I'm planning to support MacOS, the only issue is with the encoders that I'm using now, I will probably work more on it next week to try to make a release that works on MacOS as well. Thanks !
Hi, there are no LLMs involved, it is all local and an embedding (vector representation) of the data is created and then that is used for search later, nothing is sent to cloud from your files and there are no local LLMs running as well, only the encoders (I use the Perception Encoder from Meta released a few weeks ago).
This is quite different than LanceDB. In VectorVFS I'm using the inodes directly to store the embeddings, there is no external file with metadata and db, the db is your filesystem itself, that's the key difference.
Hi, I think Rust won't bring much benefit here to be honest, the bottleneck is mainly the model and model loading. It would probably be a nightmare to load these models from Rust, I would have to use torch bindings and then convert everything from the preprocessing already in Python to Rust.
Hi, not sure if I understood what you meant by opaque embeddings as well, but the reason why files surface or not is due to the similarity score (which is basically the dot product of embeddings).
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).
Thanks, I'm working on implementing the commands to clean the embeddings (you can now do that with Linux xattr command-line tool). I'm supporting CPU or GPU (NVIDIA) for the encoders and it only supports Linux at the moment.
Thanks. There is a bit of a nuance there, for example: you can build an index in first pass which will indeed be linear, but then later keep it in an open prompt for subsequent queries, I'm planning to implement that mode soon. But agree, it is not intended to search 10 million files, but you seldom have this use case in local use anyways.
I'm not sure I agree about the data manifolds being too rigid. When we look at the quality score-based generative models and diffusion we can see a clear evidence of how flexible these representations are. We could say the same about statistical manifolds, but the fact that the Fisher is the fundamental metric tensor for the statistical manifold is a fundamental piece of many 1st and 2nd order optimizers today.
I find it interesting how everyone ignore EuclidesDB (https://euclidesdb.readthedocs.io) which came before Milvus and others in 2018, it is free and open-source. Same for all presentations from major DBs.