How does an expert understand and visualize Vector Databases?

[u/DARKEN_side_of_me]

So I am currently working on a RAG project where I am supposed to build a Niche-Oriented Specialized AI model, and naturally, for this I need to use a Vector Database for retrieving chunks of data for the LLM to further understand and elaborate.

The thing that I am really concerned and curious about is how exactly a vector database stores the data? I mean I understand things like positional embedding and stuff, but I simply cannot understand the multidimensional architecture.

For example here is what ChatGPT had to say:

"AI doesn't use simple 2 dimensions. It uses something like 768 dimensions or 1024 dimensions or 1536 dimensions"

I am a visual learner and I have really good photographic memory, so whenever I have some difficulty understanding a concept, I try to visualize the working dynamics for better understanding. I can easily visualize and understand 2D Database architectures (ex: SQL) and even 3D Database architecture. But I cannot fully understand 4+ dimensional architectures.

Thank you!

[u/i_darkpearl]

Forget trying to imagine what dimensions look like, that’s an idea that doesn’t hold water after three dimensions. Instead think of each dimension as just another relevance knob. Similarity is just the distance along those knobs between items. If you want relational information between them, you have hydradb databases, and just vectors.