langchain.tools.zapier.tool.ZapierNLAListActions¶
- class langchain.tools.zapier.tool.ZapierNLAListActions(*, name: str = 'ZapierNLA_list_actions', description: str = 'A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}This tool returns a list of the user\'s exposed actions.', 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, api_wrapper: ZapierNLAWrapper = None)[source]¶
Bases:
BaseTool- Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain a list of action objects:
- [{
“id”: str, “description”: str, “params”: Dict[str, str]
}]
params will always contain an instructions key, the only required param. All others optional and if provided will override any AI guesses (see “understanding the AI guessing flow” here: https://nla.zapier.com/docs/using-the-api#ai-guessing)
- Parameters
None –
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param api_wrapper: langchain.utilities.zapier.ZapierNLAWrapper [Optional]¶
- 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 description: str = 'A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}This tool returns a list of the user\'s exposed actions.'¶
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 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 = 'ZapierNLA_list_actions'¶
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.
- 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¶
- property is_single_input: bool¶
Whether the tool only accepts a single input.