Coral VS qdrant

The Coral Project [0] (commenting platform used on Washington Post, New York Times, The Verge) uses an Apache 2.0 license [1]. Which doesn't seem to have prevented it from raking in big SaaS customers.

A lot of people worry about copy-cat services, but it's kind of rare that someone will be able to compete with you as the original in hosting your own service as well as you can. Especially when you consider support and maintenance requirements of a new product you aren't personally developing.

I could see copy-cat services being more of an issue in the late stage of a product though? When everyone knows lots about how to stand it up and use it?

[0] https://coralproject.net/

The Coral Project was created inline with this initiative. They have lots of guides that provide some of the research that was conducted: https://coralproject.net/

For comment system, I choose Coral Project Talk because it could use Akismet and Google Perspective API for reducing spam and harassment. I also need to think about the remove comments when user delete their account (GDPR stuff). Coral Talk has the above functions in the UI.

Another great API that could be self-hosted is Coral. It’s a commenting platform where users can leave online comments. It’s received contributions from over 40 people on Github. It has a good-first-issue tag and also offers a contribution guide.

Yup! We do a Typescript/Node.js/GraphQL back-end with React/Relay/Typescript on the front end.

https://github.com/coralproject/talk

It's pretty nice having the whole code base share types, syntax, structure, etc.

Coral is poorly advertised outside it's ecosystem, but should be considered. https://github.com/coralproject/talk

See https://docs.coralproject.net/coral/v5/integrating/cms/ to get an idea of it's use.

Our highest tier comment system is quite powerful, and is based off Coral Talk by Vox. For beginners like yourself, if we allowed users to integrate Disqus on all tiers, would that alleviate your concerns with using Storipress?

If you need some inspiration, we added support for server caching of responses on Coral: https://github.com/coralproject/talk/blob/develop/src/core/server/app/middleware/graphql/apolloServer.ts#L85-L88

I've seen some examples in which people embed Discourse discussions.

There's also Coral (https://github.com/coralproject/talk) which used to be Mozilla + Vox project before Mozilla handed it over to Vox completely, but I have no experience with 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.