One of my biggest annoyances with the modern AI tooling hype is that you need to use a vector store for just working with embeddings. You don't.
The reason vector stores are important for production use-cases are mostly latency-related for larger sets of data (100k+ records), but if you're working on a toy project just learning how to use embeddings, you can compute cosine distance with a couple lines of numpy by doing a dot product of a normalized query vectors with a matrix of normalized records.
Best of all, it gives you a reason to use Python's @ operator, which with numpy matrices does a dot product.
The reason vector stores are important for production use-cases are mostly latency-related for larger sets of data (100k+ records), but if you're working on a toy project just learning how to use embeddings, you can compute cosine distance with a couple lines of numpy by doing a dot product of a normalized query vectors with a matrix of normalized records.
Best of all, it gives you a reason to use Python's @ operator, which with numpy matrices does a dot product.