langchain.agents.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing

class langchain.agents.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing(*, name: str = 'requests_patch', description: str = 'Use this when you want to PATCH content on a website.\nInput to the tool should be a json string with 3 keys: "url", "data", and "output_instructions".\nThe value of "url" should be a string.\nThe value of "data" should be a dictionary of key-value pairs of the body params available in the OpenAPI spec you want to PATCH the content with at the url.\nThe value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the PATCH request creates.\nAlways use double quotes for strings in the json string.', 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, requests_wrapper: TextRequestsWrapper, response_length: Optional[int] = 5000, llm_chain: LLMChain = None)

Bases: BaseRequestsTool, BaseTool

Requests PATCH tool with LLM-instructed extraction of truncated responses.

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 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 = 'Use this when you want to PATCH content on a website.\nInput to the tool should be a json string with 3 keys: "url", "data", and "output_instructions".\nThe value of "url" should be a string.\nThe value of "data" should be a dictionary of key-value pairs of the body params available in the OpenAPI spec you want to PATCH the content with at the url.\nThe value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the PATCH request creates.\nAlways use double quotes for strings in the json string.'

Tool description.

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

Handle the content of the ToolException thrown.

param llm_chain: langchain.chains.llm.LLMChain [Optional]

LLMChain used to extract the response.

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 = 'requests_patch'

Tool name.

param requests_wrapper: TextRequestsWrapper [Required]

param response_length: Optional[int] = 5000

Maximum length of the response to be returned.

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.

model Config

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True

extra = 'forbid'