3 ms·

Hi, it is quite different, there is no LLM involved, we can certainly use it for a RAG for example, but what is currently implemented is basically a way to gene

by perone 1y ago

Hi, it is quite different, there is no LLM involved, we can certainly use it for a RAG for example, but what is currently implemented is basically a way to generate embeddings (vector representation) which are then used for search later, it is all offline and local (no data is ever sent to cloud from your files).

jlhawn 1y ago
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-search-command https://vectorvfs.readthedocs.io/en/latest/usage.html#vfs-se...
- deleted 1y ago
  [deleted]
- freeamz 1y ago
  Yeah this kind of setup is indefinitely scaleable, but not searchable without out a meta db/index keeping track of all the nodes.
pilooch 1y ago
Using it for a RAG is smart indeed, especially with a multimodal encoder (vision-rag), as the implementation would be straightforward from what you already have.

3 ms·

Hi, it is quite different, there is no LLM involved, we can certainly use it for a RAG for example, but what is currently implemented is basically a way to gene

by perone 1y ago

jlhawn 1y ago
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-search-command https://vectorvfs.readthedocs.io/en/latest/usage.html#vfs-se...
- deleted 1y ago
  [deleted]
- freeamz 1y ago
  Yeah this kind of setup is indefinitely scaleable, but not searchable without out a meta db/index keeping track of all the nodes.
pilooch 1y ago
Using it for a RAG is smart indeed, especially with a multimodal encoder (vision-rag), as the implementation would be straightforward from what you already have.