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 variableAPIFY_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
- 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
- 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
- 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
- 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 ¶