How does @Web work?

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1

Love the @Web feature! How does it work under the hood? What APIs are you calling? Just curious :grin:

2

You have probably already seen this and are seeking more detailed information, but just adding to the topic for reference:

https://docs.cursor.com/context/@-symbols/@-web

Also, for reference, I asked Cursor (using gpt-4o):

Hello - i am using Cursor (the fork of VS Code).  

It has a feature known as `@` symbols.  

One of the symbols that can be called is `@Web`.  

There is some documentation about it here:

@Web https://docs.cursor.com/context/@-symbols/@-web

Do you know how it works 'under the hood'?  

In other words, what sort of technologies it would be using to get the information?  

If you don't know, would you be able to have a guess?

It replied with:

The @Web symbol in Cursor (a fork of VS Code) is designed to fetch and display web content directly within the editor. While the exact implementation details are not publicly documented, we can make an educated guess about the technologies and methodologies it might be using based on common practices in similar tools.

Possible Technologies and Methodologies

  1. Web Scraping:

  • Libraries: It might use libraries like axios or fetch to make HTTP requests to web pages.

  • Parsing: Libraries like cheerio (for Node.js) could be used to parse HTML and extract relevant content.

  1. APIs:

  • Public APIs: It could leverage public APIs provided by websites to fetch structured data.

  • Custom APIs: Cursor might have its own backend service that interacts with various web services and APIs to fetch and process data.

  1. Browser Automation:

  • Puppeteer: A headless browser automation tool like Puppeteer could be used to render web pages and extract content dynamically.

  • Selenium: Another option could be Selenium, which is often used for web testing but can also be used for scraping dynamic content.

  1. Natural Language Processing (NLP):

  • Text Extraction: NLP techniques might be used to extract and summarize relevant information from the fetched web content.

  • Entity Recognition: Tools like spaCy or NLTK could be used to identify and extract key entities from the text.

  1. Integration with Search Engines:
  • Search APIs: It might use search engine APIs (like Google Custom Search API) to fetch search results and then process the returned data.

Example Workflow

  1. User Input: The user invokes the @Web symbol with a query.

  2. HTTP Request: The tool makes an HTTP request to a relevant web page or API.

  3. Content Extraction: The HTML content is parsed, and relevant data is extracted using a library like cheerio.

  4. Data Processing: The extracted data is processed, possibly using NLP techniques to summarize or highlight key information.

  5. Display: The processed information is displayed within the editor.

References

This is a speculative overview based on common practices in web content extraction and processing. The actual implementation in Cursor might differ.

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3

"spaCy’!? what a throwback!

Secret sauce it is, I suppose… ty for this :slightly_smiling_face:

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