bluesky-overhaul VS qdrant

Bluesky Social

bluesky is attempting it. We'll see how it goes.

It helps, but no. Go to https://bsky.app/ and click the link to join the wait list. Some people got in within a few days, some it took months.

And fresh out the door, now also bsky.app!

Bluesky Social if you know someone with an invite. https://bsky.app/

Sign up for beta at https://bsky.app/. 🌐💬💙

For those not getting the context(like me), this seems to be about the Bluesky Social(https://bsky.app/), a twitter alternative.

>Jay Graber currently serves as the company's CEO, while co-founder Jack Dorsey sits on its board of directors.

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.