I'd say the more important consideration is "consistency" between incoming query input and stored vectors. I have a huge vector database that gets updated/rege

I'd say the more important consideration is "consistency" between incoming query input and stored vectors. I have a huge vector database that gets updated/regenerated from a personal knowledge store (markdown library). Since the user is most likely to input a comparison query in the form of a question "Where does X factor into the Y system?" - I use a small 7b parameter LLM to pregenerate a list of a dozen possible theoretical questions a user might pose to a given embedding chunk. These are saved as 1536 dimension sized embeddings into the vector database (Qdrant) and linked to the chunks. The real question you need to ask is - what's the input query that you'll be comparing to the embeddings? If it's incoming as structured, then store structured, etc. I've also seen (anecdotally) similarity degradation for smaller chunks as well - so keep that in mind as well.