rum VS pgvector

(Note that Rum is also a React wrapper, but you don't have to use that part of it; you can simply use it for static rendering of HTML.)

https://github.com/tonsky/rum

I love React as long as it has a thin skim of clojurescript over top. Rum is the underdog compared to reagent but is still my weapon of choice - https://github.com/tonsky/rum

Was disillusioned when I had to dive into a pure js project using it.

The real benefit, I think, is that you get the well established Clojure idioms around isolating and managing mutable state.

State is stored in a Atom, which is atomically mutated, and reactive components essentially 'subscribe' to updates upon that atom to re render.

The mutations can be handled centrally by a message queue, but really, event sourcing like that is not always needed.

We've been using re-om during the last 6 months and it has really made our lives much easier. Before open-sourcing it, we decided to extract from re-om the code that was independent of any view technology. This code is now part of reffectory and it might be used as the base for creating frameworks similar to re-om for other view technologies, like for example rum, or even for pure Clojure projects.

I've had an in-browser animated meme editor in the freezer for a few years now:

https://www.ultime.me/

The idea came when I wanted to make a simple animated meme, but found it exceedingly frustrating to caption a simple animated gif with nice text options (like outlines). Over time, it's grown to have full keyframe animation for all text and image/video clip attributes, so it is actually pretty capable short of using a desktop video editing/fx package.

That said, the UX is bad and I should feel bad :) . I made the deliberate choice up front to focus on the underlying data model and internal APIs rather than polishing the UI - as such, it is very much an engineer interface. It would be more usable with some demo videos or call-to-action helpers for new users, but really the UX just needs reworked. Especially around animation/keyframing.

On the bright side, the clean data model and content addressable assets leave the path clear to add things like collaborative multi-user meme editing, git like meme-forking(and diffing?), and so forth.

Started it about 3 years ago when I had a period of mostly free time to play. It's been idle for a long time due to starting a family and getting consulting momentum, but I'm intending to make the time this year to polish the UX to the point of general usability and experiment with promotion/monetization. Failing that, I'll probably just open source it and write a couple of blog posts about the internals.

It is more or less a static web app, with no server side function short of some optional stats collection. It's written in Clojurescript/Clojure and uses https://github.com/tonsky/rum as a React wrapper and

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:

pgvector has you covered: https://github.com/pgvector/pgvector