig, run_manager: Optional[CallbackManagerForToolRun] = None, **kwargs: Any, ) -> Any: """Use the tool.""" if self.func: if run_manager and signature(self.func).parameters.get("callbacks"): kwargs["callbacks"] = run_manager.get_child() if config_param := _get_runnable_config_param(self.func): kwargs[config_param] = config return self.func(*args, **kwargs) raise NotImplementedError("StructuredTool does not support sync invocation.") async def _arun( self, *args: Any, config: RunnableConfig, run_manager: Optional[AsyncCallbackManagerForToolRun] = None, **kwargs: Any, ) -> Any: """Use the tool asynchronously.""" if self.coroutine: if run_manager and signature(self.coroutine).parameters.get("callbacks"): kwargs["callbacks"] = run_manager.get_child() if config_param := _get_runnable_config_param(self.coroutine): kwargs[config_param] = config return await self.coroutine(*args, **kwargs) # NOTE: this code is unreachable since _arun is only called if coroutine is not # None. return await super()._arun( *args, config=config, run_manager=run_manager, **kwargs )
[docs] @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, *, response_format: Literal["content", "content_and_artifact"] = "content", parse_docstring: bool = False, error_on_invalid_docstring: bool = False, **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. Defaults to False. args_schema: The schema of the tool's input arguments. Defaults to None. infer_schema: Whether to infer the schema from the function's signature. Defaults to True. response_format: The tool response format. If "content" then the output of the tool is interpreted as the contents of a ToolMessage. If "content_and_artifact" then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage. Defaults to "content". parse_docstring: if ``infer_schema`` and ``parse_docstring``, will attempt to parse parameter descriptions from Google Style function docstrings. Defaults to False. error_on_invalid_docstring: if ``parse_docstring`` is provided, configure whether to raise ValueError on invalid Google Style docstrings. Defaults to False. kwargs: Additional arguments to pass to the tool Returns: The tool. Raises: ValueError: If the function is not provided. 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__ if args_schema is None and infer_schema: # schema name is appended within function args_schema = create_schema_from_function( name, source_function, parse_docstring=parse_docstring, error_on_invalid_docstring=error_on_invalid_docstring, filter_args=_filter_schema_args(source_function), ) description_ = description if description is None and not parse_docstring: description_ = source_function.__doc__ or None if description_ is None and args_schema: description_ = args_schema.__doc__ or None 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. description_ = f"{description_.strip()}" return cls( name=name, func=func, coroutine=coroutine, args_schema=args_schema, # type: ignore[arg-type] description=description_, return_direct=return_direct, response_format=response_format, **kwargs, )
def _filter_schema_args(func: Callable) -> List[str]: filter_args = list(FILTERED_ARGS) if config_param := _get_runnable_config_param(func): filter_args.append(config_param) # filter_args.extend(_get_non_model_params(type_hints)) return filter_args