Source code for langchain_community.utilities.apify

from typing import TYPE_CHECKING, Any, Callable, Dict, Optional

from langchain_core.documents import Document
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.utils import get_from_dict_or_env

if TYPE_CHECKING:
    from langchain_community.document_loaders import ApifyDatasetLoader


[docs]class ApifyWrapper(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. """ apify_client: Any apify_client_async: Any @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate environment. Validate that an Apify API token is set and the apify-client Python package exists in the current environment. """ apify_api_token = get_from_dict_or_env( values, "apify_api_token", "APIFY_API_TOKEN" ) try: from apify_client import ApifyClient, ApifyClientAsync client = ApifyClient(apify_api_token) if httpx_client := getattr(client.http_client, "httpx_client"): httpx_client.headers["user-agent"] += "; Origin/langchain" async_client = ApifyClientAsync(apify_api_token) if httpx_async_client := getattr( async_client.http_client, "httpx_async_client" ): httpx_async_client.headers["user-agent"] += "; Origin/langchain" values["apify_client"] = client values["apify_client_async"] = async_client except ImportError: raise ImportError( "Could not import apify-client Python package. " "Please install it with `pip install apify-client`." ) return values
[docs] def call_actor( self, 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": """Run an Actor on the Apify platform and wait for results to be ready. Args: actor_id (str): The ID or name of the Actor on the Apify platform. run_input (Dict): The input object of the Actor that you're trying to run. 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: ApifyDatasetLoader: A loader that will fetch the records from the Actor run's default dataset. """ from langchain_community.document_loaders import ApifyDatasetLoader actor_call = self.apify_client.actor(actor_id).call( run_input=run_input, build=build, memory_mbytes=memory_mbytes, timeout_secs=timeout_secs, ) return ApifyDatasetLoader( dataset_id=actor_call["defaultDatasetId"], dataset_mapping_function=dataset_mapping_function, )
[docs] async def acall_actor( self, 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": """Run an Actor on the Apify platform and wait for results to be ready. Args: actor_id (str): The ID or name of the Actor on the Apify platform. run_input (Dict): The input object of the Actor that you're trying to run. 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: ApifyDatasetLoader: A loader that will fetch the records from the Actor run's default dataset. """ from langchain_community.document_loaders import ApifyDatasetLoader actor_call = await self.apify_client_async.actor(actor_id).call( run_input=run_input, build=build, memory_mbytes=memory_mbytes, timeout_secs=timeout_secs, ) return ApifyDatasetLoader( dataset_id=actor_call["defaultDatasetId"], dataset_mapping_function=dataset_mapping_function, )
[docs] def call_actor_task( self, 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": """Run a saved Actor task on Apify and wait for results to be ready. Args: task_id (str): The ID or name of the task on the Apify platform. task_input (Dict): The input object of the task that you're trying to run. Overrides the task's saved input. 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: ApifyDatasetLoader: A loader that will fetch the records from the task run's default dataset. """ from langchain_community.document_loaders import ApifyDatasetLoader task_call = self.apify_client.task(task_id).call( task_input=task_input, build=build, memory_mbytes=memory_mbytes, timeout_secs=timeout_secs, ) return ApifyDatasetLoader( dataset_id=task_call["defaultDatasetId"], dataset_mapping_function=dataset_mapping_function, )
[docs] async def acall_actor_task( self, 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": """Run a saved Actor task on Apify and wait for results to be ready. Args: task_id (str): The ID or name of the task on the Apify platform. task_input (Dict): The input object of the task that you're trying to run. Overrides the task's saved input. 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: ApifyDatasetLoader: A loader that will fetch the records from the task run's default dataset. """ from langchain_community.document_loaders import ApifyDatasetLoader task_call = await self.apify_client_async.task(task_id).call( task_input=task_input, build=build, memory_mbytes=memory_mbytes, timeout_secs=timeout_secs, ) return ApifyDatasetLoader( dataset_id=task_call["defaultDatasetId"], dataset_mapping_function=dataset_mapping_function, )