Coral VS faiss

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.

There are also FAISS binary indexes[0], so it'd be great to compare binary index vs binary index. Otherwise it seems a little misleading to say it is a FAISS vs not FAISS comparison, since really it would be a binary index vs not binary index comparison. I'm not too familiar with binary indexes, so if there's a significant difference between the types of binary index then it'd be great to explain what that is too.

[0] https://github.com/facebookresearch/faiss/wiki/Binary-indexe...

Or just use FAISS https://github.com/facebookresearch/faiss

You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss

Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.

The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982

According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.

Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.

Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.

My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.

I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get

https://github.com/facebookresearch/faiss

up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.

I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.

As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.

But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...

https://github.com/facebookresearch/faiss