**Description:**
I've added a new use-case to the Web scraping docs. I also fixed some
typos in the existing text.
---------
Co-authored-by: davidjohnbarton <41335923+davidjohnbarton@users.noreply.github.com>
"Related to scraping, we may want to answer specific questions using searched content.\n",
"\n",
"We can automate the process of [web research](https://blog.langchain.dev/automating-web-research/) using a retriver, such as the `WebResearchRetriever` ([docs](https://python.langchain.com/docs/modules/data_connection/retrievers/web_research)).\n",
"We can automate the process of [web research](https://blog.langchain.dev/automating-web-research/) using a retriever, such as the `WebResearchRetriever` ([docs](https://python.langchain.com/docs/modules/data_connection/retrievers/web_research)).\n",
"* Here's a [app](https://github.com/langchain-ai/web-explorer/tree/main) that wraps this retriver with a lighweight UI."
]
},
{
"cell_type": "markdown",
"id": "312c399e",
"metadata": {},
"source": [
"## Question answering over a website\n",
"\n",
"To answer questions over a specific website, you can use Apify's [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor, which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,\n",
"and extract text content from the web pages.\n",
"\n",
"In the example below, we will deeply crawl the Python documentation of LangChain's Chat LLM models and answer a question over it.\n",