langchain.tools.zapier.tool.ZapierNLARunAction¶

class langchain.tools.zapier.tool.ZapierNLARunAction(*, name: str = '', 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, api_wrapper: ZapierNLAWrapper = None, action_id: str, params: Optional[dict] = None, base_prompt: 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}', zapier_description: str, params_schema: Dict[str, str] = None)[source]¶

Bases: BaseTool

Executes an action that is identified by action_id, must be exposed

(enabled) by the current user (associated with the set api_key). Change your exposed actions here: https://nla.zapier.com/demo/start/

The return JSON is guaranteed to be less than ~500 words (350 tokens) making it safe to inject into the prompt of another LLM call.

Parameters

  • action_id – a specific action ID (from list actions) of the action to execute (the set api_key must be associated with the action owner)

  • instructions – a natural language instruction string for using the action (eg. “get the latest email from Mike Knoop” for “Gmail: find email” action)

  • params – a dict, optional. Any params provided will override AI guesses from instructions (see “understanding the AI guessing flow” here: https://nla.zapier.com/docs/using-the-api#ai-guessing)

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 action_id: str [Required]¶
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 base_prompt: 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}'¶
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 = ''¶

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 = ''¶

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

param params: Optional[dict] = None¶
param params_schema: Dict[str, str] [Optional]¶
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.

param zapier_description: str [Required]¶
__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.

validator set_name_description  »  all fields[source]¶
property args: dict¶
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'¶

Examples using ZapierNLARunAction¶