langchain_community.utilities.apify.ApifyWrapper¶

class langchain_community.utilities.apify.ApifyWrapper[source]¶

Bases: BaseModel

Wrapper around Apify. To use, you should have the apify-client python package installed, and the environment variable APIFY_API_TOKEN set with your API key, or pass apify_api_token as a named parameter to the constructor.

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 apify_client: Any = None¶
param apify_client_async: Any = None¶
async acall_actor(actor_id: str, run_input: Dict, dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → ApifyDatasetLoader[source]¶

Run an Actor on the Apify platform and wait for results to be ready. :param actor_id: The ID or name of the Actor on the Apify platform. :type actor_id: str :param run_input: The input object of the Actor that you’re trying to run. :type run_input: Dict :param dataset_mapping_function: A function that takes a single

dictionary (an Apify dataset item) and converts it to an instance of the Document class.

Parameters

  • build (str, optional) – Optionally specifies the actor build to run. It can be either a build tag or build number.

  • memory_mbytes (int, optional) – Optional memory limit for the run, in megabytes.

  • timeout_secs (int, optional) – Optional timeout for the run, in seconds.

Returns

A loader that will fetch the records from the

Actor run’s default dataset.

Return type

ApifyDatasetLoader

async acall_actor_task(task_id: str, task_input: Dict, dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → ApifyDatasetLoader[source]¶

Run a saved Actor task on Apify and wait for results to be ready. :param task_id: The ID or name of the task on the Apify platform. :type task_id: str :param task_input: The input object of the task that you’re trying to run.

Overrides the task’s saved input.

Parameters

  • dataset_mapping_function (Callable) – A function that takes a single dictionary (an Apify dataset item) and converts it to an instance of the Document class.

  • build (str, optional) – Optionally specifies the actor build to run. It can be either a build tag or build number.

  • memory_mbytes (int, optional) – Optional memory limit for the run, in megabytes.

  • timeout_secs (int, optional) – Optional timeout for the run, in seconds.

Returns

A loader that will fetch the records from the

task run’s default dataset.

Return type

ApifyDatasetLoader

call_actor(actor_id: str, run_input: Dict, dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → ApifyDatasetLoader[source]¶

Run an Actor on the Apify platform and wait for results to be ready. :param actor_id: The ID or name of the Actor on the Apify platform. :type actor_id: str :param run_input: The input object of the Actor that you’re trying to run. :type run_input: Dict :param dataset_mapping_function: A function that takes a single

dictionary (an Apify dataset item) and converts it to an instance of the Document class.

Parameters

  • build (str, optional) – Optionally specifies the actor build to run. It can be either a build tag or build number.

  • memory_mbytes (int, optional) – Optional memory limit for the run, in megabytes.

  • timeout_secs (int, optional) – Optional timeout for the run, in seconds.

Returns

A loader that will fetch the records from the

Actor run’s default dataset.

Return type

ApifyDatasetLoader

call_actor_task(task_id: str, task_input: Dict, dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → ApifyDatasetLoader[source]¶

Run a saved Actor task on Apify and wait for results to be ready. :param task_id: The ID or name of the task on the Apify platform. :type task_id: str :param task_input: The input object of the task that you’re trying to run.

Overrides the task’s saved input.

Parameters

  • dataset_mapping_function (Callable) – A function that takes a single dictionary (an Apify dataset item) and converts it to an instance of the Document class.

  • build (str, optional) – Optionally specifies the actor build to run. It can be either a build tag or build number.

  • memory_mbytes (int, optional) – Optional memory limit for the run, in megabytes.

  • timeout_secs (int, optional) – Optional timeout for the run, in seconds.

Returns

A loader that will fetch the records from the

task run’s default dataset.

Return type

ApifyDatasetLoader

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_orm(obj: Any) → Model¶
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 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¶
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¶
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¶

Examples using ApifyWrapper¶