marqo VS Coral

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo

Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo

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.