> But I mean using a Vector store isn't just a matter of generate some embeddings, slap it in a storage, and then start doing searches like cosine similarity, hoping for the best.

That is 90% of the work with RAG. RAG requires a surprising amount of QA to ensure "the best" is sufficient for your use case.

For clarification, cosine similarity is a metric not a search type, and people like it since a) it allows for more defined heuristics since cosine similarity is limited to [-1, 1] and b) it's computationally efficient (dot product, as with Euclidian distance) if the vectors are unit-normalized beforehand.

There are indeed multiple search types but HNSW is the best 99% of the time for both performance and latency, and is implemented in almost every modern major vector store.