langchain.agents.agent_toolkits.nla.tool.NLATool

class langchain.agents.agent_toolkits.nla.tool.NLATool(name: str, func: Callable, description: str, *, args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, coroutine: Optional[Callable[[...], Awaitable[str]]] = None)

Bases: Tool

Natural Language API Tool.

Initialize tool.

param args_schema: Optional[Type[BaseModel]] = None

Pydantic model class to validate and parse the tool’s input arguments.

param callback_manager: Optional[BaseCallbackManager] = None

Deprecated. Please use callbacks instead.

param callbacks: Callbacks = None

Callbacks to be called during tool execution.

param coroutine: Optional[Callable[..., Awaitable[str]]] = None

The asynchronous version of the function.

param 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.

param func: Callable[..., str] [Required]

The function to run when the tool is called.

param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False

Handle the content of the ToolException thrown.

param 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.

param name: str [Required]

The unique name of the tool that clearly communicates its purpose.

param 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.

param 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.

param verbose: bool = False

Whether to log the tool’s progress.

__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str

Make tool callable.

async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any

async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

Run the tool asynchronously.

classmethod from_function(func: Callable, name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, **kwargs: Any) → Tool

Initialize tool from a function.

classmethod from_llm_and_method(llm: BaseLanguageModel, path: str, method: str, spec: OpenAPISpec, requests: Optional[Requests] = None, verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → NLATool

Instantiate the tool from the specified path and method.

classmethod from_open_api_endpoint_chain(chain: OpenAPIEndpointChain, api_title: str) → NLATool

Convert an endpoint chain to an API endpoint tool.

invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any

validator raise_deprecation  »  all fields

Raise deprecation warning if callback_manager is used.

run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

Run the tool.

property args: dict

The tool’s input arguments.

property is_single_input: bool

Whether the tool only accepts a single input.

model Config

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True

extra = 'forbid'