noosphere VS qdrant

Subconscious | Full-time | REMOTE - Hiring globally, current team is San Francisco, Washington D.C. and Brisbane

At Subconscious, we’re building new infrastructure for an internet of ideas.

Noosphere is a next generation network protocol; a worldwide content graph (no blockchain though).

Subconscious is a social note-taking app powered by Noosphere and AI.

Our founders are former browser engineers from Google and Mozilla. We are a small, funded team of four.

We're looking for a few more folks to help bring our work into the world. Specifically, we are hiring:

- DevOps: you should know the ins and outs of cloud infrastructure orchestration and its interplay with deployed applications and services

- Cryptography/Privacy: help us make strong guarantees about public and private information, and bring user legibility to content provenance and authority

Some topical links:

- Subconscious Blog: https://subconscious.substack.com/

- Noosphere Project: https://github.com/subconsciousnetwork/noosphere

If you are interested, please drop us a line to introduce yourself at [email protected]

Source code: https://github.com/subconsciousnetwork/noosphere

Currently we have a CLI that demonstrates multi-device synchronization in principle. You can install it using `cargo install noosphere-cli` (it installs a binary called "orb"). We also have pre-built binaries of orb for x86_64 Linux that you can find on our releases on Github (e.g., https://github.com/subconsciousnetwork/noosphere/releases/ta...). We'll be expanding our pre-built binaries to include other platforms soon.

Also, we have recently released a Swift package that gives a path to easily install our project as a dependency in XCode. We're turning the corner on offering the same FFI backing implementation as a TypeScript + WASM package, too. But, as I mentioned it's early days for us, so set your expectations accordingly.

The best way to stay up to date with new things you can do is join our Discord. I make regular announcements there about our technical progress.

Plug: I'm working with the author on a decentralized protocol for networked thought and a companion browser called Subconscious. We're a very young project and just getting off the ground.

If you're interested in topics like the one in the article, all our work is open source and we love getting into it with folks on Github or our Discord (link in the README): https://github.com/subconsciousnetwork/noosphere

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

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.