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I was mulling over the idea of building keyword image search (say, with CLIP based embeddings). However I'm not really sure the cost, or whether or not this is the best solution. Do you have any case studies about large deployments of this software, and what the upper limits of scale might be?

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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]. For icing on the cake the example notebook includes deployment of the model with Nvidia Triton Server[2], which IMO is hands down the best way to actually deploy an ML model[3].

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

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

[2] - https://github.com/triton-inference-server/server

[3] - https://news.ycombinator.com/item?id=35199418#35200266

Don't start with mullivus if you're learning. Too much yak shaving. Try https://github.com/criteo/autofaiss.

Also, TBH, it is a lot cheaper to run a simple faiss index.

Milvus standalone is pretty neat. I made it successfully run on the first try.

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