text: The result is a website transformed into clean structured data (like JSON or CSV), ready for your application.This covers how to extract HTML documents from a list of URLs using the Diffbot extract API, into a document format that we can use downstream.urls = [    "https://python.langchain.com/en/latest/index.html",]The Diffbot Extract API Requires an API token. Once you have it, you can extract the data.Read instructions how to get the Diffbot API Token.import osfrom langchain.document_loaders import DiffbotLoaderloader = DiffbotLoader(urls=urls, api_token=os.environ.get("DIFFBOT_API_TOKEN"))With the .load() method, you can see the documents loadedloader.load()    [Document(page_content='LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:\nBe data-aware: connect a language model to other sources of data\nBe agentic: allow a language model to interact with its environment\nThe LangChain framework is designed with the above principles in mind.\nThis is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.\nGetting Started\nCheckout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.\nGetting Started Documentation\nModules\nThere are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity:\nModels: The various model types and model integrations LangChain supports.\nPrompts: This includes prompt management, prompt optimization, and prompt serialization.\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for