That's the basis for a search engine. There are plenty of ways : For example you can render the home page of a website to an image. Then run CLIP to get a feat

That's the basis for a search engine. There are plenty of ways : For example you can render the home page of a website to an image. Then run CLIP to get a feature vector. Then use some approximate near neighbor search library like FAISS or HNSWlib to index it. Or you can ask ChatGPT, or a neural network to summarize the webpages into a short description and then into feature vector. Old school approach are things like bags of words for document classification. Then you run a hierarchical clustering algorithm (something like hierarchical k-means). This will allow to present things that are similar but not-so-similar that they are duplicates. Interesting distances between websites are often described by considering which other websites they links to and what other websites link to them. Graph Neural Network allow to build a feature vector based on these links between websites. This is also related to the well known Page Rank algorithm. Finally gathering metadata about websites can also be an interesting axis of similarity : Who owns the site ? How often do they update ? How much money they generate ? How do they generate money ? How big is it ? How fast do they render ? What do people think about the site ? Basically answering the 5Ws about the website and building a database about it, and LLM can help answer those questions automatically (You do a web-search about the site summarize the results, put them in the context of the LLM, and ask the question in a prompt and index the answer.