Dependency Injector VS towhee

Nothing to do with the nature of the language, but with the nature of the program.

If you're writing a few line script, you don't need a DI container. Once your program gets large, it becomes extremely messy without one. It's no surprise projects like [1] exist.

[1] https://github.com/ets-labs/python-dependency-injector

Totally agree with this. And I’ve found this pattern pairs really well with https://python-dependency-injector.ets-labs.org/

dependency_injector: https://github.com/ets-labs/python-dependency-injector

As projects continue to grow, its recommended to utilise a dependency injection framework to “inject” these dependencies, such as Dependency Injector, to inject dependency arguments automatically ✨.

One approach is to pass this config as a variable to every class it is required, which I dont prefer. Another option is to annotate the config class as singleton and create the config object at every place where I need them. I also came across this library called Dependency_Injector. https://python-dependency-injector.ets-labs.org/ This seems a bit heavy weight for my use case though. I am looking forward to know how other solve this problem

Dependency Injector

dependency-injector (docs) is python library that provides a framework which enables you to implement DI and IoC in Python.

As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.

A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).

towhee has an example to do exactly what you want with CLIP[1].

[0] - https://towhee.io/

[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...

The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.

Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).

Benchmarking the models with towhee is as simple as:

The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.

I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:

Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.

You can check the result from the tutorial.