api.video-nodejs-client VS qdrant

7. api.video: Best Complete Toolkit for Integration

I'm currently working on a mobile project that involves watching video content. I switched from using static .mp4 files to HLS video-on-demand streaming with adaptive bitrates - that way the videos should watchable even though users have bad connection speed. I have managed that setup through hosting vidoes on api.video, which gives you a link to the HLS stream.

Here is our website : https://api.video

We are looking for talented GO/PHP developer to join our Api team.

You said Vimeo is not working out for you (hard to believe Vimeo is the cause of your issues and not you). So alternatively then, your best bet is to use Mux.com, Cloudflare Stream, or api.video. They are all kind of similar but specialize in handling streaming video. You will still pay out the nose. Streaming video is expensive in general.

Api. video is one of the fastest-growing startups in the industry.

The tutorial can be broken into three parts. In the first part, you will use api.video’s Node.js Client to create your own livestream service. In the second part, you will create a simple React App to host your livestream service. The UI can be built using any framework you prefer. For this tutorial, we’ll stick with React. Finally, you will connect Zoom to your livestream service for users to be able to view it.

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.

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.