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Curious about the choice of FAISS. It's a bit older now, and there are many more options for creating and selecting embeddings. Does FAISS still offer some advantages?

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Hi! I'm the guy who made Dot. I remember experimenting with a few different vector stores in the early stages of the project but decided to settle with FAISS. I mainly chose it because it made it easy to perform the whole embedding process locally and also because it allows to merge vector stores which is what I use to load multiple types of documents at once. But I am definately not an expert on the topic and would really appreciate suggestions on other alternatives that might work better! :)

What options do you think work better?

I’m trying build an exhaustive list of realistic options. See the spreadsheet here

https://shelbyjenkins.github.io/blog/retrieval-is-all-you-ne...

I don’t have an opinion, just wondering why they didn’t choose other another option such as Sentence Embeddings, OpenAI embeddings, etc.

FAISS can be used with OpenAI embeddings (and any other embedding model).

FAISS is technology for fast indexed similarity vector search - using it is an independent decision from which model you use to create those vectors.

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