Coral VS pgvector

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.

# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"

When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.

For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.

pgvector supports dense vector search well, but it does not have plan to support sparse vector.

There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455

I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)

Official GitHub repository of the pgvector extension

pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.

The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: