sqlmodel VS jina

Seems like this is a known bug in SQLModel: https://github.com/tiangolo/sqlmodel/issues/10

I have not used it myself but the creator of fastapi has made https://github.com/tiangolo/sqlmodel

I have already posted this question in Stack Overflow and GitHub and have been ignored in both 😢. You guys are my last hope.

I had a requirement to process and validate large payloads of JSON concurrently for a web service, initially I implemented it using jsonschema and fastjsonschema but I found the whole JSON Schema Specification to be confusing at times and on top of that wanted better performance. Albeit there are ways to compile/cache the schema, I wanted to move away from the schema specification so I wrote a validation library inspired by the design of tiangolo/sqlmodel (type hints) to solve this problem easier.

Github Repository

I would say as long as your current solution works and is easy to maintain keep it. If you want to switch I would recommend FastAPI, it is new(ish), but definitely old enough to have been tested and used in a large variety of production usecases. In your case it might be interesting to have a look at SQLModel (works with FastAPI, same author), especially if the API endpoints match closely to the database objects. https://github.com/tiangolo/sqlmodel

SQLModel.

Sounds similar to https://github.com/tiangolo/sqlmodel

There was a lot of hype and excitement around the release of SQLModel, a Pydantic + SQLAlchemy hybrid Model library with native integration for FastAPI. I pulled it out just now and there hasn't been any update beyond the initial anouncment on August 25th.

Jina mainly because of their use of neural networks and AI.

Jina - It’s an open-source project where you can build search engines. Well maybe not no code but it claims that you only need a few lines of code for creating projects. The project supports semantic, text, image, audio, and video search. What I’m also interested in is with their neural search and generative AI. I’m also interested in the amount of github repo that they have. I have this on my radar since this is also something I was interested in.

Do you guys have any ideas how we can match images on our database? We’re working on a project that about matching images on our database. We were trying to use SIFT and some other similar methods, but for some reason, nothing doesn’t seem to be working that well. Does anyone have any suggestions for the most effective way to do this? Maybe some open-source solutions like HuggingFace or Jina AI? We just want to make sure our image matching is correct and that part’s been a bit of a struggle on our part.

The tech lets you find 3D models without sifting through tons of text - An information retrieval framework does the heavy lifting and compares models to each other, no descriptions or keywords needed.

Jina AI -They offer a neural search solution that can help build smarter, more efficient search engines. They also have a list of cool github repos that you can check out. Similar to Vertex AI, they have image classification tools, NLPs, fine tuners etc.

Well, it's not exactly a new feature, but we've been working on early support for DocArray v2 in Jina.

Jina let’s you serve all of your models through the same Gateway while deploying them as individual microservices. You can also tie your models together in a pipeline if needed. Also some nice ML focussed features such as dynamic batching.