text: append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of Apify Actorsâ€”serverless cloud programs for varius web scraping, crawling, and data extraction use cases.This notebook shows how to load Apify datasets to LangChain.Prerequisitesâ€‹You need to have an existing dataset on the Apify platform. If you don't have one, please first check out this notebook on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs.#!pip install apify-clientFirst, import ApifyDatasetLoader into your source code:from langchain.document_loaders import ApifyDatasetLoaderfrom langchain.document_loaders.base import DocumentThen provide a function that maps Apify dataset record fields to LangChain Document format.For example, if your dataset items are structured like this:{    "url": "https://apify.com",    "text": "Apify is the best web scraping and automation platform."}The mapping function in the code below will convert them to LangChain Document format, so that you can use them further with any LLM model (e.g. for question answering).loader = ApifyDatasetLoader(    dataset_id="your-dataset-id",    dataset_mapping_function=lambda dataset_item: Document(        page_content=dataset_item["text"], metadata={"source": dataset_item["url"]}    ),)data = loader.load()An example with question answeringâ€‹In this example, we use data from a dataset to answer a question.from langchain.docstore.document import Documentfrom langchain.document_loaders import ApifyDatasetLoaderfrom langchain.indexes import VectorstoreIndexCreatorloader = ApifyDatasetLoader(