Semantic document search is one of the core use cases we see in the community (besides Question Answering) and Haystack was pretty much started because we saw that you need much more than just models. It's so much pain to integrate models properly with document storage (e.g. elasticsearch), route requests effectively in larger pipelines or track user feedback in production. Have you tried using DPR or sentence transformers for your case?

Disclaimer: I am one of the maintainers of Haystack:)

I had a client who was using sentence transformers with elasticsearch already. My colleague suggested switching to haystack to enable a larger number of model architectures. Switching over to haystack was pretty straightforward because we just used it as a wrapper around sentence transformers, but I do remember some inconvenience around all the other dependencies that haystack pulled in.

Haystack does a lot more besides just wrapping sentence transformers, and we weren't using the rest of it, so it was just a lot of extra dependencies sitting around taking up disk space and memory (I think we had to go up to a larger instance size). I remember feeling a bit frustrated that the dependencies weren't split up into "core" and "optional" in a more fine-grained way, but maybe most users don't mind and so it doesn't make sense for them to prioritize that?

[edit: looks like there's an open issue related to this: https://github.com/deepset-ai/haystack/issues/1070]

[edit 2: 'JPKab happy to share more about using huggingface and elasticsearch. email is in my profile]

Noted, we've been discussing dependencies internally indeed :) Thanks for the highlight above!!

Yeah I’m using their FAISS document store and QA pipeline to run semantic search over a set of YouTube transcripts. Was easier to set up than Jina AI in my specific use case, and the search results are actually useful. Only real constraint for me is GPU access, creating the embeddings to store in the FAISS index sans a GPU takes an unreasonable amount of time.

Glad it worked for you - thanks for sharing!

Happy to help too - feel free to ask in the community channels as well :)