langchain_core.tools
.Tool¶
- class langchain_core.tools.Tool[source]¶
Bases:
BaseTool
Tool that takes in function or coroutine directly.
Initialize tool.
- param args_schema: Optional[Type[pydantic.main.BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
- param callback_manager: Optional[langchain_core.callbacks.base.BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
- param callbacks: Optional[Union[List[langchain_core.callbacks.base.BaseCallbackHandler], langchain_core.callbacks.base.BaseCallbackManager]] = 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: Optional[Callable[[...], str]] = None¶
The function to run when the tool is called.
- param handle_tool_error: Optional[Union[bool, str, Callable[[langchain_core.tools.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 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 abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output] ¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying runnable uses an API which supports a batch mode.
- async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) Any [source]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
- 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, run_name: Optional[str] = None, **kwargs: Any) Any ¶
Run the tool asynchronously.
- assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) RunnableSerializable[Any, Any] ¶
Assigns new fields to the dict output of this runnable. Returns a new runnable.
- async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ¶
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]] ¶
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
- Parameters
input – The input to the runnable.
config – The config to use for the runnable.
diff – Whether to yield diffs between each step, or the current state.
with_streamed_output_list – Whether to yield the streamed_output list.
include_names – Only include logs with these names.
include_types – Only include logs with these types.
include_tags – Only include logs with these tags.
exclude_names – Exclude logs with these names.
exclude_types – Exclude logs with these types.
exclude_tags – Exclude logs with these tags.
- async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ¶
Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.
- batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output] ¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying runnable uses an API which supports a batch mode.
- bind(**kwargs: Any) Runnable[Input, Output] ¶
Bind arguments to a Runnable, returning a new Runnable.
- config_schema(*, include: Optional[Sequence[str]] = None) Type[BaseModel] ¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.
- Parameters
include – A list of fields to include in the config schema.
- Returns
A pydantic model that can be used to validate config.
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) RunnableSerializable[Input, Output] ¶
- configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output] ¶
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model
- Returns
new model instance
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny ¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- classmethod from_function(func: Optional[Callable], name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, coroutine: Optional[Callable[[...], Awaitable[Any]]] = None, **kwargs: Any) Tool [source]¶
Initialize tool from a function.
- classmethod from_orm(obj: Any) Model ¶
- get_graph(config: Optional[RunnableConfig] = None) Graph ¶
Return a graph representation of this runnable.
- get_input_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
The tool’s input schema.
- classmethod get_lc_namespace() List[str] ¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “openai”]
- get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) str ¶
Get the name of the runnable.
- get_output_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
- Parameters
config – A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate output.
- get_prompts(config: Optional[RunnableConfig] = None) List[BasePromptTemplate] ¶
- invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) Any ¶
Transform a single input into an output. Override to implement.
- Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.
- Returns
The output of the runnable.
- classmethod is_lc_serializable() bool ¶
Is this class serializable?
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod lc_id() List[str] ¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path to the object.
- map() Runnable[List[Input], List[Output]] ¶
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- classmethod parse_obj(obj: Any) Model ¶
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- pick(keys: Union[str, List[str]]) RunnableSerializable[Any, Any] ¶
Pick keys from the dict output of this runnable. Returns a new runnable.
- pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) RunnableSerializable[Input, Other] ¶
Compose this runnable with another object to create a RunnableSequence.
- 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, run_name: Optional[str] = None, **kwargs: Any) Any ¶
Run the tool.
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ¶
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- to_json() Union[SerializedConstructor, SerializedNotImplemented] ¶
- to_json_not_implemented() SerializedNotImplemented ¶
- transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ¶
Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model ¶
- with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) Runnable[Input, Output] ¶
Bind config to a Runnable, returning a new Runnable.
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) RunnableWithFallbacksT[Input, Output] ¶
Add fallbacks to a runnable, returning a new Runnable.
- Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
- Returns
A new Runnable that will try the original runnable, and then each fallback in order, upon failures.
- with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) Runnable[Input, Output] ¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) Runnable[Input, Output] ¶
Create a new Runnable that retries the original runnable on exceptions.
- Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time between retries
stop_after_attempt – The maximum number of attempts to make before giving up
- Returns
A new Runnable that retries the original runnable on exceptions.
- with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) Runnable[Input, Output] ¶
Bind input and output types to a Runnable, returning a new Runnable.
- property InputType: Type[langchain_core.runnables.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
- property OutputType: Type[langchain_core.runnables.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
- property args: dict¶
The tool’s input arguments.
- property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
- property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
- property is_single_input: bool¶
Whether the tool only accepts a single input.
- property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
- property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
- For example,
{“openai_api_key”: “OPENAI_API_KEY”}
- name: str = None¶
The unique name of the tool that clearly communicates its purpose.
- property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.