oxen-release VS qdrant

We've been working on a data version control system called "oxen" optimized for large unstructured datasets that we are seeing more and more with the advent of many of the generative AI techniques.

Many of these datasets have many many images, videos, audio files, text as well as structured tabular datasets that git or git-lfs just falls flat on.

Would love anyone to kick the tires on it and let us know what you think:

https://github.com/Oxen-AI/oxen-release

The commands are mirrored after git so it is easy to learn, but optimized under the hood for larger datasets.

Super cool! Would love to see an integration with Oxen and their data version control https://github.com/Oxen-AI/oxen-release

We have been working on a data version control tool called Oxen that is tackling many of your needs. Feel free to check it out here:

https://github.com/Oxen-AI/oxen-release#-oxen

Going down your list of requirements, Oxen has:

* Data versioning, similar paradigm to git, but built from the ground up for large ML datasets

We've been working on some open source tooling called "oxen" that was built for large datasets of images, video, audio, text etc. We wanted to solve the exact problem you're flagging here with git.

Feel free to check it out here https://github.com/Oxen-AI/oxen-release#-oxen would love any feedback!

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.

Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals.