[turnsout]

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?

[alexpinel]

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! :)

[simonw]

What options do you think work better?

[J_Shelby_J]

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

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

[turnsout]

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

[simonw]

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.