I understand that LLMs aren't involved in generating the embeddings and adding the xattrs. I was just wondering what the value add of this is if there's no other background process (like mds on macOS) which is using it to build a search index.

I guess what I'm asking is: how does VectorVFS enable search besides iterating through all files and iteratively comparing file embeddings with the embedding of a search query? The project description says "efficient and semantically searchable" and "eliminating the need for external index files or services" but I can't think of any more efficient way to do a search without literally walking the entire filesystem tree to look for the file with the most similar vector.

Edit: reading the docs [1] confirmed this. The `vfs search TERM DIRECTORY` command:

> will automatically iterate over all files in the folder, look for supported files and then embed the file or load existing embeddings directly from the filesystem."

[1]: https://vectorvfs.readthedocs.io/en/latest/usage.html#vfs-se...